Table of Contents
Quick Answer
AI agents use an LLM as a planner, tools (functions, APIs) to act, and memory for state. Use LangGraph or CrewAI for orchestration; keep tool sets small; always add human checkpoints for irreversible actions.
- Agents work best for multi-step tasks with clear success criteria
- Tool explosion (20+ tools) degrades performance — curate carefully
- Always sandbox shell/file tools in production
What You'll Need
- OpenAI-compatible model with function calling (Assisters, GPT, Claude)
- Orchestration framework: LangGraph, CrewAI, or custom loop
- Tools defined with JSON schema
- Observability: Langfuse or LangSmith
Steps
- Define the goal. Clear success criteria, not open-ended "help the user".
- Curate tools. Start with 3-5. Each needs a clear name, description, schema.
ts
const tools = [
{ name: 'search_docs', description: 'Search internal docs by query', parameters: {...} },
{ name: 'send_email', description: 'Send an email (requires human approval)', parameters: {...} },
];
- Write the planner prompt. System:
You are an agent. Think step by step. Call one tool at a time. - Build the loop. LangGraph or a manual while-loop calling the LLM, parsing tool calls, executing, feeding results back.
- Add memory. Short-term: message history. Long-term: vector store of past interactions.
- Checkpoints. For email, payments, deletions — pause and ask user.
- Max iterations. Hard cap at 10-15 to prevent infinite loops.
- Observability. Log every step to Langfuse. Review failures weekly.
Common Mistakes
- Too many tools. Agents lose focus past 10 tools.
- No iteration limit. Runaway loops cost thousands.
- Ambiguous tool descriptions. AI picks wrong tool.
- Skipping human approval. One wrong email destroys trust.
Top Tools
| Tool | Use |
|---|---|
| LangGraph | State machine for agents |
| CrewAI | Multi-agent collaboration |
| AutoGen | Conversational multi-agent |
| Langfuse | Agent observability |
| Pydantic | Tool schema |
Conclusion
Agents shift AI from chatbot to coworker in 2026. Start with LangGraph + 5 tools + Langfuse. Always checkpoint destructive actions. Misar Dev builds agents from natural language spec.