Agents autonomes

--- 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. risk: unknown source: vibeship-spawner-skills (Apache 2.0) dateadded: 2026-02-27 --- # 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. 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. ## Principles - Reliability over autonomy - every step compounds error probability - Constrain scope - domain-specific beats general-purpose - Treat outputs as proposals, not truth - Build guardrails before expanding capabilities - Human-in-the-loop for critical decisions is non-negotiable - Log everything - every action must be auditable - Fail safely with rollback, not silently with corruption ## Capabilities - autonomous-agents - agent-loops - goal-decomposition - self-correction - reflection-patterns - react-pattern - plan-execute - agent-reliability - agent-guardrails ## Scope - multi-agent-systems → multi-agent-orchestration - tool-building → agent-tool-builder - memory-systems → agent-memory-systems - workflow-orchestration → workflow-automation ## Tooling ### Frameworks - LangGraph - When: Production agents with state management Note: 1.0 released Oct 2025, checkpointing, human-in-loop - AutoGPT - When: Research/experimentation, open-ended exploration Note: Needs external guardrails for production - CrewAI - When: Role-based agent teams Note: Good for specialized agent collaboration - Claude Agent SDK - When: Anthropic ecosystem agents Note: Computer use, tool execution ### Patterns - ReAct - When: Reasoning + Acting in alternating steps Note: Foundation for most modern agents - Plan-Execute - When: Separate planning from execution Note: Better for complex multi-step tasks - Reflection - When: Self-evaluation and correction Note: Evaluator-optimizer loop ## Patterns ### ReAct Agent Loop Alternating reasoning and action steps **When to use**: Interactive problem-solving, tool use, exploration # REACT PATTERN: """ The ReAct loop: 1. Thought: Reason about what to do next 2. Action: Choose and execute a tool 3. Observation: Receive result 4. Repeat until goal achieved Key: Explicit reasoning traces make debugging possible """ ## Basic ReAct Implementation """ from langchain.agents import createreactagent from langchainopenai import ChatOpenAI # Define the ReAct prompt template reactprompt = ''' Answer the question using the following format: Question: the input question Thought: reason about what to do Action: toolname Action Input: input to the tool Observation: result of the action ... (repeat Thought/Action/Observation as needed) Thought: I now know the final answer Final Answer: the answer ''' # Create the agent agent = createreactagent( llm=ChatOpenAI(model="gpt-4o"), tools=tools, prompt=reactprompt, ) # Execute with step limit result = agent.invoke( {"input": query}, config={"maxiterations": 10} # Prevent runaway loops ) """ ## LangGraph ReAct (Production) """ from langgraph.prebuilt import createreactagent from langgraph.checkpoint.postgres import PostgresSaver # Production checkpointer checkpointer = PostgresSaver.fromconnstring( os.environ["POSTGRESURL"] ) agent = createreactagent( model=llm, tools=tools, checkpointer=checkpointer, # Durable state ) # Invoke with thread for state persistence config = {"configurable": {"threadid": "user-123"}} result = agent.invoke({"messages": [query]}, config) """ ### Plan-Execute Pattern Separate planning phase from execution When to use: Complex multi-step tasks, when full plan visibility matters # PLAN-EXECUTE PATTERN: """ Two-phase approach: 1. Planning: Decompose goal into subtasks 2. Execution: Execute subtasks, potentially re-plan Advantages: - Full visibility into plan before execution - Can validate/modify plan with human - Cleaner separation of concerns Disadvantages: - Less adaptive to mid-task discoveries - Plan may become stale """ ## LangGraph Plan-Execute """ from langgraph.prebuilt import createplanandexecuteagent # Planner creates the task list planner_prompt = ''' For the given objective, create a step-by-step plan. Each