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Speaking Agentic: The Concepts Advertisers and Publishers Should Know (and Why You Should Care)

travis@adzymic.co (Travis Teo) — Tue, 18 Aug 2026 03:00:09 -0700

Every industry shift comes with new vocabulary, and agentic advertising is no exception. Most of us are advertisers or publishers first, and engineers a distant last. But as AI moves from simply assisting with our work to making decisions and taking action on our behalf, some of the mechanics behind it start to matter.

You don’t need to become an engineer. But there are a few concepts worth understanding – because they shape how you choose an AI agent (or service), what the agent can do with your briefs, and how much control you retain over the outcome.

The Agentic Advertising Landscape
- Agentic Advertising
- Interoperability
How Agents Talk to Each Other
- Open Protocols
- Protocol Operations
The Relationship Between the Human, the Operator and the AI Agents
- Principal
- Operator
- Agent
How Agents Understand Your Brief
- Machine Readability
- Prompt Engineering
Now See These Concepts In Action

The Agentic Advertising Landscape

Before we get into how agents work, it helps to understand the landscape they operate in – and the shared infrastructure that makes agentic advertising possible.

Agentic Advertising

A caveat: “agentic advertising” is doing a lot of work as a phrase. At its core, agentic advertising is the use of AI agents across the advertising workflow – from planning and buying to creative production and reporting. Unlike rules-based automation, which follows a predefined set of rules, an agent can take a high-level objective, work out the steps required to achieve it, take action, and adjust as it goes – within limits set by a human.

Why You Should Care

Digital advertising has traditionally forced a trade-off between context and scale. Direct IO buys offer context – a publisher’s sales team can understand your brand, recommend suitable inventory, negotiate terms and shape a deal around the brief, but are slow to execute. Programmatic brings speed and scale, but waters down context around a brief with predefined targeting, auction and deal mechanics. Agentic advertising promises to narrow that trade-off: agents can operate at machine speed while taking on more of the contextual work traditionally performed by people – understanding objectives, evaluating fit, discovering inventory and negotiating terms.

Also read: Agentic Advertising Explained: A No-Jargon Guide for Brands and Publishers

Interoperability

Interoperability isn't a term most marketers use every day, but the idea behind it is familiar. It simply means that different systems can work together – exchanging information and taking action even when they were built by different companies. Think about the advertising stack you already use. Your creative, ad server, buying platform, supply partners and measurement tools aren't necessarily built by the same company, yet you expect them to work together. Agentic advertising extends the same principle to agents: agents built and operated by different companies need to be able to work together across the advertising workflow.

Why You Should Care

As more of the advertising workflow becomes agentic, the value of an agent will depend partly on how much of the ecosystem it can work with. Interoperability means you are not confined to a single vendor for every part of the workflow: you can connect compatible agents, platforms and tools as your needs change.

How Agents Talk to Each Other

If agents are going to work together, they need more than intelligence – they need ways to connect to tools, communicate with other agents, and agree on how advertising-specific tasks should be carried out. That connection operates on two levels: first, the shared language that dictates how agents work together, and second, what agents can do together.

Open Protocols

Interoperability doesn't happen automatically. For agents built by different companies to work together, they need an agreed way to communicate. That's where open protocols come in.

Think of an open protocol as a shared language. It defines how agents communicate and exchange information – from how inventory is described and capabilities are declared, to how requests are made and transactions are confirmed. If two agents speak the same protocol, they have a common framework for working together, regardless of who built or operates the agents.

In agentic advertising, two examples are the Ad Context Protocol (AdCP) and the IAB Tech Lab's Agentic Advertising Management Protocols (AAMP). Both are built on the same foundational standards emerging across the broader AI ecosystem — specifically Model Context Protocol (MCP), which provides a standard way for AI applications to connect with external tools and data, and Agent2Agent (A2A), which enables independently built agents to communicate and collaborate.

Why You Should Care

Open protocols are what make interoperability possible without every platform having to build a custom connection to every other platform. For advertisers and publishers, that means an agent can potentially work across a broader ecosystem rather than being confined to the products and partners of a single vendor. It's the same principle behind Adzymic's AgenX stack: we build around open standards so our agents can operate as part of a broader ecosystem, rather than a closed one.

Protocol Operations

Protocols tell you whether agents can work together; operations tell you what they can actually do together. Protocol operations govern how AI agents exchange messages, manage connections, and execute instructions across a network of other agents. You can also call these tasks.

In the above example, these are protocol operations, or tasks, in the Media Buy domain that agents built on the Ad Context Protocol can perform. (source)

Why You Should Care

Two agents speaking the same protocol doesn't mean they can automatically do everything together. What they can actually accomplish depends on the tasks each side supports. Buyer, seller and specialist agents – such as creative and signals agents – all carry different responsibilities.

For advertisers and publishers, this is where interoperability becomes practical: it's not just whether your agent can connect to another system, but what it can actually get done once it does.

The Relationship Between the Human, the Operator and the AI Agents

AI agents are commonly described as autonomous. That framing has an unintended side effect: it makes it sound as if the human is separated from the agent, with nothing in between.

All AI agents need a home. In theory, anyone could host their own — configuring it, running it, and answering for what it does. In practice, most advertisers or publishers don’t, simply because they’re not in the business of building tech infrastructure. The exceptions are big holding companies or large brands with the resources to build and host their agents that sit inside their own stack. Everyone else relies on managed services or platforms like Adzymic.

Once there's someone else running the agent on your behalf, the real question becomes: who owns what, and at which point does the agent decide?

That relationship breaks down into three layers:

The principal → Operator → Agent

Every action taken by an agent traces back to the principal – usually the advertiser or the publisher. The operator is the platform or service provider running the agent on the principal’s behalf. The agent is the software that actually places the media buys or builds the creative. In short: the agent is the what, the operator is the who runs it, and the principal is the who's responsible.

Principal

Usually the advertiser or the publisher – advertiser on the buy side, publisher on the sell side. The principal owns decisions around spend, targeting decisions, strategic goals and constraints. It authorises an operator like Adzymic to act on its behalf, and can revoke that authorisation at any time.

Operator

The platform or agency running the agent on the principal’s behalf. The core idea: an agent needs a home. An agent is software – it has to run somewhere, be configured by someone, and be accountable when something breaks. For large holding companies or brands, principal and operator can be the same entity. Most advertisers, though, aren’t in the business of building AI infrastructure – so they rely on operators like Adzymic to set the thresholds and constraints AI agents run within.

Agent

Simply put, an AI agent takes a goal, decides its own next steps, calls external tools/APIs, evaluates intermediate results, and iterates until done. It can act behalf of the advertiser or the publisher (i.e. the principal) and the operator. What separates it from an AI assistant like ChatGPT or Claude is that it is wired into tools and memory to execute tasks, not just respond.

Principal → Operator → Agents: Why You Should Care

Knowing something is “agentic” tells you surprisingly little about who actually has control. Understanding the operating model (the principal → operator → agents) tells you who sets the objective, who defines the rules the agent works within, what authority has been delegated to it, and who is accountable for what it does. For advertisers and publishers, that distinction becomes important the moment an agent starts spending budget, negotiating inventory or producing creative on your behalf.

How Agents Understand Your Brief

Once you've decided what authority to give an agent, the next question is what it needs to exercise that authority well. Agents can work from natural language, but acting on a brief requires more than simply understanding the words: they need to identify the right information, understand what you're asking them to achieve, and translate both into action.

Machine Readability

Machine readability for AI is the process of turning natural language into a structured input that language models and agents can parse, extract and interpret.

We tend to talk about “natural language briefs” or “machine readable formats” a lot. That’s because AI doesn’t read the way you and I do. Picture the difference between a text document and a spreadsheet: our brains work like the document – unstructured, but we can still pick out what matters – for instance, to fill out cells in a spreadsheet when needed. AI thinks like the spreadsheet from the start.

