AI agent orchestration: Your AI tools need a manager too

Artificial intelligence is disrupting marketing.

For the past few years, we’ve maximized the machine learning capacities of our campaigns and experimented with generative AI to standardize creative production. Now we’re entering into a new era: the integration of autonomous AI agents. These systems go beyond the boundaries of prediction and creation to make decisions and behave proactively, transforming marketers into machine-augmented humans.

But how can these agents be orchestrated to maximize their impact? Let’s explore the foundational concepts, the role of the orchestrator, and the evolution that is needed in your technological ecosystem.

Agentic AI and Multi-agent Systems (MAS)

We’re becoming increasingly familiar with chatbots that are able to lead dynamic conversations. However, AI agents are going beyond conversation to act on their own.

Generative AI works off of an instruction (a prompt) and produces an output―text, image, code, summary, categorization, or data extraction. It is reactive by nature: It waits for us to tell it what to produce, then generates an answer. That’s it.

Agentic AI, on the other hand, represents a complete architecture: an orchestrated system combining generative models, specialized tools (databases, specific features, external website), contextual memory management, decision-making policies, and iterative execution loops. Its distinguishing feature doesn’t reside in the kinds of tasks that are accomplished, but in its capacity for autonomy and orchestration.

Let’s take a concrete example to illustrate this difference in architecture with the instruction “Increase the open rate of our newsletter by 20%.” A generative model could generate suggestions for subject lines. An AI agent, that has the right tools, will examine the historical data, automatically segment the list of contacts based on their observed behaviours, generate and test different variations of personalized subject lines, then program sending for statistically optimal times for each segment―all in an autonomous manner, in a continuous improvement loop.

In a marketing context, where the different types of expertise the field is composed of are complex, a single omniscient AI agent would make little sense. It would be like asking a musician to simultaneously play every instrument in a symphony orchestra. Even the most versatile people have their limitations; true performance comes from specialists who excel in their particular discipline.

So the most appropriate approach involves establishing a multi-agent system (MAS): a collection of specialized agents that collaborate to solve complex problems. Its foundational principles are agent specialization, the division of labour, and collective intelligence.

It’s a model that takes its inspiration from the work done by people or, to continue our analogy, a group of musicians.

How does an agent orchestrator work?

In a multi-agent system, collaboration isn’t chaotic, it’s organized. Let’s take a look at the essential ingredients of this system, their basic architecture, and the different models of communication that allow agents to effectively collaborate.

Agent orchestration is based on three pillars:

1. The orchestrator (the conductor): Occupying the role of central coordinator, the orchestrator receives the initial objective and breaks it down into a list of potential tasks. But its value lies in its iterative operation: it assigns the first task to a specialized agent, analyzes the result obtained, evaluates any deviation compared to the targeted objective, then dynamically decides what the next step will be and which agent would be best suited to executing it. This capacity for adaptation in the midst of a process―rather than the simple linear execution of a pre-established plan―is exactly the advantage of a multi-agent system relative to traditional automated workflows.

2. Specialized agents (the experts): Thanks to their access to a variety of resources (web searches, writing, Model Context Protocol, just to mention a few), each agent excels in a specific area: data analysis, composing content, optimizing advertising, interacting with the CRM, etc. This specialization makes them a lot more high-performance than a generalist agent that attempts to do everything.

3. Communication protocols (the language in common): To collaborate effectively, agents need to be able to “speak” and understand one another. As with the members of a marketing team who share briefs and data, agents exchange information in an organized way. This is where a key element comes into play: contextual memory management. Contrary to what you might think, this memory isn’t necessarily shared by all agents. The most common architecture attributes the full memory to the central orchestrator, which then provides each specialized agent with only the relevant context needed to complete its specific task. This approach avoids information overload while guaranteeing the overall coherency of the process as well as the minimization of associated costs.

The benefits of using an orchestrator

A counterintuitive principle emerges when designing multi-agent systems: The more tools an agent has at its disposal, the less predictable the results become. The proliferation of options drastically increases the number of possible decisional pathways, reducing reproducibility and creating the likelihood of expensive execution loops.

This is precisely why the ideal architecture opts for highly specialized agents, each with mastery over a limited tool kit that is perfectly adapted to its area of expertise. The intelligence of the system doesn’t lie in the versatility of each agent, but in the ability of the orchestrator to mobilize the right agent at the right moment for the right task.

This modular approach guarantees both the predictability of results and reduced cost of execution.

The challenges of agentic orchestration

High-performance agentic systems use AI to decide (through reasoning and sorting) and classic coding to execute (to guarantee reliability).

To return to our metaphor, the conductor (the AI orchestrator) guides the overall interpretation, but it’s the specialist musicians (the agents) who play their particular score with precision. Each agent has deterministic tools at its disposal―fragments of verified code―that it can use to guarantee reliable execution.

