Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain
Vivek Trivedi from LangChain argues that improving AI agents is about analyzing trace data — the logged steps and decisions an agent takes while working.
Instead of guessing why an agent performs worse after updates, you can have other AI models read through these traces and identify problems. Trivedi contends that observability and continuous learning are the same problem viewed from different angles. He demonstrates that cheaper, open-source models can achieve the same quality as expensive top-tier models if you're smart about how you phrase instructions — and that it pays to iterate on instructions until progress slows, then fine-tune the model to advance further, then return to refining instructions again.
Vibekollen prepared this summary with AI from the original publication. The content belongs to AI Engineer.
More to read
Lumaril, which develops AI software for optimizing industrial production by connecting to existing control systems, raised a $66M Series A at a $400M valuation
Techmeme 3 h ago
DeepSeek open sources Super-Agent as a research prototype, a proactive coding agent it says can monitor group chats for coding issues and suggest solutions
Techmeme 4 h ago
Insurance software maker Guidewire agrees to acquire Hyperexponential, which makes AI-powered insurance underwriting and pricing software, for $1.7B in cash
Techmeme 4 h ago
One word for AI agents in 2027
Nate Herk 8 h ago