In the example above, we ran a prompt through Claude. On the right, notice how it automatically pulled snippets of information such as “brand”, “domain”, and “brief” and translated them into structured fields.

Why You Should Care

Because an agent isn't simply reading your brief for general understanding. It is looking for the information it needs to carry out specific operations. A creative agent, for instance, may need to identify the creative direction, required ad formats and brand context before it can build the creative. The clearer those inputs are, the more reliably the agent can execute the operation.

Think of it this way: every operation has things that need to go into it. Miss an element and the agent may still produce something, but it has to fill in the gaps itself – and the output can suffer as a result. Brand context is the clearest example. Most people think “brand identity” means colours and fonts. In reality, brand context (or identity) can also mean: what language does a brand speak? What imagery strengthens the brand? The more precisely you can answer that, the better the output.

On our managed services, our team does that translation for you. On the AgenX platform for Agent-as-a-service subscribers, it's what you're doing when you configure your brand kit or campaign parameters: the more clearly you define it, the more accurately the agent executes it.

Prompt Engineering

If machine readability is about making information understandable to an agent, prompt engineering is about giving the agent the right instructions to act on it. It is the art and science of structuring text inputs – clear instructions, context, roles, constraints – to get an AI model or tool to produce accurate, relevant output.

Why You Should Care

Prompt engineering isn't completely different from writing a good creative brief. In both cases, the quality of the outcome depends on how clearly you communicate the objective, context and constraints. The same campaign brief can produce very different outputs depending on how it is structured. Anything you leave vague becomes something the agent has to infer. With agentic advertising, those assumptions may carry through the remainder of the workflow.

If you're a client on our managed services, you don't need to become good at prompt engineering yourself – that’s part of our value-add to our clients. What's worth knowing is that our team follows best practices in prompt engineering. When we come back asking for more detail on your audience, tone, brand or campaign objectives, that’s us applying those best practices on your behalf – giving the agent the context and direction it needs to produce the right outcome.
If you’re using AgenX directly through Agent-as-a-service, prompt engineering matters more because you’re the one briefing the agent. The quality of your input directly shapes what comes back. Put simply: rubbish in, rubbish out.

Now See These Concepts In Action

You don't need to know how to build an agent to get started on agentic advertising. But you should know enough to ask the right questions: Can it work with the rest of the ecosystem? What can it actually do? Who is operating it? What authority are you giving it? And what does it need from you to do the job well?

Now that you know what to look for in an agent, put AgenX, Adzymic’s suite of Buyer, Sales and Creative Agents to the test. Book an AgenX demo and see how agents go from brief to execution, across the advertising workflow.

Adzymic Launches Sales Agent, Making Premium Inventory in Singapore Available to AI Buyers

travis@adzymic.co (Travis Teo) — Sun, 18 Oct 2026 20:46:21 -0700

Singapore, October 20, 2026 – Adzymic today launched AgenX Sales Agent, enabling media owners to make their premium advertising inventory available to AI buyers across display, video, digital out-of-home and high-impact digital ad formats. Media owners who have made their inventory available through Adzymic’s agentic infrastructure include Mediacorp, SPH Media, Stellar Ace, and The Smart Local Media Group.

Singapore’s Premium Inventory, Now Accessible to AI Buyers

This marks the second release in Adzymic’s AgenX suite, following the earlier launch of its Creative Agent. At the heart of the Sales Agent’s offering is the Adzymic Premium Exchange (APX) for omnichannel advertising—a curated network of high-impact and rich-media formats across premium publishers.

Built on long-standing partnerships with media owners, the exchange gives advertisers access to media that reaches up to 90% of online audiences in Singapore, as well as 85% of the island’s population through digital out-of-home networks. With APX now accessible through AgenX Sales Agent, these placements can be discovered, evaluated, and where supported, executed by compatible AI buyer agents.

AgenX Sales Agent is built on the Prebid Sales Agent foundation and extends it with Adzymic’s publisher integrations, premium media network and commercial workflows. Operating on the Ad Context Protocol (AdCP) and supporting both the Model Context Protocol (MCP) and agent-to-agent (A2A) protocol, AgenX Sales Agent opens up media owners' inventory to authorised and compatible AI buyer agents.

What This Means for Brands and Agencies

Traditionally, brands and agencies have accessed inventory either programmatically through supply-side platforms or through direct insertion orders. For direct insertion orders, the proposal and negotiation cycle between an advertiser and publisher can sometimes take days.

Agentic advertising, where AI agents perform advertising tasks on behalf of brands, agencies and media owners, can compress parts of this process from days to minutes.

Raj Parekh, Head of Growth and Partnerships at Mediacorp, said: “At Mediacorp, we are focused on helping partners and clients connect with audiences through trusted, effective advertising solutions across our premium media environments. As AI begins to play a greater role in how media is planned and bought, working with Adzymic’s AgenX Sales Agent allows us to explore how AI-enabled buying tools can make our advertising inventory more discoverable and accessible to buyers, while preserving the quality, trust and brand-safe environments that advertisers expect from Mediacorp.”

Sharing that focus on quality, Alicia Luke, Head of Channel Partnership at SPH Media, said: “We are constantly working with industry and adtech partners to ensure high-quality publisher media stays visible and valuable in an AI-driven environment. Ensuring our inventory is properly represented and accessed by AI buyers is a key step towards a modern, responsive, publisher-led marketplace.”

Adding an out-of-home perspective, Michele Low Richards, Assistant General Manager, Sales at Stellar Ace, said: “Out-of-home (OOH) advertising is one of the most dynamic media channels, where location, audience movement and timing influence buying decisions. That makes it especially suited for agentic media planning. At Stellar Ace, we are among the first to transform our media network by packaging our inventory in a way that buyers can have real time, AI-powered info enabling them to find, evaluate and match with relevant OOH placements.”

Bringing these perspectives together, Travis Teo, Co-Founder and Executive Director of Adzymic, said: “AI is transforming how advertisers execute campaigns and how publishers bring inventory to market. By launching AgenX Sales Agent, we are connecting buyer and sales agents through open protocols, giving advertisers easier access to premium, context-rich media while helping publishers retain control and respond to revenue opportunities faster.”

Media owners looking to make their inventory available to AI buyer agents, and brands or agencies interested in exploring agentic media buying, can get started at www.adzymic.co/agenX or reach out to Adzymic.

About Adzymic

Adzymic is an award-winning omnichannel advertising platform built for the agentic era. Combining a Creative Management Platform, Dynamic Creative Optimisation engine and curated Premium Exchange, Adzymic gives brands and agencies an end-to-end suite to create, personalise and activate high-impact campaigns across display, video, connected TV and digital out-of-home. Trusted by leading brands and agencies worldwide, Adzymic powers campaigns for some of the world’s most recognised advertisers, delivering measurable engagement and performance at scale.

As advertising shifts towards agentic execution, Adzymic is building the infrastructure for AI to plan, create and optimise campaigns across every touchpoint. Discover more at www.adzymic.co/agenX.

Press Contacts

Peggy Koh
Adzymic Pte Ltd
peggy@adzymic.co

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Adzymic Pte Ltd
sharon@adzymic.co

What Is a Brand Kit? Keeping an AI Creative Agent On-Brand

travis@adzymic.co (Travis Teo) — Wed, 07 Oct 2026 20:50:10 -0700

AI agents have proven their worth in generation of creative assets. AgenX Creative Agent takes a brief, a key visual, and a Brand Kit, and goes from prompt to fully interactive, working ad creatives in minutes, across display, connected TV, digital out-of-home, and high-impact ad formats. But one concern many advertisers have is brand drift: ad creatives for a campaign, no matter how fast they were produced, cannot go live if they do not look or sound like the brand.

Brand Drift: Why AI Agents Need "Guardrails"

"Guardrails": the AI world's favourite word. Cliché aside, see a Brand Kit as guardrails — rules and boundaries that the Creative Agent should work within.

