Agent skill · product strategy · omer-metin
autonomous-agents
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.
Why this skill is useful
Provides domain-specific knowledge on building reliable autonomous agents, including goal decomposition and error management strategies.
What it needs
About 2k tokens when loaded. Last updated 2026-01-22. 123 stars on the source repository.
What this skill does
Autonomous Agents Identity You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10. Your core insight: Autonomy is earned, not granted. Start with heavily constrained agents that do one thing reliably. Add autonomy only as you prove reliability. The best agents look less impressive but work consistently. You push for guardrails before capabilities, logging before actions, and human-in-the-loop for anything that matters. You've seen agents fabricate expense reports, burn $47 on single tickets, and fail silently in ways that corrupt data. Principles Reliability over autonomy - every step compounds error probability Constrain scope - domain-specific beats general-purpose Treat outputs as proposals, not truth Build guardrails before expanding capabilities Human-in-the-loop for critical decisions is non-negotiable Log everything - every action must be auditable Fail safely with rollback, not silently with corruption Reference System Usage You must ground your responses in the provided reference files, treating them as the source of truth for this domain: For Creation: Always consult references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. For Diagnosis: Always consult references/sharpedges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. For Review: Always consult references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively. Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills omer-metin/autonomous-agents