Multi-Agent Coordination: Architectures That Scale

Why Single Agents Have Limits

A single autonomous agent can plan, reason, and execute across a remarkable range of tasks. But single agents have a fundamental constraint: context window and capability scope. Complex enterprise workflows involve more context, more tool calls, and more domain-specific reasoning than a single agent can reliably handle in one execution.

Multi-agent architectures solve this by decomposing complex workflows across specialised agents — each with a focused capability set, a defined scope, and clear interfaces for handing off work to other agents in the network.

The Orchestrator-Worker Pattern

The most reliable multi-agent architecture for enterprise use cases is the orchestrator-worker pattern. A central orchestrator agent receives the high-level goal, decomposes it into subtasks, assigns each subtask to a specialised worker agent, monitors progress, and synthesises results into a final output.

Worker agents are domain-specialists: a document extraction agent, a data validation agent, a CRM lookup agent, a report generation agent. Each operates within a defined capability boundary. The orchestrator manages coordination — it doesn't need to understand how each worker accomplishes its task, only what each worker can and cannot do.

• Orchestrator: goal decomposition, task assignment, progress monitoring, result synthesis
• Worker agents: domain-specific capability, structured input/output interfaces
• Tool layer: APIs, databases, external systems accessible to worker agents
• Memory layer: shared context store accessible across the agent network

Memory Architecture in Multi-Agent Systems

How agents share context is the most consequential architectural decision in multi-agent system design. Three patterns exist: each agent maintains independent memory (simple but leads to information silos and redundant work), agents share a common memory store (powerful but creates race conditions and consistency challenges), and a hierarchical pattern where the orchestrator maintains a master context that workers read from and write to in a structured way.

The hierarchical pattern is most robust for enterprise use cases. The orchestrator maintains the authoritative state of the task. Workers read the relevant slice of context for their subtask, execute, and write their output back to the orchestrator's context store. This prevents workers from having inconsistent views of task state while avoiding the complexity of fully distributed shared memory.

Failure Handling and Circuit Breakers

Multi-agent systems have more failure modes than single-agent systems — and failures can cascade. If a worker agent begins producing incorrect outputs, the orchestrator may continue assigning it work and incorporating its flawed outputs into the final result.

Every production multi-agent system needs circuit breaker logic: mechanisms that detect when an agent is degrading (elevated error rate, increased latency, unusual output patterns) and route work away from it before failures cascade. Implement confidence scoring on worker agent outputs, with automatic escalation to human review when confidence falls below threshold.