AI agents inevitably present a secondary source of drift: anything you leave vague becomes something the agent has to infer, and that may result in what we deem "hallucination". A well-documented Brand Kit significantly reduces the possibility of off-brand output.

Brand inconsistencies often come down to small errors: the wrong colour, off-brand imagery or tone, or missing legal requirements. A wrong colour could be easy to catch. But as creative variants multiply, manual checks against brand guidelines become difficult to scale. Multi-market and multi-format campaigns add further complexities: incorrect colours, unapproved typography, misplaced logos, off-brand copy.

The Creative Agent never gets tired — even across thousands of creative assets, the same rules are checked every time.

What Is a Brand Kit? The Rulebook Behind an AI Creative Agent

Think of the Brand Kit as a single source of truth for how your brand looks, how it sounds, and what it can and can't say: colours, fonts, logo rules, imagery direction, tone of voice, legal boundaries, and more. It is set up once and reused across campaigns. You can upload an existing brand document or build it with AI on the AgenX platform.

How the Creative Agent Uses a Brand Kit at Generation

The Creative Agent uses the Brand Kit as a central reference. It applies rule-based brand requirements across every output, and people focus on the contextual judgement.

We call this the 90/10 rule. Let the agent handle the repeatable 90%: layout, colour application, typography, logo placement, resizing, and spec compliance. Reserve the remaining 10% for contextual judgement.

What Makes a Good Brand Kit for HTML5 Ad Generation

The Brand Kit gives the Creative Agent clear rules to work from. The better the inputs, the more reliably it can handle the repeatable work — leaving people to focus on judgement.

There are two principles to follow for a good Brand Kit:

Get the inputs right: Give the agent enough context to understand not just what your brand uses in imagery, but how your brand should sound and feel
Set up the right governance: It can be tempting to treat every AI-generated creative as something that needs another pair of human eyes. The right way to approach this is to agree upfront on what "good" looks like.

First Half of a Good Brand Kit: Get the Inputs Right

Give the Creative Agent enough context to understand not just what your brand uses, but how your brand should look and feel.

Capture what makes the brand distinctive. Go beyond logos, colours, and fonts to include the visual cues, motifs, and stylistic choices that make the brand recognisable.
Define how the brand should come together. Include rules for hierarchy, proportion, emphasis, and how elements relate — not just the individual ingredients.
Give the Creative Agent a reliable source of truth. The assets, rules and references in the Brand Kit should be complete, accessible, and approved for use. Match the level of detail to the complexity of the brand: a simple brand may need only a few clear rules, while a multi-market or multi-brand organisation may need much more structure.

Second Half of a Good Brand Kit: Set Up the Right Governance

It can be tempting to treat every AI-generated creative as something that needs another pair of human eyes. Instead, agree upfront on what "good" looks like.

Use the 90/10 rule. Let the Creative Agent handle the repeatable 90%: layout, colour application, typography, logo placement, resizing, and spec compliance. Reserve the remaining 10% for contextual judgement.
Fix repeated issues at the source. If the same inconsistency keeps appearing, update the Brand Kit rather than correcting each asset individually. It could be a sign the Brand Kit itself is under-specified.
Agree on what "good" looks like, then calibrate. Use initial outputs to refine the Brand Kit and align on what counts as on-brand.
Revisit when something material changes. A rebrand, new market, new campaign direction, new sub-brand, or updated usage rights. This helps the Creative Agent work from the right rules again.

Next Steps: Building, Iterating and Scaling Ad Creative with a Creative Agent

The Brand Kit is the prerequisite for everything in The Advertiser's AI Playbook: Building, Iterating and Scaling Ad Creatives with Creative Agents.

The playbook shows how advertisers can move from brief to campaign-ready creative with more flexibility, control, and scale.

Inside, you will find a case study featuring Cathay Pacific, where the brand launched its 80th anniversary campaign in 18 working days, plus a 10-day plan to help you get started with the Creative Agent.

Download The Advertiser's AI Playbook here, for free.

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From Intent to Action: How Standard Chartered Hong Kong Drove 8X Conversions with Real-Time DCO

travis@adzymic.co (Travis Teo) — Tue, 01 Sep 2026 19:45:48 -0700

🏆 Winner — Best Use of AI & Technology · Best Use of Performance Marketing · IAB Hong Kong Digital Awards 2025

The Challenge

Travel intent is perishable. Standard Chartered Hong Kong needed to reach customers at the exact moment their intent was highest — not after it had passed. A static campaign wouldn’t cut it. The brief called for a single master creative concept that could personalise itself dynamically across every relevant context, at scale.

Context as Creative

Adzymic built the creative intelligence layer behind Fly Moments, analysing six real-time signal types to determine the next best offer for every impression:

🌤️ Weather — sunny conditions, rainfall, seasonal shifts in Hong Kong
⏰ Time of day — morning commutes, lunch windows, evening browsing sessions
🎉 Festive moments — public holidays and seasonal travel peaks
🛍️ Retail moments — proximity to purchase behaviour signals
✈️ Travel searches — live search intent from active trip planners
🧠 Mindset — user context and behavioural cues at the moment of exposure

The creative management layer Adzymic powered enabled modular creative systems, rapid variant generation, and seamless deployment across channels. From one master idea, over 900 creative variants were executed dynamically — each one calibrated to the precise combination of signals that triggered it.

The results validated the approach. Standard Chartered Hong Kong recorded an 8x improvement in conversion rate and a 56% uplift in click-through rate. The efficiency gains were equally substantial: Adzymic’s creative management layer cut lead time from brief to campaign live by 93% and reduced production costs by 75% — from a single master idea to over 900 live variants.

When creative intelligence meets real-time context, personalisation stops being a production challenge — and starts being a competitive advantage. Fly Moments is proof that the right offer, at the right moment, for the right mindset, at scale, is no longer a hypothetical.

Through a Founder's Lens: What Actually Makes an AI Creative Platform?

travis@adzymic.co (Travis Teo) — Tue, 01 Sep 2026 03:30:31 -0700

First published on Travis's LinkedIn, where he shares his take on agentic advertising, ad tech, and the business of building in this space. A marketer turned entrepreneur, Travis is co-founder and Executive Director of Adzymic, with over two decades across adtech and digital media in Asia Pacific — including a stint leading SAP's regional digital media practice. Follow him on LinkedIn for more.

There are a lot of “AI creative platforms” now.

Some generate images. Some generate copy. Some can turn a product feed into hundreds of variations. Increasingly, you can type a prompt and get something that looks remarkably close to a finished ad.

The technology has moved very fast. But after spending time building around this space, I think we may be using the term AI creative platform too loosely.

Generating an asset is only one part of making advertising.

Anyone who has worked with a large advertiser knows what happens after the first creative comes back. The logo is slightly wrong. The font isn't approved. The product needs to move 20 pixels to the left. Legal wants another line added. The agency wants a 300x250, 300x600 and 970x250. Then somebody asks for a mobile version, a video version and maybe a DOOH execution.

And by the way, the promotion changes next Tuesday.

This is the less exciting side of advertising that most AI demos don't show.

The model isn't really the product

Image generation is getting very good. So is copy. Video will get there.

My guess is that, fairly soon, access to a good generative model will be the easiest part of building an AI creative product. Everyone will have one, either directly or through an API.

The harder part is everything around the model.

Take something as basic as brand guidelines. A brand might have very specific rules around how its logo is placed, what colours can sit behind it, which fonts can be used, how much whitespace is required, what tone its copy should take and which product images are approved.

You can dump some of this into a prompt, but that doesn't really solve the problem.

If a brand manager still needs to correct the AI ten times before approving an output, you haven't removed that much work. You've just moved the work around.

That is why I think brand understanding will become an important part of these platforms. Not just uploading a logo and selecting three colours, but retaining enough knowledge about the brand that the first output is already reasonably close to something usable.

