autonomous-agents

# 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 - 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 ## Capabilities - autonomous-agents - agent-loops - goal-decomposition - self-correction - reflection-patterns - react-pattern - plan-execute - agent-reliability - agent-guardrails ## Scope - multi-agent-systems → multi-agent-orchestration - tool-building → agent-tool-builder - memory-systems → agent-memory-systems - workflow-orchestration → workflow-auto...