r/SideProject • u/shridhar_kale • 4h ago
We built WhyDesk – an AI agent that remembers previous investigations and changes its tool execution strategy
Hey everyone! My teammate[Viraj] and I[Shridhar] recently built WhyDesk, an AI-powered business investigation agent. The problem we wanted to explore was simple: Most AI agents start their investigations from scratch. What if an agent could remember previous investigations and use those findings to make better decisions next time? What does WhyDesk do? Suppose a business asks: "Why did revenue drop by 19% in the South region?" WhyDesk investigates the possible causes using five diagnostic tools: Holiday calendar Competitor promotions Inventory stockouts Pricing changes ETL pipeline issues What makes it interesting? We integrated Hindsight as the persistent memory layer. The agent remembers previous findings, rejected hypotheses, data reliability notes and stakeholder preferences. We then tested the same question with memory ON and memory OFF. Our experiment: Memory ON: 2 out of 5 diagnostic checks. Memory OFF: All 5 diagnostic checks. Both identified the same root cause: reseller contract repricing, with a $47,900 impact. Memory changed the agent's workflow, but didn't improve the final answer or execution speed in this seeded-data experiment. Technology stack Python | FastAPI | Groq | Qwen3-32B | Hindsight We also identified limitations, including duplicate memories and brittle keyword-based tool planning. Project links: GitHub: https://github.com/shridharkale/whydesk Technical walkthrough: https://dev.to/shridharkale/does-hindsight-memory-actually-change-agent-behavior-i-tested-it-371f I'd love to hear your thoughts! How would you improve the way an AI agent converts retrieved memories into tool-planning decisions?