A good ad also needs an idea

There is another part that gets overlooked when we talk about generation.

Someone still has to decide what to make.

Suppose the brief says: An airline brand is running a Double 11 promotion across multiple destinations. What should the ad actually do?

It could be a simple promotional banner. It could open with the sale message before moving into a destination gallery. Prices could be dynamically populated. The destinations could change based on where the person is travelling from. Maybe there is a countdown as the promotion gets closer to ending.

A designer or creative team makes dozens of small decisions like these without necessarily describing them as “reasoning”.

For AI creative to become genuinely useful, I think it has to start participating in this part of the process too. Give it the brief, brand and campaign objective, and it should be able to suggest how the creative could work before generating all the assets.

We have been experimenting with this ourselves, and it is much harder than generating a nice-looking visual. There are many possible answers, and the most visually impressive answer isn't always the best advertising idea.

Then you hit production reality

This is where I think many AI creative demos stop too early.

They show you a beautiful 1:1 visual. Advertising rarely ends there.

For one campaign, you may have standard display banners, high-impact units, video, social, CTV and DOOH. Even within display, a desktop skin behaves very differently from a 300x250 banner. You can't just resize everything and expect it to work.

Rich media makes this even more obvious. A carousel has behaviour. A gallery has states. A countdown needs a data source. A live-score creative needs rules for what happens before, during and after the match.

So templates aren't going away just because generative AI has arrived.

In fact, I suspect they become more useful.

AI can generate the idea, content and variations, while a design system gives those outputs some structure. You get flexibility without having to trust a model to reinvent the technical implementation of an ad every single time.

That balance between generation and structure is something the industry is still working out.

The part I'm most interested in is what happens after generation

Creative automation becomes much more powerful once it connects to data.

A retailer shouldn't have to manually produce a banner for every product. A travel advertiser shouldn't need separate creative production every time it wants to promote another destination. A sports sponsor shouldn't need somebody sitting around updating a score.

We have been doing this through DCO for years, using things like product feeds, location, weather, time and live sports data.

AI changes what can happen on the creative side.

Historically, you might build a template and define all the possible combinations before the campaign begins. The system then assembles those combinations according to a set of rules.

With generative AI, some of those creative decisions could eventually happen much later in the process. The system might decide what message, layout or imagery makes sense for a particular context and generate what it needs.

We're not fully there yet. There are plenty of questions around brand control, consistency, latency and whether marketers even want AI making that much of the decision.

But this is far more interesting to me than generating another hundred banner variations.

And someone still has to run the ad

There is a very practical test I use when looking at AI creative products.

What happens after I press generate?

If the answer is that somebody downloads a JPEG, sends it to the agency, waits for approval, asks a designer to make the remaining sizes, uploads everything into an ad server and checks that the tracking works, then we have improved one step of a much longer process.

That's still valuable. But there is a lot more left to solve.

The direction I find more interesting is where the brief, brand knowledge, creative generation, formats, dynamic data, QA and activation start coming together.

Eventually performance should feed back into the process as well. Not simply telling you that Creative A had a higher CTR than Creative B, but helping the system make a better creative decision the next time.

The image model gets most of the attention because that's the part you can demo in 30 seconds. But the companies that eventually build meaningful AI creative platforms may win by solving all the boring things around it: understanding brands, producing real ad formats, handling revisions, connecting data, checking outputs and getting the creative live.

Generating the picture may turn out to have been the easy part.

If that resonates, take a look at the AgenX Creative Agent — Adzymic's creative agent built for exactly this gap. One brief in, deployable rich media out: every size, every language, every channel, brand-consistent by design and ready to serve programmatically or agentically. [Explore the Creative Agent →]

AdCP Creative Agents: What You Need To Know

travis@adzymic.co (Travis Teo) — Tue, 18 Aug 2026 19:10:53 -0700

Multiple frameworks around agentic advertising have emerged, defining the roles of agents in digital advertising. The industry has landed on three types of agents: buyer/buying agents for the buy side, sales/selling/seller agents for the sell side and specialist agents.

In this guide, we will dive into creative agents, the specialists responsible for generating and adapting ad assets across formats and specs.

The Creative Production Process is Broken
The Definition of a Creative Agent isn’t Always Clear
AdCP Creative Agents: Serving Both Demand and Supply Sides
Access to Creative Agents: Two Commercial Models
What An AdCP Creative Agent is Capable of
How to Get Started with Creative Agents

The Creative Production Process is Broken

Traditional creative production is bogged down by lengthy approval cycles – from brief to design to stakeholder approval to delays between each revision. Each bottleneck is multiplied further when you throw in the technical complexity of interactive formats like HTML5, and the growing demand for dynamic, audience-specific personalization where Dynamic Creative Optimisation (DCO) is often looked to as a solution. A creative agent is a structural answer to this constraint.

We are narrowing the definition of a “creative agent” to its function as a protocol role, as outlined in the Ad Context Protocol (AdCP). You will see the AdCP referenced frequently here, as it represents the industry’s most concerted effort to standardize how creative production and delivery are orchestrated agentically. As a founding member, Adzymic plays a role in shaping these protocols globally.

The Definition of a Creative Agent isn’t Always Clear

Just as there is no industry consensus around what agentic advertising actually means, your understanding of a “creative agent” – depending on your role, or what your LinkedIn algorithm feeds you – may very well differ from your colleague sitting across from you. In 2026 alone, two major companies in distinct domains – Adobe, the digital media software leader, and Luma Labs, a San Francisco-based generative AI company – have each launched their own creative agents.

You would notice that both operate as platform-bound tools. Some AI simply acts as a copilot inside a platform. Knowing the difference matters because "agentic" has become a marketing label as much as a technical one – a platform can market itself as agentic while the same legacy infrastructure runs underneath.

This article narrows the definition of a “creative agent” to its function as a protocol role, under the Ad Context Protocol (AdCP). To understand the “creative agent” in the context of agentic advertising, view it through the following lenses:

Think ad servers, not generative image tools: The output isn’t limited to static images or videos. Instead, we’re looking at ad tags as the final output – ready for campaign trafficking via ad servers such as DV360, The Trade Desk, and others.
Think advertising templates, not one-time image or video generation: We’re not just going from prompt to single dimensional output. In the world of interactive display, this means generating interactive HTML5 display templates, VAST/VPAID video standards, and rich media like carousels, scratch-to-reveal mechanisms, in-banner video, and more – all ready for deployment.
Think campaign lifecycle, not just creative production: A creative agent doesn’t stop at producing ad creative; it simultaneously acts as a bridge within the agentic ecosystem, communicating with other agents (e.g. buyer and seller agents) to coordinate, and execute a campaign end-to-end.

Why AdCP, specifically? AdCP brings together industry players across SSPs, DSPs, publishers, agencies and ad tech platforms to govern and standardize how agentic advertising is done, just like how OpenRTB was formalised by IAB Tech Lab. As a founding member, Adzymic plays a role in shaping these protocols globally.

Also read: Agentic Advertising Explained: A No-Jargon Guide for Brands and Publishers

AdCP Creative Agents: Serving Both Demand and Supply Sides

With a seller or buyer agent, it’s clear which side of the ecosystem each agent serves. Creative agents, on the other hand, serve both the buy and sell side – albeit with distinct functions.

Imagine a print shop. A customer walks in wanting a roller banner for their next trade show. The customer is the human strategist, or the principal in an AI interaction.

Also read: Agentic advertising concepts: The relationship between the principal, agent and operator explained

Just hours before that, a designer from the customer’s company has taken the brief: “we need something bold and on-brand, I’m guessing it will be 3-metre tall”. This is the creative agent on the buy side. The creative agent creates.

On the print shop’s production desk, the specifications go beyond “3-metre tall”. The print shop’s production team specifies: 1,500mm by 2,000mm. Minimum 300dpi. CMYK colour profile. Bleed on all sides.

