Agent Orchestration Architecture
SkillMediaDesign reliable AI-agent and multi-agent systems. Use when deciding whether a workflow needs one agent, tools, specialist agents, manager control, handoffs, durable execution, human approval, model routing, retries, or an operating boundary for an AI workflow.
Available today. Use it from your connected AI after setup.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Agent Orchestration Architecture skill
What this skill tells your AI
The instructions your AI receives, as published by thegoat395/codex-skills in skills/agent-orchestration-architecture/SKILL.md and read by ahel’s review.
Design in escalating complexity
Choose the lightest design that satisfies the outcome:
- Deterministic program or promptless automation.
- One agent with bounded tools and structured output.
- One manager agent that calls specialists as bounded tools.
- Handoffs only when a specialist should own the rest of the interaction.
- Durable workflow runtime only when work survives interruptions, waits, or external callbacks.
Do not use an agent swarm as a substitute for an owned workflow.
Define the operating contract
Before implementation, record:
- outcome, success evidence, and named final-output owner;
- inputs, output schema, permitted tools, and forbidden actions;
- model and reasoning choice by decision difficulty, not task size;
- state owner, memory lifetime, tenant boundary, and source of truth;
- approval checkpoints, budgets, timeouts, stop conditions, and escalation;
- retry, idempotency, compensation, and human-handoff behavior.
Parallelize only independent work whose outputs can be reconciled without conflicting writes.
Make control explicit
- Use a manager when one agent must enforce shared policy, combine specialist work, or own the user-facing answer.
- Use a handoff when the specialist needs a focused interaction and clear transfer of responsibility.
- Pass structured task packets, not vague conversation history. Minimize context to the specialist's need.
- Delegate only a concrete, independent workstream with a defined output. Parallelism can reduce latency, but every subagent performs separate model and tool work and therefore increases usage.
- Designing a delegation architecture does not authorize spawning agents. Follow current global and native tool constraints: preserve the selected parent model/effort and apply the current global child-model and reasoning policy. Do not freeze a session-specific model or effort into this specialist. Use only supported context controls and sufficient task context; do not override native inheritance constraints for a preferred packet format.
- Give each specialist the exact sources, constraints, output schema, stopping condition, and useful result-size limit. Ask for distilled findings instead of raw logs or copied source material.
- Keep the parent thread as the canonical decision owner. Do not spawn several agents to reread the same corpus, and do not use subagents merely to avoid doing a bounded task locally.
- Use inherited authorization for routine reversible in-scope writes, drafts and configuration. Require explicit authorization for actual sends, spending, binding commitments, publication and materially irreversible actions at their real boundaries; do not reopen an already clear authorization.
- Design every loop with a maximum attempt count and a useful terminal state.
Required outputs
For a material architecture request, produce the smallest useful combination of an execution diagram, role/tool matrix, state/approval map, operating limits, and evaluation plan. Do not create every artifact for a simple routing decision. Route persistent-context design to $agent-memory-provenance; route quality and release testing to $agent-evaluation-operations; route external events to $integration-contract-reliability.
Optional specialists
For external events, integration-contract-reliability is optional. Without it, define typed inputs/outputs, authentication, idempotency, retries, duplicate handling, uncertain-effect reconciliation and the actual success boundary in the project contract.
Signals
- GitHub stars
- 124
- Forks
- 48
- Last commit
- Sep 2026
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agent-orchestration-architecture- Source
- github.com/thegoat395/codex-skills