Autonomous agents
Skill payals/ai-skills-engine/dot_cursor/skills/ai-research/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% bFrom its SKILL.md
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SKILL.md
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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
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most task breakdown skills give in 372 tokens
Counted across 221 of the 230 authors here whose files we hold, read 2026-09-06
- Write acceptance criteria for every taskin 32 of 221, across 30 files
- Add checkpoints every two to three tasksin 18 of 221, across 16 files
- Schedule high-risk tasks earlyin 16 of 221, across 14 files
- Slice work vertically into complete feature pathsin 16 of 221, across 14 files
- Include verification steps in every taskin 15 of 221, across 13 files
- Map dependencies between componentsin 14 of 221, across 12 files
- Get human approval before implementingin 14 of 221, across 12 files
- Enter read-only plan mode before writing codein 10 of 221
- Read the spec and codebase before planningin 9 of 221, across 7 files
- Map the dependency graph before ordering tasksin 8 of 221
- Break any task touching more than five filesin 8 of 221
- List each task's dependenciesin 7 of 221
Said here and by no other author read
- Start with heavily constrained single-purpose agents
- Add autonomy only after proving reliability
- Add guardrails before adding capabilities
- Reduce step count
- Set hard cost limits
- Test at scale before production
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.