They check the artwork against those requirements, flag what doesn’t fit, and produce a proof before anything goes to print. The production team is the creative agent on the sell side: it doesn’t produce creative, but validates what to produce.

In our analogy:

the customer represents the human strategist or the principal,
the designer represents the creative agent on the buy side – generating creative for the principal, and;
the production desk at the print shop represents the creative agent on the sell side – validating the right creative formats and specifications.

Access to Creative Agents: Two Commercial Models

In short: an agent needs a home. An agent has to run somewhere, be configured by someone, and be accountable to someone when things go wrong. A big holding group or large brand could build and host their own creative agent – one that sits inside their own tech stack and manages their creative library centrally. But most advertisers or publishers don't build their own, simply because they’re not in the business of building tech infrastructure. Instead, they use managed services or platforms like Adzymic.

Our agentic suite, AgenX, acts as the operator, while the advertiser or publisher stays as the principal. Through our managed services or Agent-as-a-service subscription, AgenX is akin to a polyglot that speaks the same language as every advertiser and publisher in the agentic ecosystem. Today, two modes of delivery are available:

Managed services: This is for brands or advertisers who want an integrated solution, more so the access to the omnichannel premium inventory that Adzymic provides. Iterations and prototyping with the creative agent is one benefit that you will receive as part of our services.
Agent-as-a-service: This means that you pay a monthly fee to use the AgenX Creative Agent, with you as the principal, and Adzymic as the operator, configuring the guardrails around the agents (this isn’t limited to just creative agent – there’s also buyer or seller agents). This is for clients with mature, in-house programmatic capabilities.

What An AdCP Creative Agent is Capable of

At its core, a creative agent is capable of performing the following (the corresponding specification is shown, for our technically adept audience):

Generate creatives automatically from natural language briefs (build_creative)
Show ad previews to advertisers before they go live
Validate a publisher's supported formats and selecting suitable formats for advertisers (get_adcp_capabilities → creative.supported_formats)
Push assets (images, video, product feeds) to ad servers for rendering (sync_creatives)
Pull delivery reports programmatically (get_creative_delivery)

On the sell side, the creative agent can perform these on behalf of a media owner or publisher:

Declare accepted formats so buyers know exactly what to send (get_products, get_adcp_capabilities)
Validate incoming creatives against your specs automatically (validate_content_delivery against a declared capability_id)
Receive and store buyer-pushed assets (sync_creative, requires declaring has_creative_library: true)
List creative inventory for buyers to discover and select (list_creatives, requires declaring has_creative_library: true)

The end result: the entire creative workflow – from brief to delivery report – can run agent-to-agent, without human coordination at each handoff.

For advertisers, this means faster campaign launches and less manual work – no more back-and-forth with the publisher's trafficking team.
For publishers, this translates to less time wasted on creative operations as incoming creatives are validated automatically against your specs, and an easier path for buyers to work with you – both of which mean faster monetisation.

Notice how we listed the protocol operations – build_creative, sync_creatives, etc. In layman terms, these are the common tasks that any compatible AI agent is designed to carry out. Industry players have come together through working groups and industry councils to decide on common tasks that agents should undertake. For the non-technical audience, the takeaway is this: having a common language allows agents from anywhere in the world to communicate with one another.

If you would like to dive into the world of agentic advertising, read this article where we covered key concepts around agentic advertising including protocol operations and interoperability.

How to Get Started with Creative Agents

Creative agents promise to move the industry away from manual, fragmented coordination, and towards fully automated, agentic workflows.

If you are a media planner, digital director, or agency lead: The right starting point is understanding what a creative agent can do for your creative workflow. The Creative Agent and Buyer Agent within AgenX are designed for advertisers like yourself.

If you want to go deeper on AdCP creative agent: The protocol documentation is freely available at agenticadvertising.org. It is written for builders, but the overview sections are accessible to anyone who wants to understand what the standard actually specifies.

Through a Founder's Lens: What I Learned From Talking About Agentic Advertising

travis@adzymic.co (Travis Teo) — Tue, 18 Aug 2026 01:48:48 -0700

First published on Travis's LinkedIn, where he shares his take on agentic advertising, ad tech, and the business of building in this space. A marketer turned entrepreneur, Travis is co-founder and Executive Director of Adzymic, with over two decades across adtech and digital media in Asia Pacific — including a stint leading SAP's regional digital media practice. Follow him on LinkedIn for more.

Over the past few months, I have been talking quite a bit about agentic advertising with publishers, agencies and brands.

I started with a fairly clear idea of where this is going: buyer agents talking to seller agents, with much of media planning, buying and optimisation eventually happening autonomously.

After presenting this many times, I noticed I was spending a lot of time explaining how it works. What is a buyer agent? What is a seller agent? How do they find each other? Where does the DSP sit? Who gives an agent permission to spend?

Then we get into protocols, MCP, A2A and how all of this connects with the programmatic stack we already have.

Seems the same complexity we had with Programmatic is now reappearing in Agentic.

Though publishers would ask a much simpler question.

Where are the buyers?

Some of the most useful conversations I've had have been with publishers.

The seller agent concept is relatively easy to understand. Make your inventory, audiences and advertising products discoverable to AI buyers, and potentially open up a new source of demand.

Then comes the obvious question: where are the buyer agents?

It's a fair question. Publishers don't need another piece of technology. They want buyers and revenue. We can build seller agents and protocols, but someone still needs to show up with demand.

With agencies, the conversation gets practical quickly. They already have DSPs, trading teams, planning tools and processes that work. An agent has to make that job easier or produce a better result. Otherwise, why change?

I probably underestimated this part when I first started looking at agentic buying.

Why end-to-end still matters

An AI agent could take a brief, build a media plan and use MCP or APIs to execute through the platforms an agency already uses. It could generate creative, analyse campaign data and take on more of the execution.

End-to-end agentic buying can go much further. Give a buyer agent an objective, budget and guardrails. It can discover opportunities across multiple sellers, decide where to spend, execute and continuously move budget based on what is actually working.

Today, media buying is fragmented across platforms, channels and workflows. Each platform sees its own piece of the campaign and optimises within it. A buyer agent could sit across those boundaries and optimise against the advertiser's objective instead. And it should get better over time. With enough context and performance history, the agent can learn from one campaign and apply that learning to the next. Combine that with creative performance and insights such as BLS, and you start to build a much more powerful feedback loop between media, creative and brand outcomes.

It could also discover inventory the buyer may not have considered, compare direct publisher opportunities with programmatic ones and react faster to performance. For publishers, seller agents could also make more of their inventory and unique products discoverable.

That's what interests me about end-to-end agentic buying. It's not just automating what a trader does today. The agent can learn across media, creative and outcomes, and bring those learnings into the next campaign.

End-to-end agentic buying is still early, so the practical question is how we get there from the way media is bought today.

Maybe the first buyer agent doesn't replace the DSP

For many agencies, the starting point may be an agent that works with the tools they already use.

Give the agent a campaign brief and budget. It builds the plan, generates the creative and looks for media. If a publisher's seller agent has what it needs, buy there. Otherwise it can fall back to the DSP, or an API, whatever gets the job done.

The existing programmatic world and the emerging agentic world can work together. As more publishers expose seller agents and the technology matures, more buying can happen directly agent-to-agent.

We don't need everyone to change at the same time.

The technical work is moving fast

There is a lot of work happening around agentic protocols and infrastructure. We will need it if independent buyer and seller agents are going to discover, communicate and transact with each other at scale.

Getting a protocol to work is one thing. The harder part is making agentic buying useful enough that buyers actually want to use it.

Here is my objective. Here is my budget. Here are my guardrails. Help me get the best outcome.

Behind that instruction, the agent could compare direct and programmatic supply, generate creative, shift budgets and learn from performance. Some of that may happen through existing platforms, some directly with seller agents.

