What is Agent Training?
Also called agent tuning, knowledge training.
Agent training refers to improving an agent's behavior after setup by supplying documents, examples, and corrections that it can retrieve or that are folded into its instructions. In most business products this does not change the underlying model at all. The word is used loosely, so it is worth asking whether a feature stores knowledge, edits instructions, or genuinely fine tunes a model.
Three different mechanisms travel under this one name. Retrieval adds documents to a store the agent searches before answering. Instruction editing changes the standing rules it follows every time. Fine tuning adjusts model weights on example data, which is comparatively rare in business products because it is costly to maintain and hard to reverse. The three differ in how quickly a change takes effect and how easily it can be undone.
In practice most teams train by correction. The agent produces something, a person edits it, and the edited version is stored as the preferred example or the underlying rule is rewritten. This works because the failures worth fixing are usually specific and repeatable: the wrong tone, an outdated policy, a system it should have checked before answering.
The word implies the agent improves on its own over time, which is generally not what happens. Improvement comes from someone noticing a pattern and changing the material behind it. Where memory features do accumulate automatically, they accumulate mistakes as readily as facts, so periodically reviewing what an agent believes is a normal part of running one.
Training builds on onboarding, which establishes the baseline. The material it produces usually ends up organized as a skill library or as standard operating procedures. Where the correction concerns what an agent is permitted to do rather than what it knows, the right change is to permissions and approval rules, not to training material.
Key points
- Usually knowledge and instructions, not model weight changes
- Retrieval, instruction editing, and fine tuning are different mechanisms
- Most improvement comes from correcting specific repeatable failures
- Automatically accumulated memory needs periodic review
In practice
A property manager notices the agent keeps quoting a deposit policy that changed in the spring. She replaces the old document in its knowledge store, adds the current policy as a rule in the standing instructions, then asks the tenant question three different ways to confirm the new answer comes back each time. Nothing about the model changed, only the material it reads.