Before You Fine-Tune Your AI Agent, Capture the Corrections
Fine-tuning is getting easier. Knowing what to teach the model is still the hard part.
LangChain made that gap visible on September 24 when it launched LangSmith Fine-Tuning in public beta. Its new smithtune workflow can turn agent traces into training data, send a supervised fine-tuning job to Fireworks or Baseten, evaluate the result, and prepare the tuned model for deployment.
That removes a lot of infrastructure work.
It does not remove the founder’s judgment.
A fine-tuned model learns from the examples you select. If those examples capture your best decisions, your product can become more consistent on a narrow task. If they capture shortcuts, vague reviews, or accidental behavior, you can make the wrong pattern more repeatable.
Before you fine-tune your AI agent, build a correction system.
Your best training data is probably being deleted
Early AI products create valuable teaching moments every day.
A customer edits a generated report. An operator changes the category an agent selected. A founder rejects a draft because it missed the buyer’s real objection. A specialist steps in when the agent used the right facts but reached the wrong conclusion.
Most products treat these moments as cleanup. The corrected output replaces the first attempt, and the team moves on.
That throws away the lesson.
For each meaningful correction, you want to preserve four things:
- the original input;
- the context and tools available to the agent;
- the action or output the agent produced;
- the corrected action or output an expert accepted.
For an agentic product, the final answer alone may not be enough. LangChain defines a trajectory as the ordered sequence of messages, tool calls, and tool results that shows how an agent completed a task. That sequence matters because the mistake may have happened before the final response.
The agent may have selected the wrong source. It may have skipped a verification step. It may have called the right tool with the wrong parameter. It may have continued when it should have asked for approval.
The correction system should capture the path, not only the destination.
Do not train on every successful run
A completed task is not automatically a good example.
Some runs succeed because a person quietly fixed them. Some contain unnecessary tool calls. Some reach an acceptable answer using a process you would not want repeated at scale. Others solve rare edge cases that should not dominate the model’s normal behavior.
This is why data selection matters more than simply collecting volume.
LangChain reported that an earlier, less selective training set reduced performance on one of its internal fine-tuning experiments. The team improved the result only after changing the curation process and reviewing traces more carefully.
OpenAI’s fine-tuning guidance makes the same point from another direction. Training examples should be correct, consistent, representative of real usage, and complete enough that the model does not have to invent missing information. When quality and quantity conflict, a smaller set of strong examples can be more useful than a larger set of weak ones.
A practical review rubric might ask:
- Did the run produce an outcome the customer actually wanted?
- Did it use the correct evidence and tools?
- Did it follow the business rules an expert would defend?
- Did it handle uncertainty honestly?
- Did it stop or escalate at the right moment?
- Would you want the product to repeat this behavior without supervision?
Only promote a run into the training set when the answer is clear.
Domain expertise becomes the labeling system
The model provider cannot define a good insurance submission, a safe patient handoff, a credible board report, or a realistic construction estimate for your specific workflow.
That judgment comes from the domain.
An experienced professional knows which missing field changes the decision. They know which source outranks another. They know when an exception is harmless and when it creates risk. They can tell the difference between a polished answer and a useful one.
This is where a domain-expert founder has an advantage.
Your expertise should become a simple labeling system that a reviewer can apply repeatedly. Start with labels such as:
- accepted without changes;
- accepted after a minor edit;
- wrong source or evidence;
- wrong decision;
- missing verification;
- should have asked a question;
- should have escalated;
- unsafe or outside scope.
Then require a short reason for any rejected or corrected run.
Over time, those reasons expose recurring failure patterns. They also show whether the problem belongs in the prompt, the tool design, the workflow, the evaluation suite, or a fine-tuning experiment.
Not every recurring error needs model training.
Fine-tune only after the workflow is stable
LangChain recommends starting with the agent harness before supervised fine-tuning. That is good advice for a young product.
First check the simpler layers:
- Are the instructions clear?
- Does the agent have the right context?
- Are tool descriptions specific enough?
- Can deterministic code handle part of the task?
- Is a required source missing?
- Does the workflow need an approval gate?
- Do your evals measure the corrected behavior?
Fine-tuning makes more sense when the task repeats, the desired behavior is well understood, and you have reviewed examples showing the right path.
Keep a separate test set that the model does not train on. Compare the tuned model with the current version. Check outcome quality, tool choices, cost, latency, and regressions. A model that improves the common case but weakens an expensive edge case may still be a bad release.
The goal is not to own a custom model. The goal is to deliver a better customer outcome with less manual correction.
Build the correction loop now
You do not need to wait for a fine-tuning project.
Add one review action to your current product. Let the expert accept, edit, reject, or escalate the agent’s work. Save the run, the correction, and the reason. Remove sensitive data that is not needed for learning. Review the patterns every week.
That loop improves the product before you train anything.
It gives you better eval cases. It reveals missing tools and unclear rules. It shows where customers need human judgment. When fine-tuning becomes the right move, you already have the raw material.
Models will keep getting easier to train. Curated judgment will remain scarce.
If you want help turning a workflow you understand into a focused AI product, a correction loop, and a 12-week path to launch, book a strategy call with AI Product Accelerator.