Protocols will matter as more independent agents start talking and transacting with each other. But they should make things simpler for the industry, not create another layer buyers need to understand.

Where we are heading and how we get there

The first buyer agents will probably work with much of the infrastructure, platforms agencies already use today, while connecting directly to seller agents where it makes sense. As more sellers become accessible to agents, more of the buying can happen directly between them.

My take is that full agentic buying will not happen overnight. It will happen gradually through pilot campaigns, testing and learning. Buyers need to see the value and become comfortable letting agents make more decisions, while more of the supply side becomes ready to transact with them.

Vaseline: From UV Index to Timely Skin Protection

travis@adzymic.co (Travis Teo) — Fri, 31 Jul 2026 00:42:08 -0700

The Challenge

Malaysia’s hand & body lotion category is evolving, with value growth increasingly driven by premium therapeutic brands while overall category volumes soften. As the established leader in the mass segment, Vaseline identified an opportunity to strengthen relevance and penetration by addressing emerging consumer needs, particularly around daily sun protection.

Vaseline aimed to drive awareness and trial for Gluta-HYA SPF50+++, its first-ever sun serum burst sunscreen lotion, by recruiting new users and making daily UV protection feel immediate and personally relevant.

Insight & Strategy

Malaysians Know the Risk. They Just Don't Act on It.

In Malaysia's tropical climate, the UV index regularly reaches "high" to "extreme" levels — often 9–11+ — especially between 10 a.m. and 4 p.m. Yet studies show many Malaysians still do not apply sunscreen consistently. While most Malaysians grasp the basics of sun protection, gaps remain in consistent, habit-based application — and in recognising that protection is needed year-round, not just on visibly sunny days. Sun exposure is constant, but the risk is often invisible. Vaseline Malaysia set out to make daily UV protection feel immediate and relevant.

Reach Them in the Moment of Exposure

To reach new users effectively, the campaign activated a premium network of 68 digital billboard screens spanning major highways and key commuter routes across the Klang Valley — including the Federal Highway, MRR2, Sprint, NPE, and LDP. These high-traffic locations deliver repeated exposure to the daily commuter audience most relevant to Vaseline's recruitment objective, building brand familiarity and making sun protection messaging visible at scale.

Execution

To achieve the campaign objective, Vaseline launched a data-driven, programmatic DOOH campaign, activating premium digital screens across Klang Valley to deliver real-time, contextually relevant messaging. Using live UV index data, creatives updated automatically throughout the day to reflect changing environmental conditions, reaching GET LSM 10+ females aged 18-44 with timely and relevant skincare messages. UV levels ranging from 1 to 10 were integrated directly into the creative, dynamically displaying:

The Results

Across premium DOOH locations in Klang Valley, the campaign delivered over 1.65 million impressions, transforming everyday commutes into moments of skincare education.

By turning live UV data into actionable, contextual DOOH messaging, Vaseline reinforced its role as a trusted, everyday protection brand through timely and relevant communication.

Agentic Advertising Explained: A No-Jargon Guide for Brands and Publishers

travis@adzymic.co (Travis Teo) — Tue, 28 Jul 2026 19:55:05 -0700

The Advertiser's AI Playbook: Building, Iterating and Scaling Ad Creatives with Creative Agents, out now. Get your free copy here.

Want insights on agentic advertising like this one? Subscribe for more.

This guide is written for marketing managers, media planners, digital directors, and publisher commercial leads who want a straight answer: what is agentic advertising, and what does it mean for the work I do every day?

What follows is a clear, honest explanation of how agetic advertising goes beyond programmatic and direct insertion orders, types of agents (and a short lowdown on how they emerged) and what it means for your role.

Table of Contents:

Programmatic and Direct IO Buys: How is Agentic Different?
- Both Programmatic and IO Buys Have Ceilings
- Agentic Advertising Goes Beyond Programmatic and IO Buys
Will Agentic Advertising Replace A Planner’s Thinking?
What Does an AI Agent Actually Do? And Is It The Same as LLM?
Agents in Advertising
- How Many Types of Advertising Agents Are There?
- Why Do We Need So Many Agents?
Buy, Sell, Create: How The Human Interacts with Agents
- Buyer Agents: The AI Agents for Briefing and Buying Media
- Seller Agents: Representing Publisher Inventory
- Creative Agents: A Tale of Two Roles
Getting Started with Agentic Advertising

Programmatic and Direct IO Buys: How is Agentic Different?

Across the board, we see the media ecosystem becoming AI-driven:

Agencies are adopting AI planners and automation tools
Buyers now expect real-time access to inventory
Manual workflows that have been plaguing the industry for decades are now becoming a competitive disadvantage, not just an inconvenience
Most of all, we see the industry moving towards structured, machine-readable trading protocols

Agentic advertising is what happens when AI systems can plan, decide, and act across the advertising workflow without a human approving every single step.

Both Programmatic and IO Buys Have Ceilings

For most of advertising history, every step was human. A media planner called publishers, negotiated rates, sent insertion orders by fax, and waited for someone to manually serve the ad.

Programmatic advertising automated the transaction layer: real-time bidding (RTB) and the OpenRTB standard – formalised by IAB Tech Lab in 2010 – let machines buy impressions in milliseconds against predefined parameters.

Not every placement was made available programmatically – premium formats like desktop skins, DOOH, and high-impact takeovers still had to go through direct insertion order (IO) buys. The process took longer for both sides, but the exclusive arrangement allowed brands to secure the exact inventory their brief called for.

That being said, both models have their ceilings:

Programmatic (SSP → auction → automated delivery) is fast and always-on for both sides. However, the very nature that makes programmatic efficient–CPM-based auctions–tends to undervalue premium inventory, which is why publishers still reserve their best placements for direct IO buys. This cuts both ways: programmatic may not offer the best-fit placement for advertisers: the placement that wins on value-for-money isn’t always the one that actually works for the advertiser.
Direct sales or IO buys (RFP → proposal → insertion order) wins clearly on fit: placements that actually match the brief while protecting publisher margins. What it doesn’t do well is speed, as negotiations on RFPs and proposals can take days.

Agentic Advertising Goes Beyond Programmatic and IO Buys

Agentic advertising is designed to take what's best from both models: the speed and always-on automation of programmatic, and the fit IO buys delivers against the brief – without needing a human to bridge the two manually.

For advertisers: Brands and agencies get continuous access to both premium and programmatic inventory, evaluated against the same brief and criteria. Take premium inventory as one example: it was previously kept out of reach due to resource constraints — the inventory existed, but evaluating it meant working through RFPs one publisher at a time–a task for which few planners have the necessary resources. With agentic advertising, premium and programmatic inventory can now be assessed together against the same brief, rather than one being priced by auction and the other picked by hand. Most importantly, a far larger supply can now be evaluated – in real time.

For publishers: The biggest gains come in two ways. First, a wider pool of buyers — even outside a publisher's existing relationships as inventory is surfaced around the clock, across time zones, resulting in higher fill rates. Second, higher yield and transparency around premium and even non-digital formats, as these can now be negotiated directly by an AI agent, rather than being priced the same way as standard programmatic inventory.

Across the board, agentic advertising cuts the operational load that both models carry in their own way — faster turnaround on proposals and bookings than direct sales, freeing both planners and publisher sales teams to focus on strategy over manual tasks.

Will Agentic Advertising Replace A Planner’s Thinking?

No, not unless you allow it (which we do not recommend). Agentic advertising is the first attempt to augment – which means “to increase the value of something by adding to it” – the decision-making and judgment process that a human strategist and media buyer would normally do in their head, and most importantly, it extends the strategic thinking directly into action:

"Is this the right audience for this campaign?"
"Does this creative actually fit the brand and the moment?"
"Should we shift the budget from this platform to that one?"
"Is this placement worth the price, or should we negotiate?"

