ai-agent-design

# AI Agent Design Skill > Patterns for designing AI agents—autonomous systems that use LLMs to reason, plan, and execute multi-step tasks. ## Agent vs Chatbot vs Workflow | Aspect | Chatbot | Workflow | Agent | |--------|---------|----------|-------| | Autonomy | Low | None | High | | Planning | None | Predefined | Dynamic | | Tool Use | Limited | Fixed | Flexible | | Memory | Session | None | Persistent | | Error Recovery | Retry | Fail | Reason & adapt | ## Core Patterns ### ReAct (Reasoning + Acting) ```text 1. Thought: Reason about the task 2. Action: Choose and execute a tool 3. Observation: Process tool output 4. Repeat until complete ``` **Example:** ```text Thought: Need Seattle weather to answer umbrella question Action: weather_api(location="Seattle") Observation: {"temp": 52, "condition": "rain", "precipitation": 80%} Thought: Raining with 80% precipitation. Recommend umbrella. ``` ### Plan-and-Execute For complex multi-step tasks: 1. **Planner**: Create high-level plan 2. **Executor**: Execute each step 3. **Replanner**: Adjust based on results Use when order matters and partial failures need recovery. ### Reflexion Self-improvement through reflection: 1. Attempt task 2. Evaluate outcome 3. Generate reflection on failures 4. Store reflection in memory 5. Retry with reflection context ## Multi-Agent Patterns ### Supervisor Central coordinator delegates to specialists: ```text Supervisor / | \ Research Writer Reviewer ``` ### ...