Selma's Blog

Momentum Learning Day 7

Last updated: September 23, 2025

# Day 7 Momentum Learning Series

Today I got deeper into smolagents, specifically the CodeAgent.

The core insight: letting the agent write and execute Python code instead of just JSON unlocks flexibility and makes tool use feel natural.

In practice, this means you don’t just get a black-box answer. You see:

  • the Python code the model generated,
  • the execution results,
  • and the reasoning steps logged in memory.

For me, that transparency was the big shift. It felt less like guessing and more like debugging with a colleague.

What I Built: Alfred the Party Planner 🦇

I followed the tutorial and built a playful butler agent (Alfred) that plans a party at Wayne’s mansion.

  • Custom tools I wrote:
    • suggest_menu(): suggest menus depending on the occasion.
    • catering_service_tool(): simulate picking the best catering service in Gotham.
    • SuperheroPartyThemeTool: generate themed ideas .
  • Prebuilt tools I plugged in:
    • DuckDuckGoSearchTool (search),
    • VisitWebpageTool (navigate),
    • FinalAnswerTool (format output).

With these wired into a CodeAgent, I could ask:
“Give me the best playlist for a party at Wayne’s mansion. Theme: villain masquerade.”

Alfred went step by step: picked the theme, searched, browsed links, and finally returned a curated playlist.
Watching each tool call and execution log made the whole process feel robust and traceable.

Reflections as an Engineer

  • smolagents is pragmatic: the MultiStepAgent + execution log design is exactly what makes debugging feasible.
  • Small tools matter: even trivial ones (suggest_menu) gave structure and extended capabilities.
  • Observability is real: I like that smolagents integrates with OpenTelemetry + Langfuse. Being able to replay a run or see why it failed is non-negotiable in production.
  • Feels future-proof: this setup makes agents composable, testable, and closer to real software systems rather than “magic prompts.”

Next Step

The party planner was fun, but the same pattern applies to serious workflows.
Next, I want to try building a study assistant that schedules prep tasks with [datetime] and pushes runs to the Hugging Face Hub for reuse.

End of Day 7. I feel like I’m not just learning AI concepts anymore, I’m actually starting to think like an engineer of agents.

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