Design multi-agent orchestration with workflow DAGs, routing, handoff protocols, and state management. Use when building pipelines of specialized agents, designing fan-out/fan-in patterns, or implementing fault-tolerant workflows.
Instalación
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Design multi-agent orchestration with workflow DAGs, routing, handoff protocols, and state management. Use when building pipelines of specialized agents, designing fan-out/fan-in patterns, or implementing fault-tolerant workflows.
The agent designs multi-agent orchestration systems using five core patterns: sequential pipeline, parallel fan-out/fan-in, hierarchical delegation, event-driven reactor, and consensus validation. It implements agent routing strategies, circuit breaker reliability patterns, context window budgeting, and cost optimization across LangGraph, CrewAI, AutoGen, and Claude Code agent teams.
Reliability engineering — circuit breakers, retry with backoff, dead letter queues, timeout enforcement, idempotency
When to Use
Building multi-step AI pipelines that exceed one agent's capability
Parallelizing research, analysis, or generation tasks
Creating specialist agent teams with defined roles and contracts
Designing fault-tolerant AI workflows for production deployment
Optimizing cost across workflows with mixed model tiers
Clarify First
Before designing the workflow, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Workflow topology — linear, parallel, tree/delegation, reactive, or consensus (selects which of the five orchestration patterns)
Framework target — LangGraph, CrewAI, AutoGen, or Claude agent teams (determines the implementation code emitted)
Reliability & cost constraints — failure tolerance and budget (drives circuit breakers, retries, timeouts, and model-tier routing)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Pattern Selection Decision Tree
What does the workflow look like?
│
├─ Linear: step A feeds step B feeds step C
│ └─ SEQUENTIAL PIPELINE
│ Best for: content pipelines, code review chains, data transformation
│
├─ Parallel: N independent tasks, then combine
│ └─ FAN-OUT / FAN-IN
│ Best for: competitive research, multi-source analysis, parallel code gen
│
├─ Tree: orchestrator breaks work into subtasks dynamically
│ └─ HIERARCHICAL DELEGATION
│ Best for: complex projects, open-ended research, code generation with planning
│
├─ Reactive: agents respond to events/triggers
│ └─ EVENT-DRIVEN REACTOR
│ Best for: monitoring, alerting, continuous integration, chat workflows
│
└─ Verification: multiple agents must agree on output
└─ CONSENSUS VALIDATION
Best for: high-stakes decisions, code review, fact checking, safety-critical output
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
references/orchestration-patterns.md — full implementations of all five patterns (LangGraph sequential pipeline, async fan-out/fan-in, hierarchical orchestrator with dependency batching, event bus, consensus validation). Read after picking a topology from the decision tree.
references/routing-and-cost.md — intent-based router, context window budgeting (ContextBudget), and the cost optimization matrix. Read when deciding how requests reach agents and how to control spend.
references/reliability-and-troubleshooting.md — circuit breaker, common pitfalls, best practices, troubleshooting table, and success criteria. Read when hardening for production or diagnosing failures.
references/subagent-scoping-and-orchestration.md — when to split work into scoped subagents vs one loop; scoping a subagent (minimal tool allow-list, focused instructions, isolated context, return contract); the lead→parallel-specialists→merge pattern on a shared workspace; failure isolation/retries; and multi-agent vs single-agent cost/latency tradeoffs. Read when designing a lead that delegates to specialist subagents.
Tools Overview
Stdlib-only Python CLIs in scripts/ (run with python3, support --json and human-readable output):
cost_estimator.py — per-step token/cost estimate for a workflow DAG with model-tier what-ifs.
multi_agent_cost_estimator.py — compares a lead + scoped subagents design (per-role price tier, call counts, token sizes, reasoning-effort multiplier) against a single strong agent baseline, with a per-role breakdown and total-cost projection. Prices are user-supplied with neutral placeholder defaults — pass --price tier=input/output or a JSON price_tiers block with your real rates.
workflow_validator.py / workflow_visualizer.py — validate and render workflow DAGs.
Common Patterns
Scoped subagents — split a job into specialists only where responsibilities are genuinely independent; give each a minimal tool allow-list, one-job instructions, an isolated context, and a small return contract, then merge their contracts in the lead on a shared workspace (see references/subagent-scoping-and-orchestration.md).
Multi-model routing — run the orchestrator on a stronger tier and narrow subagents on cheaper tiers, matching reasoning effort to each role; estimate both topologies with scripts/multi_agent_cost_estimator.py before committing, and keep the single loop if the multi-agent design isn't meaningfully cheaper or faster.
Scope & Limitations
This skill covers:
Design and implementation of five core multi-agent orchestration patterns (sequential, parallel, hierarchical, event-driven, consensus)
Agent routing strategies including intent-based, skill-based, and cost-aware routing
Reliability engineering patterns: circuit breakers, retries, timeouts, and dead letter queues
Training or fine-tuning the underlying LLMs used by agents (see engineering/ml-pipeline-architect for ML training workflows)
Infrastructure provisioning, container orchestration, or deployment pipelines (see engineering/cloud-infrastructure-designer for cloud architecture)
Human-in-the-loop approval workflows or UI design for agent dashboards (see product-team/ux-researcher for user-facing workflow design)
Long-term agent memory, vector database setup, or RAG pipeline construction (see engineering/rag-pipeline-architect for retrieval-augmented generation)
Integration Points
Skill
Integration
Data Flow
engineering/ml-pipeline-architect
Agent workflows that include ML inference stages use ML Pipeline Architect for model serving and batch prediction design
Workflow DAG exports stage specs to ML pipeline; ML pipeline returns inference endpoints for agent consumption
engineering/rag-pipeline-architect
Research and retrieval agents within workflows rely on RAG pipelines for grounded knowledge access
Agent sends queries to RAG pipeline; RAG returns ranked document chunks with citations for agent context
engineering/cloud-infrastructure-designer
Production deployment of agent workflows requires infrastructure design for scaling, queuing, and monitoring
Workflow resource requirements feed into infrastructure specs; infra returns endpoint URLs, queue ARNs, and scaling policies
engineering/api-design-architect
Inter-agent communication contracts and external API boundaries follow API design standards
Agent handoff schemas are validated against API design specs; API architect provides OpenAPI definitions for external integrations
engineering/system-design-architect
Overall system architecture decisions (sync vs async, monolith vs distributed) shape workflow topology choices
System design constraints (latency budgets, availability targets) inform pattern selection; workflow requirements feed back into system capacity planning
project-management/technical-project-planning
Complex multi-agent projects require structured planning for phased rollout, risk management, and milestone tracking
Workflow complexity estimates feed into project plans; PM skill provides sprint boundaries and dependency timelines for staged deployment
