Individual skill

agent-orchestration

Designs reliable multi-agent LLM systems - choosing a topology, writing handoff contracts between agents, deciding what runs in parallel vs series, and setting context, retry, and cost budgets that stop runaway loops. Use when someone asks "should I split this into multiple agents", "my agents keep looping", "how do I pass context between agents", "orchestrator vs pipeline vs router", or is architecting an agent workflow. Do NOT use for tuning a single prompt - use prompt-engineer instead; do NOT use for measuring agent output quality - use llm-evaluation instead; for making a product agent-operable end to end, use build-on-agent-native.

skills/agent-orchestration/SKILL.md

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Repository last updated: Oct 13, 2026
License: MIT

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# Agent Orchestration

Multi-agent systems fail two ways: teams split work across agents that a single capable agent handles better (paying latency, cost, and failure surface for nothing), or they wire agents together with vague handoffs and no budgets, producing loops that burn tokens until someone notices the bill. This skill designs the coordination layer - topology, contracts, budgets - so agent count is justified and every failure mode has a pre-decided response.

## Operating procedure

### Step 1: Gather inputs

- The end-to-end task and the acceptance criteria for its final output.
- Why one agent is insufficient - name the specific failure (context overflow, skill mismatch, needed parallelism). If the user cannot name one, the answer is one agent, and this design is over.
- Volume of context the task consumes vs the model's window; latency and cost ceilings per run.
- Which steps have side effects (writes, sends, purchases) vs are pure reads.

### Step 2: Justify each agent