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
Overall
score
37%
Does it follow best practices?
Validation for skill structure
Install with Tessl CLI
npx tessl i github:sickn33/antigravity-awesome-skills --skill autonomous-agentsYou 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
Alternating reasoning and action steps
Separate planning phase from execution
Self-evaluation and iterative improvement
| 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 |
Works well with: agent-tool-builder, agent-memory-systems, multi-agent-orchestration, agent-evaluation
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.