How I Build AI Agents for Real Products
Building an agent for production is a completely different challenge from a demo. First: precisely define what problem it solves and what tools it needs. An agent without well-defined tools is just an LLM with the illusion of autonomy.
I use LangGraph to orchestrate the flow with clear nodes. Memory is the most underestimated piece: I separate episodic (history), semantic (RAG), and procedural (system instructions). For errors, retries with exponential backoff and LangSmith traces to detect failure patterns.
Install the full stack with: pip install langgraph langsmith langchain-openai && npm install -g @langchain/cli