This layered architecture―where the AI decides and directs while the code executes―guarantees both strategic flexibility and operational reliability. This is what distinguishes an impressive prototype from a system that is reliable on a daily basis.

A (r)evolution in your marketing stack

Introducing orchestrating agents isn’t limited to the addition of yet another tool. It fundamentally transforms the way in which your marketing systems work together. Here are three major evolutions to keep an eye out for.

Data unification becomes a reality

Today, companies use dozens of different marketing tools (CRM, ad platform, analytics, etc.) that operate in silos. This fragmentation of the data makes it hard, if not impossible, to get a complete view of the customer journey. This is a significant obstacle at a time when artificial intelligence can be used to optimise every stage of the customer experience (CX).

Orchestrator agents resolve this issue by automatically connecting all of these tools. They bring together scattered data and create a unified view of the customer. Their strength lies in the fact that they don’t just stop at transferring data like traditional integration tools, but analyze them and make intelligent decisions in real time. An interesting example was implemented by HubSpot with HubSpot Breeze AI.

Increasing the capacities of the Customer Data Platform (CDP)

Traditionally, CDPs consolidate and segment your customer data. With agentic AI, these platforms are evolving into autonomously intelligent platforms that are able to:

• Determine and automatically execute the next best action for each customer in real time, coordinating cross-channel touchpoints (journey orchestration agent)
• Detect disengagement signals and automatically deploy personalized reactivation strategies (proactive retention agent)
• Continuously unify fragmented profiles by learning behavioural patterns (identity resolution agent)
• Monitor and automatically apply consent and privacy rules to all activations (compliance agent)

A CDP will no longer be a passive warehouse, but an independent intelligence implementing your customer strategy, as seen in examples from Salesforce (Salesforce Agentforce) or even the Adobe Real-Time CDP with AI Assistant.

The emergence of new tool categories

New players are appearing: agent orchestration platforms specialized for marketing, pre-built agent marketplaces, governance tools, and agent observability tools. Your martech is being enhanced with a new layer of intelligence.

The decline of “all-in-one” platforms

Why pay for a monolithic marketing suite when an AI orchestrator can intelligently interconnect your best specialized tools? Companies will progressively give preference to stacks composed of best-of-breed tools orchestrated by AI.

Our 5 recommendations for deploying a multi-agent ecosystem

1. Invest in the fundamentals

Data quality is what drives AI. Before deploying agents, ensure that:

• Your data are clean and deduplicated
• Key sources are consolidated (CRM, data analysis, advertising platforms)
• Clear governance rules are established

Without a data strategy, your agents will produce mediocre results.

2. Start small and prove value

Identify the use cases with high potential for ROI and limited scope to build your first successful experiment and generate enthusiasm internally:

• A campaign management agent that creates, tests, and automatically optimizes ad variants on a continuous basis
• A lead nurturing agent to orchestrate the prospect qualification journey until it is transferred to sales
• A writing agent that identifies content opportunities, generates articles, and distributes them automatically)

3. Adapting as a marketing professional

Repetitive tasks (execution, management, reporting) will be delegated to AI. Marketers will therefore define strategy, supervise AI agents, interpret the results, and ensure the responsible use of these systems.

4. Develop internal skills

Your teams must evolve to work effectively with AI agents:

• Understand the basic principles of AI (no need to be a data scientist).
• Have a command of prompt engineering as a statistical discipline: rigorously test the same objectives in different formats, evaluate the effectiveness based on clear KPIs, and optimize according to the model (differences between Gemini and ChatGPT, or between GPT 4.0 and later versions, are substantial in terms of quality, speed, and cost).
• Develop skills in interpreting the results produced by AI.
• Master the supervision and governance of autonomous systems by integrating a mechanism for registering feedback and evaluating results.

Invest in training that is practical, not just theoretical. At adviso, our training goes beyond theory and technology, helping you to turn AI into concrete actions.

5. Adopt an iterative approach

Deploy agents progressively, rigorously measure their impact at every stage, and use a test-and-learn approach to continuously optimize systems and minimize risk

Orchestrating your AI agents: creating a marketing symphony

Agentic AI opens up a world of fascinating possibilities for marketing: wide-scale personalization, real-time optimization, and a freeing up of time for creativity. But such power comes with great responsibility.

Establishing clear governance from the get-go is not just an option, it’s a necessity. Defining rules for use, protocols for validation, and mechanisms for surveillance will guarantee that your AI agents operate with respect for your values, your ethics, and current legislation.

Pioneers in this technology won’t be those who deploy the most agents, but those who orchestrate them the best. Start small, rigorously measure (by introducing adapted KPIs such as the hallucination rate, response completion, cost, etc.) and progressively build your competitive advantage.

The orchestra has tuned up, the score is waiting. All that’s left is to play your marketing symphony. And if you're worried about hitting the wrong notes, adviso can support you every step of the way in your AI multi-agent orchestration. Get in touch to discuss it further.