Agentic System Design AI Prompt

Design a reliable LLM agent system that uses tools to complete multi-step tasks.

Agent task: {{task}}
Available tools: {{tools}} (web search, code execution, database query, API calls, file operations)
Reliability requirement: {{reliability}} (best-effort or guaranteed completion)
Human-in-the-loop: {{hitl}} (yes/no — is human approval required for certain actions?)

1. Agent architecture:

   ReAct loop (Reasoning + Acting):
   - Thought: the agent reasons about what to do next
   - Action: the agent selects and calls a tool
   - Observation: the agent receives the tool result
   - Repeat until the agent decides the task is complete

   Plan-and-execute (more reliable for complex tasks):
   - Planning step: decompose the task into a sequence of sub-tasks
   - Execution: execute each sub-task sequentially (or in parallel where possible)
   - Re-planning: if a step fails, re-plan from the current state

2. Tool design:
   - Each tool has: name, description (the agent reads this to decide when to use it), input schema, output schema
   - Tools must be: idempotent where possible (safe to retry), fast (< 5s for most tools), well-scoped (do one thing well)
   - Tool description quality is critical: the agent's tool selection depends entirely on the description
   - Validation: validate tool outputs before passing to the next step

3. Error handling and retries:
   - Transient failures: retry the tool call up to 3 times with backoff
   - Persistent failures: skip the step and log; reroute to a fallback tool if available
   - Maximum iterations: set a hard limit (e.g., 20 steps) to prevent infinite loops
   - Checkpoint saving: save the agent's state after each completed step; resume from the last checkpoint on failure

4. Safety for agentic systems:
   - Minimal footprint: request only the permissions needed for the current task
   - Human approval gates: require human confirmation before irreversible actions (sending emails, deleting data, making payments)
   - Sandboxed execution: run code in an isolated container (e.g., E2B sandbox)
   - Audit log: log every action the agent takes, every tool it calls, and every decision it makes

5. Frameworks:
   - LangGraph: production-grade graph-based agent framework with state management
   - LlamaIndex Agents: strong for RAG-augmented agents
   - AutoGen (Microsoft): multi-agent conversation framework
   - Pydantic AI: type-safe agent framework with validation
   - Anthropic's computer use: for agents that interact with GUIs

Return: agent architecture selection, tool specification schema, error handling strategy, safety controls, and framework recommendation.