When One Agent Isn't Enough
Some problems require multiple perspectives. Orchestration lets you assign roles, share context between agents, and build systems that improve through structured collaboration.
Multi-agent orchestration goes beyond simple chaining. It's about building systems where specialized agents collaborate, debate, and reach consensus — handling complex tasks that no single agent could solve alone.
Delegation Patterns
In role-based delegation, a coordinator agent breaks a task into subtasks and assigns each to the most qualified specialist. The coordinator reviews outputs, resolves conflicts, and synthesizes a final result. This mirrors how a project manager operates.
Consensus and Debate
When agents disagree, run a structured debate. Each agent presents its position, a judge agent evaluates the arguments, and the system converges on the strongest answer. This dramatically improves output quality for nuanced tasks like strategy or code review.
Shared Memory
Agents in an orchestrated workflow share a memory pool. Facts discovered by one agent are immediately available to others. FlowManner tracks provenance — you can always see which agent contributed each piece of knowledge.
Step-by-Step
Define Agent Roles
Create specialized agents for each role: researcher, analyst, writer, reviewer, coordinator. Give each a focused system prompt that defines its expertise and boundaries.
Set Up the Coordinator
Build a coordinator agent whose job is to decompose the incoming task, assign subtasks to specialists, and synthesize results. Its system prompt should include the roster of available agents and their capabilities.
Configure Shared Memory
Enable the shared memory pool in your mission settings. All agents in the workflow will read from and contribute to the same knowledge store during execution.
Add a Debate Stage
Insert a debate node between agents that need to reach consensus. Configure the number of rounds (2-3 is usually sufficient) and the judge agent that evaluates arguments.
Set Escalation Policies
Define what happens when an agent fails or produces low-quality output. Escalation policies can retry with a different model, delegate to a more capable agent, or flag for human review.
Run and Monitor
Execute the orchestration and watch the task graph in real time. Each agent's status, reasoning, and output stream live. Pay attention to handoff points where one agent's output feeds another's input.
Iterate on Performance
After each run, review the execution log. Identify bottlenecks — is the coordinator spending too long decomposing tasks? Is the reviewer too strict? Adjust prompts and model selections based on real data.
Related guides
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