Multi-Agent Systems: When One AI Isn't Enough

The Limits of a Single Agent

A single AI agent works well for focused, well-scoped tasks. Research this company. Draft this email. Process this document.

But some business processes are too broad, too complex, or too long-running for a single agent to handle well. When a task requires different specialisations at different stages — or when parallelism would dramatically speed things up — multi-agent architectures become the right tool.

Understanding when and how to use them is increasingly a competitive advantage.

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What a Multi-Agent System Looks Like

A multi-agent system consists of multiple AI agents, each specialised for a specific role, coordinated by an orchestrator that routes tasks and manages state.

The simplest version is a pipeline — agents run in sequence, each one consuming the output of the previous.

A more complex version is a network — agents run in parallel, each working on a different aspect of the same problem, with an orchestrator synthesising their outputs.

A concrete example: proposal generation

A consulting firm wants to automate their proposal process. A single-agent approach would struggle — the task is too broad and requires genuinely different capabilities at different stages.

A multi-agent approach:

• Research agent — gathers information about the prospect, their industry, recent news, and publicly visible challenges
• Competitor analysis agent — runs in parallel, researching what competitors have proposed for similar clients
• Pricing agent — queries internal pricing database, applies rules, generates fee estimate
• Writer agent — receives output from all three, drafts the proposal narrative in the firm's house style
• Review agent — checks the draft against the firm's quality criteria, flags issues

Orchestrator manages the sequence and parallelism, passes context between agents, and delivers the finished draft to a human for final review.

What used to take a senior consultant half a day now takes 12 minutes of agent time and 20 minutes of human review.

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The Orchestrator Pattern

The orchestrator is the most important component in a multi-agent system. It:

• Receives the high-level goal
• Breaks it into subtasks
• Routes subtasks to the appropriate specialist agents
• Manages dependencies (agent B can't start until agent A finishes)
• Handles failures (if agent C fails, retry or reroute)
• Synthesises outputs into a coherent result

In most implementations we build, the orchestrator is itself an LLM — typically a more capable, slower model (like Claude Opus) coordinating faster, cheaper specialist models for execution tasks.

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When Multi-Agent Is Worth the Complexity

Multi-agent systems are more complex to build and maintain than single agents. They're worth it when:

The task requires parallel workstreams — multiple independent research or analysis tasks that can run simultaneously

Different stages require different capabilities — a task that needs deep research, then analysis, then writing is better served by specialist agents than a generalist

The task is too long for a single context window — complex, long-running tasks benefit from agents that hand off context cleanly rather than one agent losing track of earlier work

Reliability matters — specialist agents with narrow scopes are easier to test, monitor, and correct than generalist agents doing everything

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The Emerging Patterns We're Watching

A few multi-agent patterns we're seeing prove their value in real deployments:

The researcher-writer pattern — one agent gathers information, another writes from it. Cleaner outputs than a single agent trying to do both.

The critic pattern — one agent produces output, a second agent reviews it against quality criteria before it reaches a human. Significantly reduces errors.

The monitor-escalate pattern — a lightweight monitoring agent watches a stream of data continuously, escalating to a more capable (and expensive) analysis agent only when something unusual is detected.

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Practical Advice

If you're new to agents, don't start with multi-agent. Build one agent well, understand its failure modes, learn how to evaluate it. Multi-agent compounds both the power and the complexity.

When you're ready to graduate: identify the bottleneck in your current single-agent process. That bottleneck — the stage that takes longest or produces the most errors — is the best candidate for splitting into a specialist agent.

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Building complex workflows that might benefit from multi-agent architecture? Let's talk →