Fast Facts
- Built and tested three Python-only systems—retrieval-only, action-only, and hybrid—to evaluate how retrieval and action components interact without external tools.
- Identified and fixed two key bugs related to task parsing and retrieval logic, improving system accuracy in matching ticket updates and understanding actions.
- Found that retrieval alone can’t modify tickets, and actions alone lack knowledge context; only the hybrid system successfully integrates both for complex tasks.
- Demonstrated that simple retrieval methods (TF-IDF) and regex parsing have limitations, suggesting potential improvements like semantic search and more robust parsing for broader applications.
Understanding RAG and Agents
Retrieval-Augmented Generation (RAG) helps find relevant information from a large collection of documents. It can locate facts or data but cannot take actions like updating a ticket. On the other hand, an agent can perform tasks such as changing a record or assigning a task. However, an agent needs the right information to decide what to do. Combining these two means retrieving information and then using it to act. But, it is not as simple as assuming both systems automatically improve each other. Building separate, functional parts creates a clearer picture of what each does. This approach shows that retrieval helps decision-making only when connected properly to action, otherwise, it might not add value.
Building and Testing the Hybrid System
The system was created using pure Python, with no external tools or APIs. I made three versions: one with only retrieval, one with only actions, and a hybrid that connects both. I tested all three with nine tasks to see how they perform. The retrieval is simple, based on word frequency, and runs fast—around 1.4 milliseconds per search. The environment includes small ticket data and actions like changing status or priority. The hybrid setup calls retrieval first, then passes that information to a simple planner that decides what to do. While testing, I found two bugs: one where the system didn’t check if actions were needed, and another where regexes failed to parse commands correctly. Fixing these was essential to get accurate results, which showed how important proper connection and debugging are in these systems.
What the Results Show and Their Implications
When running all systems again after fixing bugs, the hybrid system performed best overall. It could find relevant information and act on tickets correctly. The retrieval-only system answered questions but could not change tickets. The agent-only system could update records but lacked knowledge for questions. The main insight is that neither retrieval nor action alone suffices for tasks that need both. Combining them in a simple, transparent way shows the real value of integrating information retrieval with decision-making. This method also highlights that building a clear, modular system makes debugging and understanding easier. Even with small tests, it becomes obvious how crucial the connection is—without proper linkages, systems may fail or give incomplete results. This approach sets a foundation for more complex AI tools, emphasizing transparency and precise functionality.
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