ai-agents

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AI agent architecture, graph and loop composition, protocol choice, evaluation, and observability. Use when scoping or reviewing systems before implementation.

AI & Automation 87 stars 19 forks Updated 1 weeks ago MIT

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Skill Content

# AI Agents Development — Architecture Hub Use this skill to decide whether a workflow should be an agent, which agent shape fits, which protocol boundary to use, and what production controls must exist before rollout. Default posture: explicit control flow, bounded tools, typed contracts, auditable state, human approval for high-risk actions, and telemetry from day one. Keep this file lean. Load detail from [`references/index.md`](references/index.md), `assets/`, and sibling skills only when needed. ## When to Use This Skill Use this skill when the user asks for: - agent architecture or operating-model decisions - build-vs-not-agent assessment - MCP vs A2A protocol choice - production readiness review for an existing agent system - evaluation, observability, rollout, or safety planning - framework selection after requirements are already clear - a starting template for a new agent spec - graph engineering, agent/workflow graphs, state-machine orchestration, cyclic graphs, or DAG-versus-loop design - loop engineering, run-until-done coding agents, self-improving workflows, evaluator feedback loops, or bounded autonomous iteration - uncertainty over whether a "graph" means execution control flow, an improvement network, or a knowledge/context graph ## Use Other Skills for Depth - Prompt contracts and structured outputs → [`../ai-prompt-engineering/SKILL.md`](../ai-prompt-engineering/SKILL.md) - Retrieval, chunking, reranking, search quality → [`../ai-rag/SKILL.md`](../...

Details

Author
vasilyu1983
Repository
vasilyu1983/AI-Agents-public
Created
10 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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