Autonomous Agents
When & Why to Use This Skill
This Claude skill provides a comprehensive architectural framework for building reliable autonomous agents. It focuses on bridging the gap between experimental demos and production-ready AI systems by implementing proven agent loops such as ReAct and Plan-Execute. The skill emphasizes the importance of goal decomposition, self-correction, and the implementation of strict guardrails to prevent the compounding error rates that often lead to failure in long-running autonomous tasks.
Use Cases
- Case 1: Designing complex AI workflows that require independent goal decomposition and multi-step execution using the Plan-Execute pattern.
- Case 2: Implementing the ReAct (Reasoning + Acting) loop to enable agents to interact with external tools while maintaining a transparent and logical decision-making process.
- Case 3: Enhancing system reliability by integrating reflection patterns and self-evaluation mechanisms to catch and correct errors before they escalate.
- Case 4: Establishing operational guardrails and cost limits for autonomous systems to ensure safe, predictable, and cost-effective performance in production environments.
| name | autonomous-agents |
|---|---|
| description | "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% b" |
| source | vibeship-spawner-skills (Apache 2.0) |
Autonomous Agents
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 befor
Capabilities
- autonomous-agents
- agent-loops
- goal-decomposition
- self-correction
- reflection-patterns
- react-pattern
- plan-execute
- agent-reliability
- agent-guardrails
Patterns
ReAct Agent Loop
Alternating reasoning and action steps
Plan-Execute Pattern
Separate planning phase from execution
Reflection Pattern
Self-evaluation and iterative improvement
Anti-Patterns
❌ Unbounded Autonomy
❌ Trusting Agent Outputs
❌ General-Purpose Autonomy
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | ## Reduce step count |
| Issue | critical | ## Set hard cost limits |
| Issue | critical | ## Test at scale before production |
| Issue | high | ## Validate against ground truth |
| Issue | high | ## Build robust API clients |
| Issue | high | ## Least privilege principle |
| Issue | medium | ## Track context usage |
| Issue | medium | ## Structured logging |
Related Skills
Works well with: agent-tool-builder, agent-memory-systems, multi-agent-orchestration, agent-evaluation