Selma's Blog

Momentum Learning Day 9

Last updated: September 29, 2025

Day 9 | Momentum Learning Series

After four days away, I came back to the course today. Pausing was a useful reminder: momentum is fragile, but once the concepts have been internalized, resuming feels less like starting over and more like reconnecting with a system already in place.

The Agents course is still long, but I’m steadily moving through it , aiming to complete the smolagents section tomorrow inchallah.

Retrieval Agents

Today’s focus was on Retrieval-Augmented Generation (RAG) and its agentic extension.

  • Traditional RAG: simply retrieval + generation.
  • Agentic RAG: retrieval becomes iterative and reflective. Agents can formulate queries, evaluate results, and loop until a satisfying outcome is reached.

This shift made me see retrieval not as a static lookup, but as a reasoning layer tightly integrated with the agent’s decision cycle.

What I Implemented

  1. Web Search Agent with DDGS
    • Used CodeAgent with DuckDuckGoSearchTool.
    • Flow: analyze request → retrieve → process → store for reuse.
    • This embedded retrieval directly inside the reasoning process, rather than treating it as a side operation.
  2. Custom Knowledge Base with BM25Retriever
    • Built a small knowledge set (superhero party themes).
    • Applied a text splitter, then designed PartyPlanningRetrieverTool with BM25 to return top 5 ranked results.
    • Engineering perspective: constructing a pipeline — raw docs → embeddings/index → retriever → agent reasoning.

Embedded Reflections

  • Building tools felt less like “trying out features” and more like designing interfaces for agents to reason over knowledge.
  • BM25 gave precise ranking control, showing that retrieval quality is deeply tied to algorithmic choice, not just embeddings.
  • Compared to earlier exercises, today’s work had more of a system-architecture feel: retrieval pipelines as part of the reasoning flow, not isolated utilities.

Quiz Checkpoints

  • Tool Creation → lightweight functions via @tool; complex ones via Tool subclasses.
  • CodeAgent & ReAct → iterative cycle of reasoning, action, feedback, adjustment.
  • Tool Sharing → Hugging Face Hub makes custom tools reusable across projects.
  • ToolCallingAgent → emits JSON with tool + arguments.
  • Default Toolbox → provides baseline tools (search, Python, etc.) for prototyping.

Takeaway: Retrieval isn’t passive storage or search; it’s an active reasoning partner in agent workflows. And even with long gaps, progress compounds: the deeper the system level understanding, the quicker I can pick up where I left off.

At this point, it’s less about learning a course and more about shaping the mindset of an agent systems architect, designing the reasoning flow itself, not just calling tools.

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