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% b
40
Quality
27%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agent/skills/autonomous-agents/SKILL.mdAutonomous 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
🧠 Knowledge Modules (Fractal Skills)
1. ReAct Agent Loop
2. Plan-Execute Pattern
3. Reflection Pattern
4. ❌ Unbounded Autonomy
5. ❌ Trusting Agent Outputs
6. ❌ General-Purpose Autonomy
- Repository
- Dokhacgiakhoa/antigravity-ide
- Commit
3395991
- Last updated
- Created
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.