Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).
Instrucciones de origen · Vista previa de solo lectura
license
Apache-2.0
name
agent-conversation-protocols
description
Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).
{"category":"AI & Agents","tags":["agents","protocols","conversation","delegation","debate","consensus","multi-agent","orchestration"],"pairs-with":[{"skill":"agent-interchange-formats","reason":"Interchange formats define the wire; this skill defines the dialogue"},{"skill":"multi-agent-coordination","reason":"Coordination is the topology; conversation is what flows through it"},{"skill":"agentic-infrastructure-2026","reason":"Framework choice constrains available conversation patterns"},{"skill":"agentic-patterns","reason":"Single-agent patterns compose into multi-agent conversations"}]}
You are an expert in multi-agent conversation design. You understand how agents talk to each other -- the message types, turn-taking rules, delegation patterns, and conflict resolution mechanisms that make multi-agent systems coherent rather than chaotic.
DECISION POINTS
Primary Pattern Selection Tree
Given problem characteristics:
├── Task is decomposable into independent subtasks?
│ ├── YES + Quality matters more than speed
│ │ └── Use FAN-OUT/FAN-IN with redundant execution (3+ agents same task)
│ ├── YES + Speed matters more than quality
│ │ └── Use FAN-OUT/FAN-IN with partitioned execution (divide work)
│ └── NO + Task requires sequential dependencies
│ └── Use SUPERVISOR-WORKER with delegation chains
│
├── Multiple valid approaches exist?
│ ├── YES + Verifiable ground truth exists
│ │ └── Use DEBATE (adversarial refinement with judge)
│ ├── YES + Subjective preference decision
│ │ └── Use VOTING/CONSENSUS (democratic selection)
│ └── NO + Single approach but needs refinement
│ └── Use CRITIQUE-REFINE (iterative improvement)
│
├── Knowledge synthesis from diverse sources?
│ └── Use BLACKBOARD (shared state accumulation)
│
└── Simple capability delegation?
└── Use REQUEST/RESPONSE (synchronous handoff)
Topology × Initiative × Turn Order Decision Matrix
Topology
Initiative
Turn Order
Use When
Example
Star
Push
Round-robin
Clear leader coordinates work
CrewAI hierarchical process
Star
Pull
Priority-queue
Workers request tasks when ready
AutoGen GroupChat with manager
Mesh
Push
Free-form
Peer collaboration, no bottlenecks
Multi-agent debate
Tree
Push
Depth-first
Hierarchical decomposition
Complex delegation chains
Broadcast
Reactive
Event-driven
Knowledge sharing, updates
LangGraph state updates
Termination Condition Selection
If conversation type is:
├── DEBATE → Stop when judge_confidence > 0.8 OR rounds >= 3
├── CRITIQUE → Stop when verdict == 'approve' OR iterations >= 4
├── VOTING → Stop when all votes collected OR timeout
├── FAN-OUT → Stop when gather_policy satisfied (all/majority/first)
├── SUPERVISOR → Stop when all subtasks complete OR budget exceeded
└── BLACKBOARD → Stop when goal_condition met OR staleness detected
FAILURE MODES
1. Delegation Ping-Pong
Detection: Agent A delegates to B, B delegates back to A, creating infinite loops
Symptoms: Exponentially growing message counts, same tasks repeated endlessly
Root Cause: No cycle detection in delegation chains, workers can delegate upward
Fix: Implement delegation constraints with chain tracking and upward delegation blocks
2. Sycophancy Collapse
Detection: In debates, all agents converge to same position by round 2 regardless of evidence
Symptoms: No position changes after initial round, unanimous agreement on complex topics
Root Cause: Agents optimize for agreement rather than truth-seeking
Fix: Assign explicit adversarial roles, require agents to defend assigned perspectives
3. Supervisor Bottleneck
Detection: All coordination flows through single supervisor, high latency on parallel tasks
Symptoms: Workers idle waiting for supervisor responses, linear scaling on parallelizable work
Root Cause: Supervisor acts as message router instead of synthesizer
Fix: Restructure as fan-out/fan-in or enable direct worker-to-worker communication
4. Blackboard State Explosion
Detection: Shared state grows unbounded, agents waste tokens reading irrelevant entries
Symptoms: Query response times increasing over time, high token usage on reads
Root Cause: No garbage collection or relevance filtering on blackboard entries
Fix: Implement confidence-based expiration and semantic filtering on reads
5. Context Degradation Cascade
Detection: Deep delegation chains (>3 levels) lose essential context at each hop
Symptoms: Workers ask clarifying questions, output quality decreases with chain depth
Root Cause: Context compression artifacts compound across delegation hops
Fix: Flatten hierarchy to max 2 levels or pass full context to all workers
Pure CRITIQUE chain: Too slow (sequential reviews)
Pure DEBATE: Security issues get debated away by majority
Pure FAN-OUT: No blocking for security failures
Hybrid: Security first, then parallel, then resolve conflicts
Example 2: Research Paper Writing
Problem: 3 agents (researcher, writer, fact-checker) produce literature review
Decision Process:
Initiative Type: Pull-based (agents work when ready) vs Push-based (coordinator assigns)
Quality vs Speed: Quality critical → redundancy needed
Decomposition: Topic can be partitioned by research area
Chosen Protocol:
researcher: Partitioned FAN-OUT across research areas
fact-checker: CRITIQUE-REFINE on each section
writer: SUPERVISOR role synthesizing all inputs
Why not alternatives:
All agents in single DEBATE: No clear roles, writer expertise wasted on fact-checking
Sequential REQUEST/RESPONSE chain: Too slow, no parallel research
Pure BLACKBOARD: No synthesis, just knowledge accumulation
Termination Logic:
Stop when:
- All research areas covered (completeness check)
- Fact-checker confidence > 0.85 on all sections
- Writer produces coherent synthesis
- Total tokens < budget OR time < deadline
Example 3: Dynamic Routing Decision
Problem: During execution, supervisor realizes 3 workers are overwhelmed, 1 worker is idle
Real-time Decision Tree:
Current state: 3 workers at 90% capacity, 1 worker at 10%
Options:
1. Rebalance work (migrate tasks to idle worker)
2. Add redundancy (parallel execution on critical path)
3. Change topology (switch from star to mesh for peer delegation)
Decision factors:
├── Time remaining? < 25% → Option 2 (parallel, accept higher cost)
├── Budget remaining? < 50% → Option 1 (rebalance, optimize cost)
└── Task dependencies? High coupling → Option 3 (mesh topology)
Execution: Supervisor detects state, broadcasts topology change message, workers update their delegation rules, work continues with new pattern
QUALITY GATES
Termination Policy Defined: Every conversation has explicit max messages, time, and cost limits
Cycle Detection Active: Delegation chains track agent history and prevent A→B→A loops
Confidence Scores Present: All outputs include agent confidence (0.0-1.0) for quality assessment
