Lesson 970 of 2116
Multi-Agent Coordination Patterns: Orchestration vs Choreography
Multi-agent systems can be orchestrated (central coordinator) or choreographed (peer-to-peer). The choice shapes failure modes, observability, and operational complexity.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Multi-Agent Handoff Coordination: Avoiding Context Loss
- 3The premise
- 4The Multi-Agent Debate Pattern: Two Models Argue, One Decides
Concept cluster
Terms to connect while reading
Section 1
The premise
Multi-agent systems require explicit coordination patterns; the choice between orchestration and choreography shapes long-term operational characteristics.
What AI does well here
- Choose orchestration for use cases needing strong observability and centralized control
- Choose choreography for use cases needing scale, fault tolerance, and loose coupling
- Implement explicit handoff protocols between agents (not implicit through shared state)
- Build observability that traces requests across multi-agent boundaries
What AI cannot do
- Get distributed-systems benefits without distributed-systems complexity
- Substitute for clear ownership of each agent's behavior
- Avoid the operational cost of coordination (always present in some form)
Key terms in this lesson
Section 2
Multi-Agent Handoff Coordination: Avoiding Context Loss
Section 3
The premise
Multi-agent handoffs lose context if the handoff isn't designed; specific patterns preserve fidelity.
What AI does well here
- Design explicit handoff schemas (what's passed, in what format)
- Validate receiving agent has what it needs before continuing
- Maintain conversation history across handoffs
- Test handoff edge cases (partial state, errors)
What AI cannot do
- Eliminate handoff complexity in multi-agent systems
- Get reliable behavior without explicit handoff design
- Predict every edge case
Section 4
The Multi-Agent Debate Pattern: Two Models Argue, One Decides
Section 5
The premise
On hard, ambiguous tasks, two debating agents and a judge often outperform one strong agent thinking alone.
What AI does well here
- Surface tradeoffs the single-agent path missed
- Catch hallucinated facts when one side challenges the other
- Produce a transcript that is auditable after the fact
- Improve calibration on questions with no clean answer
What AI cannot do
- Justify 3x the cost on routine tasks
- Eliminate shared blind spots when both agents share the same base model
- Replace a real human reviewer for legally significant calls
Section 6
Agentic AI: when multi-agent setups stop being worth it
Section 7
The premise
Multi-agent architectures are fashionable but often slower and less reliable than a single well-prompted agent with the same tools. Add agents only when their roles cannot be expressed as different prompts to one agent.
What AI does well here
- Pass structured messages between agents when given a protocol
- Specialize behavior when given a clear role prompt
- Hand off tasks when the handoff condition is explicit
What AI cannot do
- Coordinate without an externally enforced protocol
- Avoid duplicated work without explicit ownership rules
- Match a single-agent baseline you never measured
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