When you type a question into ChatGPT, the model only has what you typed to work with. But tools like Claude Code can plan, iterate, test, and recover from mistakes. They work more like we do. The difference is the agent harness: Planning tools, file system access, sub-agents, and carefully crafted system prompts that turn a raw LLM into something genuinely capable.

Sydney Runkle is back on Talk Python representing LangChain and their new open source library, Deep Agents: A framework for building your own deep agents with plain Python functions, middleware hooks, and MCP support. This is how the magic works under the hood.

Sentry Error Monitoring, Code talkpython26

Agentic AI Course

Talk Python Courses

Links from the show

Sydney Runkle: github.com

Claude Code uses: x.com

Deep Research: openai.com

Manus: manus.im

Blog post announcement: blog.langchain.com

Claudes system prompt: github.com

sub agents: docs.anthropic.com

the quick start: docs.langchain.com

CLIs: github.com

Talk Python's CLI: talkpython.fm

custom tools: docs.langchain.com

DeepAgents Examples: github.com

Custom Middleware: docs.langchain.com

Built in middleware: docs.langchain.com

Improving Deep Agents with harness engineering: blog.langchain.com

Prebuilt middleware: docs.langchain.com

Watch this episode on YouTube: youtube.com

Episode #543 deep-dive: talkpython.fm/543

Episode transcripts: talkpython.fm

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X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy