deep agents

Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain

Aug 12, 2026 · 20:02

Vivek Trivedy, lead of applied research at LangChain, argues that improving agents is fundamentally a data mining problem: ship agents, collect traces, then mine them to drive continual learning. He claims observability and continual learning are the same problem because agents operating in environments produce trace data, which is the substrate for all improvement. Trivedy details how LangChain sends agents to read other agents' traces to find good/bad interactions, detect degradation after compactions, and test counterfactuals like swapping GPT-5.5 for GLM 5.2. He shares that with Harvey on a legal benchmark, an open model matched Opus's trace judging at one to two orders of magnitude lower cost, achieved through harness engineering informed by traces. His rule for when to stop prompt tuning and start fine-tuning is feedback speed: harness engineering answers in about two minutes, so exhaust that ceiling first, then fine-tune to break through, then return to harness engineering. He…

The Agentic AI Engineer - Benedikt Sanftl, Mutagent

Jun 29, 2026 · 34:50

Benedikt Sanftl and Burak of Mutagent introduce the Agentic AI Engineer, an automated loop that builds, evaluates, and optimizes AI agents at scale. They argue the human bottleneck in manually iterating on agent prompts, evaluations, and diagnostics can be removed by using an orchestrator that manages spec-driven development, eval-driven testing, and online monitoring. The offline loop covers spec, build, and eval; the online loop monitors traces, diagnoses failures, and feeds improvements back into the system. They demo two research-preview agents: an evaluator that constructs eval suites and a diagnostics agent that analyzes production traces to suggest targeted fixes. The platform connects to observability tools and target frameworks (e.g., Claude Code, LangChain) and outputs PRs or updated agent definitions. The core claim is that automating this lifecycle enables teams to ship and improve hundreds of agents with faster iteration and less manual effort.