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 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.