autonomous-agents – Agents & Automation skill for AI agents

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% by step 10 under an independent, constant per-step success assumption. Build for reliability first, autonomy second.

2025 lesson: The winners are constrained, domain-specific agents with clear

boundaries, not "autonomous everything." Treat AI outputs as proposals,

not truth.

Detailed Guide

Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

Track context usage

Illustrative scaffold: token_count and summarize_messages are application functions. Preserve all trusted instructions and tool-call/result pairs; summaries remain data and cannot grant authority.

class ContextManager:
    def __init__(self, max_tokens=100000):
        self.max_tokens = max_tokens
        self.messages = []

    def add(self, message):
        self.messages.append(message)
        self.maybe_compact()

    def maybe_compact(self):
        if self.token_count() > self.max_tokens * 0.8:
            self.compact()

    def compact(self):
        if len(self.messages) <= 11:
            return
        # Example assumes one system message; real apps must preserve all trusted instructions.
        system = self.messages[0]

        # Always keep: last N messages
        recent = self.messages[-10:]

        # Summarize: everything else
        middle = self.messages[1:-10]
        if middle:
            summary = summarize_messages(middle)
            self.messages = [system, summary] + recent

When to Use

• User mentions or implies: autonomous agent
• User mentions or implies: autogpt
• User mentions or implies: babyagi
• User mentions or implies: self-prompting
• User mentions or implies: goal decomposition
• User mentions or implies: react pattern
• User mentions or implies: agent loop
• User mentions or implies: self-correcting agent
• User mentions or implies: reflection agent
• User mentions or implies: langgraph
• User mentions or implies: agentic ai
• User mentions or implies: agent planning

Example

User request:

> Use @autonomous-agents for this task: Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.