If your AI agent isn’t performing the way you want, the instinct is to tweak the prompt, swap the model, or add another tool. Those changes might help — but they’re guesses. According to Vivek Trivedy (Applied Research Lead at LangChain Labs), the systematic approach to agent improvement is fundamentally a data mining problem. And the data you need is already sitting in your traces.

This guide walks through the practical framework Trivedy laid out at AI Engineer World’s Fair 2026 and expanded in a July 7 LangChain blog post.

The Core Insight: Traces Are Your Improvement Substrate

Every time your agent runs, it generates a trace — a full record of every step: which tools it called, what inputs it received, what outputs it produced, and where it succeeded or failed. LangSmith captures these traces and makes them queryable.

The key reframe is this: traces are not just debugging information, they are training data for your agent improvement loop. Mining them systematically is how you move from guessing to hill-climbing.

Step 1: Deploy and Start Collecting Traces

You can’t mine traces you don’t have. The first step is getting your agent into production (even limited production) and capturing its behavior in LangSmith.

If you haven’t set up LangSmith tracing, refer to the LangSmith documentation for integration instructions. Key things to instrument:

  • Full tool call chains (inputs, outputs, latency)
  • Final outputs (with user feedback signals if you have them)
  • Any intermediate reasoning steps your agent produces

The goal at this stage is breadth: capture the full range of what your agent does, not just the cases you think are interesting.

Step 2: Mine Traces to Identify Failure Clusters

With traces flowing in, the next step is analysis. This is the “data mining” part of the problem.

In LangSmith, you can:

  • Filter traces by metadata (user, session, environment, timestamp)
  • Tag traces manually or with automated classifiers
  • Use LangSmith Engine to automatically scan traces, detect patterns, and surface failure modes

Trivedy recommends looking for failure clusters — groups of traces that share a common failure pattern. These might be:

  • A specific type of user query where the agent consistently gives wrong answers
  • A tool that the agent calls with malformed inputs in certain contexts
  • A reasoning step where the agent confidently produces incorrect outputs

Failure clusters are your improvement priorities. One well-identified cluster is worth more than a hundred anecdotal bug reports.

Step 3: Curate Evaluations from Your Traces

Once you have failure clusters identified, you build evaluations (evals) from them. This is the step that most teams skip — and it’s why their agents don’t improve systematically.

Evals are not just metrics for measurement — they are training signals for agent improvement. An eval that captures a failure mode becomes the specification for what “fixed” looks like. Without evals, you can’t know if your changes actually helped.

To build evals from traces in LangSmith:

  • Identify representative examples from your failure cluster
  • Annotate them with the correct expected output (human-in-the-loop, or use an automated judge for scale)
  • Save them as an evaluation dataset in LangSmith

Trivedy’s recommended scale: aim for 20–50 high-quality annotated examples per failure cluster before running experiments. Volume without quality is noise.

Step 4: Run Experiments

With your eval dataset in place, you can run structured experiments. Each experiment is a change — a prompt edit, a model swap, a tool update, a harness change — measured against your eval dataset.

LangSmith’s evaluation tooling makes this systematic:

  • Run your changed agent against the eval dataset
  • Compare scores to the baseline
  • Identify regressions (did fixing one thing break another?)

Trivedy’s recommended experiment order is: Harness Engineering → Fine-Tuning → Harness Engineering (the “funnel/sandwich” approach). Start with harness changes (prompt structure, tool selection, memory integration) because they’re fast to iterate. Fine-tune a model when harness changes plateau. Return to harness engineering after fine-tuning to squeeze out remaining gains.

Step 5: Scale With Open Models and Compound Systems

One challenge that grows with success: processing large volumes of traces to find failure clusters is expensive if you’re using frontier models as your “trace judge.”

Trivedy’s solution is to use fine-tuned open models as narrow trace judges. A smaller model fine-tuned specifically to classify a particular type of agent failure can outperform a general frontier model on that specific task — at a fraction of the cost. This makes mining large trace volumes economically viable.

For complex analysis tasks, compound agent systems (multiple specialized agents working in parallel on different aspects of the trace analysis) can distribute the work further.

Step 6: The Continual Learning Loop

The goal isn’t one improvement cycle — it’s building a flywheel where agent behavior continuously improves over time. The loop:

  1. Agent runs in production → generates traces
  2. Traces are mined for failure clusters
  3. Failure clusters → curated evals
  4. Evals → experiments → improvements deployed
  5. Improved agent runs → generates new traces
  6. Repeat

LangSmith Engine is designed to operate this loop with increasing automation: it reads traces, detects issues, suggests fixes, generates evals, and commits insights to memory — reducing the manual effort required at each step.

Key Takeaways

  • Treating agent improvement as a data mining problem is more effective than treating it as an intuition-driven process
  • Traces are your most valuable data asset — instrument everything from day one
  • Evals built from real failure clusters are the forcing function for systematic improvement
  • Open model fine-tuning makes processing large trace volumes economically viable
  • The goal is a continuous flywheel, not a one-time optimization

Refer to the LangSmith documentation and LangChain Academy for hands-on tutorials to implement this workflow.

Sources

  1. LangChain Blog: Improving Agents is a Data Mining Problem — Vivek Trivedy, July 7, 2026
  2. LangChain Blog: The Anatomy of an Agent Harness
  3. LangSmith Documentation

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260708-0800

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