Orchestrating AI Agents: Patterns and Pitfalls
Practical lessons from building multi-agent AI systems
Building AI agents that actually work requires more than prompt engineering. It requires systems thinkingāunderstanding how agents interact, fail, and recover.
The Core Challenge
Single-turn AI is straightforward: input ā output. Multi-agent orchestration is not. Youāre dealing with:
- State management across multiple calls
- Error propagation between agents
- Context degradation over long chains
- Emergent behaviors you didnāt design
Patterns That Work
The Supervisor Pattern
One agent coordinates others. It:
- Breaks tasks into subtasks
- Routes subtasks to specialized agents
- Synthesizes results
- Handles failures and retries
This mirrors how good managers workānot doing everything, but orchestrating those who do.
The Critic Pattern
Pair a generator agent with a critic agent:
- Generator produces output
- Critic evaluates against criteria
- Generator revises based on feedback
- Iterate until criteria met
This dramatically improves quality over single-pass generation.
The Memory Pattern
Agents without memory repeat mistakes. Implement:
- Short-term context for the current task
- Long-term memory for learned patterns
- Episodic recall for similar past situations
The CAMELOT framework implements this through structured context windows and semantic retrieval.
Pitfalls to Avoid
The Telephone Game
Chain too many agents and quality degrades. Each handoff introduces interpretation drift. Keep chains short; prefer parallel over sequential.
The Infinite Loop
Agents that can call other agents can recurse forever. Always implement:
- Maximum depth limits
- Time-based timeouts
- Token budget caps
The Hallucination Cascade
One agentās hallucination becomes the next agentās fact. Build verification into the pipelineāagents that check other agentsā work.
The Context Cliff
Models have finite context windows. When you exceed them, behavior degrades unpredictably. Monitor context usage and implement summarization strategies.
The Systems Perspective
Agent orchestration is a systems problem. Youāre designing:
- Feedback loops between agents
- Balancing mechanisms to prevent runaway behavior
- Leverage points where small changes have big effects
The most effective orchestration systems feel more like ecosystems than assembly lines.
Current Best Practices
- Start simple. One agent, one task. Add complexity only when needed.
- Log everything. Youāll need it for debugging.
- Build in observability. Know what your agents are doing at all times.
- Plan for failure. Every agent will fail. Design for graceful degradation.
- Iterate fast. The field moves quickly. What works today may not work tomorrow.
Whatās Next
The tooling is improving rapidly. Weāre moving from:
- Manual prompt chains ā Declarative workflows
- Implicit state ā Explicit state machines
- Trial and error ā Systematic evaluation
The CAMELOT framework is my attempt to codify what Iāve learned. Itās evolving as I learn more.