Single-agent AI systems hit limits when tasks require diverse expertise or complex multi-step reasoning. Multi-agent orchestration solves this by having specialised agents collaborate — each with its own context, tools, and decision-making.
Why Multi-Agent?
Consider a task like "analyse this dataset, write a report, and deploy a dashboard". A single agent would need deep skills in data analysis, technical writing, and deployment — a tall order. With multi-agent orchestration, you have:
- A Data Agent that reads and analyses the dataset
- A Writer Agent that produces the report from the analysis
- A DevOps Agent that handles deployment and infrastructure
- An Orchestrator Agent that coordinates the workflow, passes context between agents, and handles errors
Architecture Patterns
Hierarchical Orchestration
A central orchestrator agent manages sub-tasks, dispatching work to specialised leaf agents and aggregating results. This is the most common pattern and works well for well-defined workflows with clear dependencies.
Peer-to-Peer Collaboration
Agents communicate directly with each other, sharing intermediate results and negotiating next steps. This is more flexible but requires careful design to avoid coordination overhead and circular dependencies.
Key Lessons from Production
- Always time-box agents — Without explicit timeouts, a single agent can stall the entire workflow
- Pass structured context, not raw output — Agents should receive structured summaries, not hundreds of pages of source material
- Error handling at every level — Each agent should handle its own errors gracefully, and the orchestrator should have fallback strategies
- Observability is non-negotiable — Trace each step of the agent workflow to debug failures and optimise performance
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