Multi-agent systems coordinate multiple specialised AI agents to complete complex tasks that a single agent cannot handle reliably — distributing work, parallelising effort and improving output quality.
A multi-agent system is an AI architecture in which several distinct agents — each with a defined role and capability set — collaborate to complete tasks that are too complex, too long, or too varied for a single agent to handle reliably. One agent might gather information, another analyse it, a third draft an output, and a fourth verify the result against a compliance checklist. An orchestrating agent coordinates their sequence, manages handoffs, and assembles the final product. This architecture reflects a practical reality: just as human organisations divide complex work across specialists, effective AI systems often perform better when responsibility is distributed rather than concentrated.
In a multi-agent system, each agent is a distinct unit of AI capability. It has its own instructions (system prompt), its own set of tools it can call, and often its own model selection — a fast, cost-efficient model for straightforward subtasks, a more capable reasoning model for complex analysis.
Agents communicate through structured messages passed via an orchestration layer. The orchestrator (itself often an AI agent) decomposes an incoming task into subtasks, routes each subtask to the appropriate specialised agent, tracks progress, handles errors, and synthesises outputs. Some architectures are fully hierarchical — a single orchestrator directs all subordinates. Others are more distributed, with agents handing off to each other in a defined sequence or dynamically routing based on intermediate results.
Complex business workflows typically involve heterogeneous tasks: extracting data from documents, reasoning about that data, generating structured outputs, validating them against rules, and triggering downstream systems. Compressing all of this into a single agent produces a system that is difficult to test, difficult to maintain, and prone to compounding errors across its long reasoning chain.
Multi-agent architectures address this by isolating each step. A failure in the document extraction agent does not contaminate the analysis agent's outputs if the orchestrator detects and handles the error cleanly. Each agent can be tested independently. Individual agents can be retrained or replaced when their domain changes without rebuilding the entire system.
BCG's 2025 research found that AI agents are expected to account for approximately 29% of total AI value by 2028, up from 17% in 2025 — growth that will be driven substantially by multi-agent systems tackling higher-complexity processes.
The core components of a multi-agent system are:
Multi-agent systems are considerably more complex to build and operate than single-agent designs. The additional complexity is justified when a workflow has clearly separable stages, when different stages benefit from different models or tool sets, when parallel execution would meaningfully accelerate completion, or when volume is high enough that modular testing and maintenance pays off.
Organisations new to agentic AI should build and validate single-agent workflows first. The discipline required to define a reliable single agent — clear scope, tested tools, explicit error handling — is the same discipline needed to compose a multi-agent system, but the stakes of getting it wrong are lower in a single-agent context.
Edison AI's AI implementation engagements regularly include a workflow decomposition phase where complex processes are mapped into candidate agent responsibilities before any code is written. This mapping surfaces whether a multi-agent architecture is genuinely warranted or whether a well-designed single agent with good tooling is sufficient.
Observability is non-negotiable. Every agent interaction — inputs, outputs, tool calls, errors — must be logged to a centralised trace, or diagnosing failures in a multi-agent flow becomes intractable.
Before commissioning a multi-agent build, produce a workflow decomposition document: list each logical stage in the target process, identify which stages are separable, and assess whether different model characteristics or tool sets genuinely benefit different stages. Use this to determine whether multi-agent architecture is warranted and, if so, what the minimum viable agent graph looks like. Begin with two to three agents before expanding.
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A multi-agent system is an architecture in which multiple distinct AI agents collaborate to complete a task. Each agent typically has a specialised role — one might research, another draft, another verify — and an orchestrating agent coordinates their work, assembles results and manages handoffs.
Single agents become unreliable as task complexity grows. Specialised agents perform better within their defined scope, parallel execution is faster than sequential processing, individual agents can use different models tuned for their specific task, and errors in one agent can be isolated without contaminating the whole workflow.
The primary challenges are coordination overhead, error propagation between agents, increased observability requirements, and the difficulty of debugging failures that span multiple agents. Each agent interaction is a potential point of failure, so robust error handling and logging across the full agent graph is essential.
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Article: Multi-Agent Systems: How Specialised AI Agents Work Together
