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.
What You'll Learn
- Design role-based delegation patterns where a coordinator assigns tasks to specialists
- Implement structured debate between agents to improve output quality
- Configure shared memory pools for cross-agent knowledge sharing
- Set up escalation policies for handling agent failures gracefully
- Monitor and optimize multi-agent execution performance
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.
Prerequisites
- Experience building multi-step workflows (see Building a Multi-Step Workflow)
- At least 3-4 specialized agents configured with distinct roles
- Familiarity with the Visual Builder
- Understanding of when multi-agent systems outperform single agents
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.
Escalation and Fallback
Define escalation policies for each agent. If an agent fails or produces low-quality output, the system can retry with a different model, delegate to a more capable agent, or flag for human review. This prevents silent failures in production workflows.
Performance Optimization
Multi-agent systems are inherently slower than single agents. Optimize by: running independent agents in parallel, using cheaper models for routine steps, caching repeated lookups, and keeping system prompts concise. Monitor the execution timeline to identify bottlenecks.
Start with 3 agents, not 10
A coordinator + researcher + writer is enough to prove the pattern. Add more agents only when you hit quality ceilings with fewer ones.
Debate rounds are expensive
Each debate round multiplies your API calls by the number of participants. 2-3 rounds is usually sufficient. More rounds rarely improve quality beyond that.
Shared memory is ephemeral
The shared memory pool exists only during mission execution. It does not persist between runs. For persistent knowledge, use the Memory Store feature.
Name your coordinator clearly
The coordinator's system prompt should list all available agents by name and capability. Ambiguous references like "the research agent" cause misassignment.
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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