← Back to Blog

Multi-Agent Systems Explained

A single AI agent handles a focused task well. But real business processes are rarely a single task — they are sequences of specialised steps that depend on each other. Multi-agent systems coordinate multiple agents to complete these workflows reliably.

Why One Agent Is Not Always Enough

When you build your first AI agent, the temptation is to make it do everything: research a company, write a personalised email, send it, log the result, and follow up in three days. In practice, agents that try to do everything tend to do nothing particularly well. Each step introduces potential failure, and a long chain of steps compounds errors.

The more robust pattern is to decompose a complex workflow into discrete, focused agents — each responsible for one thing — and coordinate them as a pipeline. This is the principle behind multi-agent systems: separation of concerns applied to AI automation.

How Multi-Agent Systems Work

In a multi-agent system, agents are connected so that the output of one becomes the input of the next. The simplest pattern is sequential:

1. Agent 1 (Research) — takes a company name, queries LinkedIn and news sources, returns a structured research summary
2. Agent 2 (Personalisation) — takes the research summary and a product brief, generates a personalised outreach message
3. Agent 3 (Delivery) — takes the personalised message, formats it, and sends it via the configured email connector
4. Agent 4 (Logging) — records the outreach in the CRM with a timestamp and a copy of the message

Each agent is independently testable and replaceable. If the research agent starts returning poor data, you fix that agent without touching the personalisation or delivery agents.

Parallel Agent Execution

Not all multi-agent workflows are sequential. Some tasks can run in parallel — and doing so dramatically reduces total runtime. A competitive analysis workflow, for example, might fan out to five research agents simultaneously (one per competitor), then pass all five outputs to a synthesis agent that generates a comparison report.

Parallel execution requires a coordinator that dispatches work to multiple agents and waits for all results before proceeding. This is more complex to build from scratch but straightforward in platforms designed for it.

Orchestrator-Worker Pattern

A common architecture for multi-agent systems is the orchestrator-worker pattern. A central orchestrator agent receives the top-level task, breaks it into subtasks, dispatches each subtask to a specialised worker agent, collects the results, and synthesises a final output.

This pattern works well for tasks where the subtasks are predictable but the number of them varies (e.g., research five companies this week, twenty next week). The orchestrator dynamically decides how many worker agents to spin up and what to send each one.

When to Use Multi-Agent vs Single Agent

Multi-agent systems add coordination overhead. They are not always the right choice. Use a single agent when:

• The task fits within a single, short workflow with fewer than five steps
• Each step uses the same data and there is no meaningful decomposition
• Latency is critical and coordination overhead matters

Reach for a multi-agent system when:

• Different steps require different tools or data sources
• Steps can fail independently and you want isolated retries
• The workflow includes parallel workstreams
• You want to reuse individual agents across multiple larger workflows

Real Examples in Production

Multi-agent systems show up across industries once teams move past initial automation experiments:

• Sales — prospect research → personalised outreach → CRM logging → follow-up scheduling
• Finance — data collection → anomaly detection → narrative generation → report distribution
• DevOps — log analysis → root cause identification → remediation suggestion → incident ticket creation
• Recruitment — CV parsing → fit scoring → candidate summary → interview scheduling email

Building Multi-Agent Systems Without Code

Most multi-agent frameworks (LangGraph, CrewAI, AutoGen) require Python and significant orchestration code. Open Enterprise takes a different approach: agents and their connections are defined in YAML. A multi-agent pipeline is a YAML file that specifies each agent step, its inputs and outputs, and whether steps run sequentially or in parallel. No custom orchestration code required.

This makes multi-agent systems accessible to teams without dedicated ML engineers — a product manager or a technical analyst can define and iterate on agent pipelines without writing a line of Python.

---