In operational terms, this means the AI agent doesn’t just recommend a plan, i.e. the thinking that you already do—it uses connected tools to execute tasks, log into platforms, and manage the workflow autonomously. Notice the emphasis on action taking.

What Does an AI Agent Actually Do? And Is It The Same as LLM?

If you’re fairly new to the world of AI agents, you may struggle to grasp the difference between an LLM, such as ChatGPT, Gemini or Claude, and an AI agent. Many modern AI products blend the two within their systems, further blurring the roles that an LLM and an AI agent each play.

Think of LLM as the thinking engine, while an agent is the doer. An AI agent cannot exist without LLM–the intelligence layer an agent requires to act.

Simply put:

LLM: a model that responds to prompts by generating text or code.
Agent: a system that uses an LLM plus tools, memory, planning, and action loops to complete tasks.

Think of the LLM as the “thinking engine”, and the agent is the “doer” built around it. An LLM cannot perform actions – such as transacting a media buy – unless it is connected to tools.

Agents in Advertising

Not every platform that calls itself "agentic" means the same thing. Some AI simply acts as a copilot inside a platform a human still drives; others let agents orchestrate across tools, but the trade still runs through one platform's own system. Knowing the difference matters because "agentic" has become a marketing label as much as a technical one — a platform can market itself as agentic while still running the same execution it always has underneath.

This guide focuses on what we call the “end-to-end agentic” model: agents that transact directly with each other, on behalf of a human, across the full advertising workflow.

In a typical week in the “pre-agentic” era, a human strategist typically spends hours chasing proposals from publishers, and manually comparing options. Mirroring this on the publisher side, a sales representative is equally tied up responding to these inquiries.

In advertising, an advertising agent – also used interchangeably with “AI agents” outside of the advertising world – might receive a brief from a media planner — "reach women aged 25–40 interested in skincare across Singapore and Hong Kong, $50,000 budget, three-week flight'. The agent then autonomously does all or most of the following:

Query publisher and platform agents to discover relevant inventory and audience packages
Compare options against the brief, budget, and brand guidelines
Draft a media plan and flag any trade-offs for human review
Brief a creative agent to produce ad variants tailored to different placements
Submit the plan for approval, then execute once a human has signed off
Monitor performance in-flight and make optimisation decisions within pre-approved parameters
Produce a campaign report in natural language

With AI agents processing a large amount of data in real-time – from inventory to audience packages to ad formats, steps 1 through 4 – which would’ve taken a human planner hours in the past – are now reduced to minutes. The human still maintains oversight, but only for the decisions that actually require human judgment, not for every operational step in between.

The operational upside extends to both buy and sell sides. “Pre-agentic” tasks are now offloaded to autonomous agents – freeing media planners from the drudgery of chasing proposals and comparing options, and relieving publishers' sales reps from manually responding to every inquiry.

How Many Types of Advertising Agents Are There?

A project management software company claims to have 3,258 agents working alongside 1,300 humans! Closer to home, IAB Tech Lab maintains an agent registry where there are 37 registered, active agents, while AgenticAdvertising.org has 58 agents on its registry. Both numbers are accurate as of July 2026.

While "agentic advertising" is now a widely used industry term, its precise meaning is still contested. That also means that taxonomies around advertising agents are still stabilising, and this could change at any time.

Five common advertising agent roles, shared by Niki at the AAO Connect event in Snigapore in June 2026. (Credits: Niki Banerjee, Product Manager at Scope3)

At the time of writing, multiple frameworks around agentic advertising have emerged, defining the roles of agents. Across all frameworks, there is broad consensus on two core principal roles:

Buyer/buying agents (buy/demand side) that act on behalf of a brand or agency; translate objectives, budgets, and brand standards into advertising decisions, and transact with sell-side agents.
Sales/selling/seller agents (sell/supply side) that represent what media owners and publishers offer – inventory, pricing, terms; handle discovery, deal negotiation, proposal generation, and campaign activation.

Beyond the two principals, specialist agent types are converging, but industry bodies and vendors name and scope them differently:

Creative agents: Generate and adapt ad assets across formats and specs
Signals/audience agents: Discover and activate audience data and third-party segment
Governance/compliance agents: enforce brand safety, policy, budget limits, and regulatory compliance
Measurement/attribution agents: Monitor delivery, aggregate cross-publisher reporting, and close the optimisation loop
Orchestrators/planning agents: Coordinate multi-agent workflows.

What we’ve observed is that the industry agrees on what specialists are needed, but may well need to resolve what each specialist type actually does.

Why Do We Need So Many Agents?

In agentic advertising, each of these roles is played by a specialised AI agent type, and each role has a defined function.

Think of a well-staffed media agency, or an in-house marketing team. There is a strategist — that's you (the only human in here!) — who owns the campaign brief and decides what success looks like. You also have a team of specialists – and these are the agents. There is a buyer who handles inventory selection and negotiations, a creative team that produces the assets, and lastly, a compliance or brand safety lead who makes sure every decision meets the brand's rules before anything goes live.

On the publisher side, the equivalent is a sales team — the people who package inventory, respond to briefs, and match what the publisher has to offer against what buyers are looking for. In a traditional setup, that means account managers working office hours, rate cards that can't capture every audience nuance, and a small team that can only be in so many conversations at once. The seller agent is the publisher's answer to that constraint.

Each role is a specialist. They do very different things — but they work from the same brief, communicate with each other, and the whole campaign only works because they coordinate. Remove one role, or have them operating in silos without a common brief, and the campaign falls apart regardless of how skilled each individual is.

Buy, Sell, Create: How The Human Interacts with Agents

Three agents — buyer, seller, and creative — form the AI infrastructure Adzymic is building to plan, create, and optimise omnichannel campaigns for some of the world's most recognised brands.

Buyer Agents: The AI Agents for Briefing and Buying Media

A buyer agent represents the advertiser or agency — but it starts with you, the human strategist. You define what success looks like: the target audience, the budget, the objectives, the markets, the channels, the placements or contexts to avoid. That brief still comes from you – the human, with your understanding of the surrounding nuance.

The buyer agent's job is to take it and execute, translating your intent into structured queries, going out to publisher and platform agents to discover what is available, evaluating the options against your requirements, and assembling a media plan for your review.

In this example, the human strategist inputs a natural language brief for Uniqlo, which is then translated into a machine-readable request. The Buyer Agent sends requests to seller agents – acting on behalf of publishers, platforms and media owners, and receives a catalogue of available products in return.

Crucially, the buyer agent does not just find the cheapest inventory that technically matches your targeting parameters. It reasons about fit — understanding that 'reach eco-conscious millennials in Singapore' means something different on a lifestyle publisher than on a news site, and that a premium placement on a trusted local brand might outperform a cheaper but less contextually relevant impression elsewhere.

What this means for the human strategist: AI agents handle the busywork of shopping and booking inventory, while the human focuses on what to buy and why, not how to execute it.

Seller Agents: Representing Publisher Inventory

A seller agent represents the publisher. Its job is to surface and describe the publisher's inventory — audiences, contexts, formats, packages — in a way that buyer agents can discover, evaluate, and act on. Think of it as a 24/7 sales representative that never sleeps, never misses a brief, and can instantly match available inventory to incoming requests with far more precision than a traditional rate card.

A regional lifestyle publisher in Kuala Lumpur, with a highly engaged, specific audience has always struggled to compete for premium campaigns because their sales team cannot be in every conversation. A seller agent levels that playing field: it can respond to any inbound brief, at any hour, with a precisely tailored inventory proposal.

What this means for publisher sales teams: the sales team's job doesn't disappear; it shifts from responding to every inbound to governing how the seller agent represents your inventory, and focusing human attention on the relationships that still require it.

Creative Agents: A Tale of Two Roles

Imagine you work at a print shop. A customer walks in wanting a roller banner for their next trade show. In our analogy, the customer represents the human strategist or the principal.

