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💬
Conversational Orchestration
Multi-agent coordination through structured conversation patterns
Complexity: highPattern
Core Mechanism
Multi-agent coordination through structured, stateful conversation. A supervisor/router selects which agent speaks or acts next based on intent, context, and policy; agents exchange messages, call tools, and update shared conversation state. The orchestrator maintains memory, enforces guardrails, and terminates when success criteria are met, yielding a summarized, traceable outcome.
Workflow / Steps
- Intake: normalize goal, constraints, user profile, and initial context/memory.
- Initialize roles: register agents/tools; define capabilities and policies.
- Turn loop: route → agent speaks/acts → tools/retrieval → observe → update shared state.
- Safety: apply input/output filters, validations, and auth scopes each turn.
- Stopping: detect completion/impasse via heuristics or explicit success predicates.
- Summarize: distill final answer, provenance, and next-step recommendations.
- Learn: log traces, evals, costs; refine routing and prompts based on outcomes.
Best Practices
Use an explicit router/supervisor with typed state; keep steps idempotent.
Prefer small, fast models for routing/critique; reserve large models for hard reasoning.
Bound loops: max turns, per-turn token caps, wall-clock timeouts, and cancellation.
Maintain conversation memory with summarization and schema-based context packing.
Instrument traces, costs, latencies, tool I/O, and safety events; add regression evals.
Apply least-privilege credentials; validate tool outputs; implement circuit breakers.
Design clear role prompts and message schemas to avoid ambiguity and ping-pong loops.
When NOT to Use
- Simple, single-step tasks where a single agent/tool suffices within SLOs.
- Strict real-time paths with tight p95 latency budgets sensitive to turn-taking overhead.
- High-risk actions without human review, auditing, or robust policy enforcement.
- Teams lacking observability, evals, and operations maturity to monitor multi-agent flows.
Common Pitfalls
- Agent ping-pong and recursion causing cost/token blowups.
- Context drift and stale/shared state inconsistencies across turns.
- Unvalidated tool effects, missing idempotency, or duplicate writes on retries.
- Privilege creep: broad API scopes or long-lived secrets for many agents.
Key Features
Supervisor/router with policy- and capability-aware routing
Role registry and message schema for structured conversations
Stateful memory with summarization and retrieval integration
Tool/function calling with validation and guardrails
Parallel subthreads with arbitration/consensus when safe
Graph-based control (e.g., state graphs) for complex flows
Human-in-the-loop handoff and escalation paths
Full observability: traces, metrics, audits
KPIs / Success Metrics
- Task resolution rate; handoff success; judge/arbiter agreement when used.
- Turns to resolution; p50/p95 end-to-end latency; stuck/aborted session rate.
- Cost/tokens per successful resolution; router accuracy vs ground truth.
- Escalation-to-human rate; safety intervention frequency; MTTR by failure type.
Token / Resource Usage
- Cap turns and per-turn tokens; compress context; cache conversation summaries.
- Use small models for routing/classification; batch safe tool calls; parallelize where possible.
- Apply backpressure/queues; per-edge timeouts and circuit breakers; stream partial outputs.
- TTL for memory entries; checkpoint state for recovery; avoid unbounded fan-out.
Best Use Cases
- Customer support triage with tool use, retrieval, and structured escalation.
- Research, analysis, and drafting using expert debate/judge patterns.
- Incident response and operations runbooks with multi-role collaboration.
- Sales discovery and solutioning with role-based assistants and guardrails.
References & Further Reading
Academic Papers
Implementation Guides
Community & Discussions
💬
Conversational Orchestration
Multi-agent coordination through structured conversation patterns
Complexity: highPattern
Core Mechanism
Multi-agent coordination through structured, stateful conversation. A supervisor/router selects which agent speaks or acts next based on intent, context, and policy; agents exchange messages, call tools, and update shared conversation state. The orchestrator maintains memory, enforces guardrails, and terminates when success criteria are met, yielding a summarized, traceable outcome.
Workflow / Steps
- Intake: normalize goal, constraints, user profile, and initial context/memory.
- Initialize roles: register agents/tools; define capabilities and policies.
- Turn loop: route → agent speaks/acts → tools/retrieval → observe → update shared state.
- Safety: apply input/output filters, validations, and auth scopes each turn.
- Stopping: detect completion/impasse via heuristics or explicit success predicates.
- Summarize: distill final answer, provenance, and next-step recommendations.
- Learn: log traces, evals, costs; refine routing and prompts based on outcomes.
Best Practices
Use an explicit router/supervisor with typed state; keep steps idempotent.
Prefer small, fast models for routing/critique; reserve large models for hard reasoning.
Bound loops: max turns, per-turn token caps, wall-clock timeouts, and cancellation.
Maintain conversation memory with summarization and schema-based context packing.
Instrument traces, costs, latencies, tool I/O, and safety events; add regression evals.
Apply least-privilege credentials; validate tool outputs; implement circuit breakers.
Design clear role prompts and message schemas to avoid ambiguity and ping-pong loops.
When NOT to Use
- Simple, single-step tasks where a single agent/tool suffices within SLOs.
- Strict real-time paths with tight p95 latency budgets sensitive to turn-taking overhead.
- High-risk actions without human review, auditing, or robust policy enforcement.
- Teams lacking observability, evals, and operations maturity to monitor multi-agent flows.
Common Pitfalls
- Agent ping-pong and recursion causing cost/token blowups.
- Context drift and stale/shared state inconsistencies across turns.
- Unvalidated tool effects, missing idempotency, or duplicate writes on retries.
- Privilege creep: broad API scopes or long-lived secrets for many agents.
Key Features
Supervisor/router with policy- and capability-aware routing
Role registry and message schema for structured conversations
Stateful memory with summarization and retrieval integration
Tool/function calling with validation and guardrails
Parallel subthreads with arbitration/consensus when safe
Graph-based control (e.g., state graphs) for complex flows
Human-in-the-loop handoff and escalation paths
Full observability: traces, metrics, audits
KPIs / Success Metrics
- Task resolution rate; handoff success; judge/arbiter agreement when used.
- Turns to resolution; p50/p95 end-to-end latency; stuck/aborted session rate.
- Cost/tokens per successful resolution; router accuracy vs ground truth.
- Escalation-to-human rate; safety intervention frequency; MTTR by failure type.
Token / Resource Usage
- Cap turns and per-turn tokens; compress context; cache conversation summaries.
- Use small models for routing/classification; batch safe tool calls; parallelize where possible.
- Apply backpressure/queues; per-edge timeouts and circuit breakers; stream partial outputs.
- TTL for memory entries; checkpoint state for recovery; avoid unbounded fan-out.
Best Use Cases
- Customer support triage with tool use, retrieval, and structured escalation.
- Research, analysis, and drafting using expert debate/judge patterns.
- Incident response and operations runbooks with multi-role collaboration.
- Sales discovery and solutioning with role-based assistants and guardrails.