home / skills / sickn33 / antigravity-awesome-skills / autonomous-agents

autonomous-agents skill

safe

This skill helps you design reliable autonomous agents by enforcing guardrails, structured loops, and reflection to reduce error propagation.

This is most likely a fork of the autonomous-agents skill from xfstudio

npx playbooks add skill sickn33/antigravity-awesome-skills --skill autonomous-agents

Review the files below or copy the command above to add this skill to your agents.

Files (1)

SKILL.md

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---
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`

Overview

This skill teaches practical design and hardening techniques for autonomous agents so they work reliably in production. It emphasizes constrained autonomy, reducing step counts, and adding guardrails before capabilities. The focus is on repeatable patterns that minimize compounding error rates.

How this skill works

It analyzes agent loops and recommends structural patterns like ReAct and Plan-Execute to separate reasoning from action. It prescribes goal decomposition, reflection cycles, and self-correction checkpoints to detect and recover from failures. It also provides operational safeguards: cost limits, least-privilege access, structured logging, and validation against ground truth.

When to use it

Building a new autonomous agent that must run unattended
Converting a prototype agent into a production-grade service
Designing workflows where compounding step errors are a risk
Auditing agent reliability, security, or cost exposure
Integrating agents with critical APIs or data systems

Best practices

Start with narrow, well-defined tasks and expand autonomy gradually
Limit decision steps; fewer steps reduce compound failure probability
Separate planning from execution and validate plans before action
Implement reflection/self-checks after each action to detect drift
Enforce guardrails: rate, cost, and permission limits
Log structured events and ground-truth key outputs for audits

Example use cases

A code-generation agent that plans edits, simulates outcomes, and applies only validated patches
Automated incident triage that decomposes goals, runs safe probes, and escalates on anomalies
Data extraction pipeline that validates outputs against schema and retries with adjusted prompts
Autonomous deployment agent that enforces least-privilege credentials and hard cost caps
Multi-step automation where each step includes a self-check to avoid cascading failures

FAQ

How do I measure compound failure risk?

Estimate per-step success probability and compute cumulative success across the expected step count. Reduce steps or improve per-step reliability until overall success meets your target.

When should I allow more autonomy?

Only expand autonomy after consistent success in production-like tests, with monitoring, rollback paths, and hardened guardrails in place.