What is a Multi-Agent System in the Context of Agentic AI?

This post explains "What is a Multi-Agent System in the Context of Agentic AI?" Why it matters, and how to think about it if you are responsible for building next-generation software.

Artificial intelligence is moving from cool new tech to production systems. This means architecture and system design is becoming very important.  How should be organise agentic systems?. Increasingly, the conversation is turning towards multi-agent systems, networks of intelligent agents that collaborate to achieve goals.

If single agents are useful, why multiply them? Because complexity does not scale in a straight line. Just as software engineering evolved from monolithic codebases to microservices, AI is heading towards distributed teams of agents.

Agents: the building blocks

An agent is more than a chatbot or a script. It is an autonomous software entity capable of perceiving its environment, reasoning about tasks, and acting to achieve goals. Unlike a static automation rule, an agent can interpret intent, choose tools, adapt to feedback, and continue until a defined objective is met.

Think of a personal assistant agent. It does not just answer one prompt. It manages your calendar, negotiates appointments, pulls data from your inbox, checks the weather, and then suggests a running slot. That orchestration is already powerful. But in real businesses, the workload is rarely linear. This is where multi-agent systems come in.

From lone operators to collaborative systems

In practice, many problems cannot be solved by a single agent. Imagine a customer support case that involves verifying account details, classifying the issue, escalating compliance risks, and drafting a reply. One agent could attempt all of that, but it would be brittle and hard to govern.

Instead, we design specialised agents:

• One retrieves and validates customer information.

• Another interprets the inbound message.

• A third selects the right policy response.

• A fourth generates a draft reply for human review.

Together, they behave like a team, passing tasks, checking each other’s work, and escalating when necessary. That is a multi-agent system, not one monolithic brain but a coordinated network of smaller ones.

The microservices analogy

The shift mirrors a familiar journey in software engineering. Traditional applications were once monoliths, huge, tightly coupled codebases. They worked, but they were hard to scale, slow to change, and brittle under stress.

Microservices changed the game. Each service became responsible for a single function, exposing an interface and relying on orchestration to create value. Teams could ship faster, scale selectively, and recover gracefully when parts failed.

Multi-agent systems work in a similar way. Each agent is like a microservice: specialised, loosely coupled, and composable. Instead of HTTP endpoints, they expose capabilities such as classification, summarisation, or decision-making. Orchestration stitches them together into a flow.

The benefit is not only resilience. It is also innovation. By combining agents in new patterns, you unlock emergent behaviours that would not exist if everything sat inside one black-box model. Just as microservices enabled a wave of digital platforms, multi-agent systems are enabling new classes of intelligent software.

Why it matters now

The rise of multi-agent systems reflects the real challenges of putting AI into production.

1. Scalability: A single large model can be impressive, but it is expensive and prone to bottlenecks. Splitting work across agents makes workloads cheaper and more efficient.

2. Governance: Regulated industries need audit trails. If one agent is responsible for classification and another for templated responses, it is easier to trace and validate decisions.

3. Adaptability: Business processes change. Updating a specific agent is simpler than retraining an entire monolith.

4. Safety: Isolating responsibilities makes it easier to put guardrails around risky tasks, with human-in-the-loop where needed.

These are not abstract benefits. They are the difference between a demo and a deployable product.

Patterns of collaboration

Just as software architecture has design patterns, so too does agentic AI. Some common ones include:

• Operator agents: handle repetitive tasks such as classification or triage.

• Sentinel agents: monitor activity, detect anomalies, and escalate risks.

• Archivist agents: manage memory and context, ensuring continuity across interactions.

• Coordinator agents: act as conductors, delegating work to others and integrating results.

These patterns can be combined to mirror real teams. For instance, a compliance workflow might pair an Operator to classify cases, a Sentinel to flag edge conditions, and a Coordinator to escalate anything ambiguous to a human reviewer.

The real power comes when these patterns are deployed into everyday systems such as email, CRM, call centres, and logistics platforms, where they can shoulder the routine load and let humans focus on judgement.

The role of orchestration

If multi-agent systems are teams, orchestration is the project manager. Agents need ways to communicate, share state, and sequence their work. This can be achieved in different ways:

• Pipelines: a structured flow where one agent’s output becomes another’s input.

• Markets: agents negotiate tasks or compete to provide the best answer.

• Blackboards: agents post information to a shared memory that others can read.

Choosing the right orchestration model depends on the problem. But the principle is the same. No single agent has the whole picture. Coordination is what transforms isolated skills into business outcomes.

Technology foundations

Modern multi-agent systems rely on more than clever prompts. Key ingredients include:

• Context storage (databases, vector stores) so agents can recall history.

• Tool use (APIs, functions, knowledge bases) so agents can act beyond text generation.

• Feedback loops to learn from human correction.

• Deployment platforms (cloud services, containerised runtimes) that provide scalability and governance.

Standards like MCP (Model Context Protocol) are emerging to make these integrations simpler, acting as a universal connector between agents and tools. This is still early, but it echoes the role that APIs played in the growth of the web.

Looking ahead

Multi-agent systems represent a move from AI as a single brain in a box to AI as a distributed organisation, an organisation made of agents that can be designed, audited, and improved.

Like any young architecture, there will be turbulence. Costs must fall, orchestration standards must mature, and governance must catch up. But the direction is clear.

For enterprises, the pressing question is how to adopt multi-agent systems effectively. Will they remain experiments at the edges, or become core to how your organisation delivers value?

At Dootrix, our view is simple. Just as microservices reshaped the cloud era, multi-agent systems will define the AI-native era. The organisations that embrace this shift early will build faster, safer, and smarter software, and they will be the ones leading when everyone else is still catching up.

👉 What is Agentic Computing?