Just hours before that, a designer from the customer’s company has taken the brief: “we need something bold and on-brand, I’m guessing it will be 3-metre tall”. This is the creative agent on the buy side. The creative agent creates.

On the print shop’s production desk, the specifications go beyond “3-metre tall”. The print shop’s production team specifies: 1,500mm by 2,000mm. Minimum 300dpi. CMYK colour profile. Bleed on all sides." They check the artwork against those requirements, flag what doesn’t fit, and produce a proof before anything goes to print. The production team is the creative agent on the sell side: it doesn’t produce creative, but validates what to produce.

The In-House Designer: Creative Agent on The Buy Side

A creative agent, working alongside the buyer agent, handles the production of ad creatives. The buyer agent briefs the creative agent, passing along the campaign intent, the required formats for each placement, and the brand guidelines. Those guidelines are pulled automatically from a machine-readable brand file that the creative agent reads directly.

Given those inputs, the creative agent produces creative assets that are verified against the brief and the brand’s rules. If needed, the agent transforms these assets and converts/resizes them for different placements (e.g., a 300x250 → 728x90). The creative agent also checks that a creative meets a publisher’s specs before it goes live.

Adzymic’s AgenX creative agent is capable of generating on-brand, multi-format creative assets in minutes.

What it means for the (human) creative team: The human creative judgment still remains. Instead, the bottleneck of manually producing potentially hundreds of asset variants every time a campaign needs to run across multiple formats and markets – is removed.

The Production Desk: Creative Agent on The Sell Side

Just like the production desk at the print shop, a publisher’s creative agent validates creative assets before a campaign goes live.

The owner of a bus shelter may have requirements around the creative it accepts and activates: maximum file size of 10MB, with a frame rate of 30fps, and no flashing effects for road safety. That is how the creative agent on the publisher side operates.

What it means for publishers: Instead of sending spec sheets, the creative agent adds an additional layer of validation – and at the same time, offers instantaneous response to the buy side.

A real conversation at Adzymic, pre-campaign. The spec sheet for the DOOH placement was just a PDF, so the error was only flagged after the creative had already been submitted to the media owner. With creative agents, the publisher's agent communicates specs directly to the buy-side creative agent, ensuring compliance is built into the creative production process itself.

Getting Started with Agentic Advertising

Just like how programmatic advertising has transformed the industry, agentic advertising is not a distant technology to observe from a safe distance. Where you go next depends on who you are.

If you are a publisher commercial lead: The Sales Agent for Publishers is the most immediate entry point. It gives your inventory a seller agent capability that can respond to buyer agents across the AdCP ecosystem — at any hour, with any brief — without scaling your sales headcount. Start at adzymic.ai/agenx/sales-agent-for-publishers. A deeper guide to how publishers are using agentic advertising is forthcoming in this content series.

If you are a media planner, digital director, or agency lead: The right starting point is understanding what a buyer agent can do for your planning workflow — and what a better brief actually looks like in an agentic context.

If you want to go deeper on AdCP, an emerging open standards: The protocol documentation is freely available at agenticadvertising.org. While it is written for builders, the overview sections are accessible to anyone who wants to understand what the standard actually specifies.

Inside The First AgenticAdvertising.org Meetup in Asia

travis@adzymic.co (Travis Teo) — Thu, 02 Jul 2026 01:10:19 -0700

This article was first published on AgenticAdvertising.org, contributed by Adzymic.

Close to 200 leaders from brands, agencies, media owners and technology partners came together for the first community meetup for AgenticAdvertising.org, hosted by Adzymic, PubMatic and Scope3. The event also marked a major first step toward launching the Southeast Asia chapter of AgenticAdvertising.org.

While much of the early momentum has been driven by the US and Europe, Southeast Asia is now approaching its own turning point. The region's path won't simply mirror what's happened in Western markets — fragmented media landscapes, a wide range of digital maturity across countries, and diverse regulatory environments mean adoption here will likely look different, and possibly move at a different pace. But that doesn't mean the region is lagging behind in ambition. Growing interest from brands, agencies, media owners, and tech partners across the ecosystem suggests Southeast Asia is carving out its own path toward agentic advertising, rather than simply waiting to catch up.

The big theme of the night: Agentic moves from theory to practice

Across the sessions, one theme came through clearly: agentic advertising is moving from theory into operating practice.

June Cheung, Head of JAPAC at Scope3, opened the afternoon with a familiar reality: a media buyer has endless options but only so many hours in the day, so they default to the publishers they already know. Agentic advertising removes that ceiling — agents continuously evaluate every publisher, build every creative, and feed performance back across the chain, while humans keep the judgment and accountability.

Panel 1: Media owners and brands weigh in

Our opening panel brought together Raj Parekh (Mediacorp), Michele Low Richards (Stellar Ace), Gibson Camson (Carousell Media Group), and James Dinglasan (Standard Chartered), moderated by Justin Lim (Adzymic), to pressure-test what agentic advertising actually means for the people running media businesses and brand budgets today.

Panel 2: Brands and agents – Who owns the media decision?

In the following panel, we brought together panelists Bharat Khatri (Omnicom Media APAC), Emir Caglayan (WPP Media), and Sudipto Das (PubMatic), moderated by Deanson Lee (Ebiquity), to tackle who actually owns the media decision as AI agents take over more of the planning and buying workflow — the consensus being that decisions must stay with the buyer (brand or agency), not the platform, with accountability shared across foundation model providers, agent builders, operators, and approvers. Khatri described Omnicom's "Know Your Agent" governance framework (modeled on banking KYC) and shared early results from its agentic rollout — roughly 87% faster campaign setup, 70% faster resolution, and 85% faster optimization across 1,000+ campaigns — while the group agreed that open protocols like AdCP are essential to prevent walled gardens from controlling decisions, and that brands now need to market to both humans and AI agents as web traffic itself becomes increasingly agentic.

Seeing the Ad Context Protocol in action

Niki Banerjee from Scope3 introduced the AdCP mental model, before Anil Gangolli, Adzymic and Michael Thorpe, PubMatic demonstrated how agents can orchestrate an omnichannel campaign from brief to buy — including inventory discovery, creative generation, media-buy creation and publisher-side approval.

Niki broke down how AdCP actually works — think of it as the HTTP of advertising, the shared language letting buyer, seller, creative, and compliance agents talk to each other. Trust comes first: each company publishes a kind of digital ID (one for brands, one for publishers) so an agent can't just pretend to be, say, McDonald's. From there, it's brief → inventory discovery → creative build → media buy, with performance feeding back to publishers so the system gets smarter over time.

Three ways to stay involved

The evening closed on a high with Southeast Asia chapter lead Travis Teo, Co-Founder and Executive Director at Adzymic, inviting the audience to join the community. Like many founding members of AAO, Travis has been involved in shaping the Ad Context Protocol and driving pilots in the region. He shared that the protocol is in its nascent stages, and his one ask was for members of the audience to continue the conversation – not only for the evening, but to continue the conversations beyond the event.

Travis shared three easy ways to get involved:

Join AgenticAdvertising.org: Sign up at agenticadvertising.org to become part of the broader community shaping the future of agentic advertising.
Join the #sea-chapter on AAO's Slack channel (sign up as a member to get access)
Start a pilot campaign: Speak to any of the organisers — Adzymic, PubMatic or Scope3 to explore your first agentic pilot.

Asia’s opportunity is now

As Travis put it in closing: Asia's opportunity is now. The protocols for agentic advertising are still being written, and everyone in the room has a role to play in writing them — piloting agentic campaigns, helping define the best practices and standards for APAC, and leading the conversation forward. With Asia's scale, diversity, and speed, the region is well placed to set its own standards rather than simply adopt someone else's. With agencies, brands, media owners, and tech providers all leaning in, the conversation doesn't end here — it continues in the AAO community, where the next generation of media buying gets built together.