My personal junior. Part 2. Giving the agent a little bit of brains

▤ Summary

This article explains how to make a LangGraph-based agent smarter by adding planning, execution control, memory compression, and finalization steps. It shows how to use LangChain components such as PydanticOutputParser, PromptTemplate, and message handling to structure agent behavior. It also covers practical engineering patterns for handling tool calls, limiting step iterations, storing history, and checkpointing state in memory or Postgres. The examples focus on integrating GigaChat and Qwen models with LangFuse-managed prompts and MCP workflows. Overall, the piece provides implementation guidance for building a more reliable agent architecture.

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