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.

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.