Orchestrating Multiple AI Agents: Patterns and Pitfalls

Single-agent workflows are powerful, but many real-world processes require capabilities that no single agent can provide. Orchestrating multiple agents - each with its own specialisation - unlocks a new level of automation. It also introduces new complexity.

Here are the patterns and pitfalls you need to know.

Why multiple agents?

The case for multi-agent systems comes down to specialisation and separation of concerns.

A single agent trying to do everything - research, analysis, drafting, review - becomes unwieldy. Its instructions grow long and contradictory. Its behaviour becomes hard to predict and debug.

Multiple agents, each focused on a specific task, are easier to design, test and improve. They can be developed in parallel, updated independently, and combined in flexible ways.

But this modularity comes at a cost: someone or something must coordinate the agents.

Pattern 1: Sequential handoff

The simplest orchestration pattern is sequential handoff. Agent A completes its work and passes the output to Agent B, which passes to Agent C, and so on.

This works well when:

• The workflow has a clear, linear structure.
• Each agent’s output is the next agent’s input.
• There is no need for iteration or backtracking.

Examples include document processing pipelines, where one agent extracts data, another validates it, and a third routes it to the right system.

The pitfall here is rigidity. If the workflow needs to branch, loop or handle exceptions, sequential handoff becomes awkward.

Pattern 2: Supervisor agent

A more flexible pattern uses a supervisor agent that coordinates the others. The supervisor decides which agents to invoke, in what order, and how to combine their outputs.

This works well when:

• The workflow has conditional logic or branching.
• Agents need to be invoked dynamically based on context.
• A single point of control simplifies monitoring and debugging.

The supervisor pattern is common in customer service scenarios, where the supervisor triages an enquiry and dispatches it to specialist agents for billing, technical support or escalation.

The pitfall is that the supervisor becomes a bottleneck and a single point of failure. If it is not well-designed, it can make poor routing decisions or struggle to handle unexpected inputs.

Pattern 3: Blackboard architecture

In more complex scenarios, agents can communicate through a shared workspace - often called a blackboard. Agents read from and write to the blackboard, contributing their outputs and reacting to the contributions of others.

This works well when:

• Agents need to collaborate on a shared artefact.
• The order of operations is not fixed in advance.
• Multiple agents may need to refine or correct each other’s work.

An example is a research and analysis workflow, where one agent gathers sources, another summarises them, a third critiques the summaries, and a fourth synthesises the final output.

The pitfall is coordination overhead. Without clear rules about who writes what and when, the blackboard can become chaotic. Race conditions, conflicts and stale data are all risks.

Pattern 4: Hierarchical delegation

Some workflows benefit from a hierarchy of agents. A top-level agent breaks a task into sub-tasks, delegates to subordinate agents, and aggregates their results.

This works well when:

• Tasks are naturally decomposable.
• Subordinate agents can work in parallel.
• The top-level agent can meaningfully evaluate and combine outputs.

An example is a due diligence workflow, where the top-level agent identifies the checks required, delegates each to a specialist agent, and compiles the final report.

The pitfall is depth. Too many layers of delegation make the system hard to understand and debug. Each layer adds latency and opportunities for error.

Common pitfalls across patterns

Regardless of the pattern you choose, several pitfalls recur:

Unclear ownership

When multiple agents touch a process, it can be unclear who is responsible for what. This leads to gaps, duplication and finger-pointing when things go wrong.

Mitigation: Define clear responsibilities for each agent and document the handoff points.

State management

Multi-agent systems need to track state - what has been done, what is pending, what has failed. Without a robust state management approach, agents can lose context or repeat work.

Mitigation: Use a persistent state store that all agents can read and write, with clear conventions for updates.

Error propagation

When one agent fails, the error can cascade through the system. Downstream agents may receive garbage input and produce garbage output.

Mitigation: Build error handling into each agent and into the orchestration layer. Fail fast, fail loudly, and make recovery paths explicit.

Debugging complexity

Tracing a problem through a multi-agent system is harder than debugging a single agent. Logs are scattered, causality is unclear, and reproducing issues is difficult.

Mitigation: Invest in observability from the start. Correlate logs across agents, trace requests end-to-end, and make it easy to replay failed runs.

Start simple

The temptation is to design a sophisticated multi-agent architecture from day one. Resist it.

Start with the simplest pattern that meets your needs. Add complexity only when you have evidence that the simpler approach is insufficient.

A well-designed single agent often outperforms a poorly designed multi-agent system. Orchestration is powerful, but only when it is earned.