autonomous-agents

Installation
Summary

Architectural patterns and guardrails for building reliable autonomous agents that start constrained and earn autonomy through proven reliability.

  • Covers three core agent loop patterns: ReAct (alternating reasoning and action), Plan-Execute (separated planning and execution phases), and Reflection (self-evaluation and iterative improvement)
  • Emphasizes guardrails-first approach with hard cost limits, step count reduction, and least-privilege API access to prevent runaway behavior
  • Identifies critical failure modes including unbounded autonomy, blind trust in agent outputs, and premature general-purpose design
  • Includes production readiness checklist: ground truth validation, robust API clients, structured logging, and context usage tracking
SKILL.md

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.

Principles

  • Reliability over autonomy - every step compounds error probability
Related skills
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GitHub Stars
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First Seen
Jan 19, 2026