A familiar test still passing is reassuring. It does not tell you whether an agent changed its behavior somewhere the test did not cover.
Agent behavior is spread across instructions, models, tools, retrieval, memory, and orchestration. A small edit in any of those places can change when the agent asks for help, what evidence it follows, or whether it recognizes a tool failure.
Regression testing starts by making those expectations explicit.
Define what must keep working
Write down the intended outcome and the boundaries the agent must preserve. “Handle refund requests” leaves too much open. “Check eligibility before promising a refund, and escalate when policy evidence is missing” gives the evaluation something concrete to exercise.
Record the baseline and candidate versions. Identify the changed components and the workflows they could affect. Mark any paths for which you have no representative case or trace.
For uncommitted changes, an Agentagon review can help identify concerns tied to the captured diff. Preparing and running an evaluation requires a clean, committed input.
Choose cases that could reveal the regression
Include common requests, then add cases where a wrong decision would be consequential or hard to notice. Exercise missing information, tool errors, escalation, and the changed retrieval or tool path.
A compact set with clear expectations can be more useful than a large set whose coverage nobody can explain. Keep the scope honest: passing these cases supports a claim about these cases, not every possible production interaction.
An Agentagon evaluation turns a concern into a reviewed, frozen benchmark. Declare its checks, objectives, and execution limits before using it to judge candidate fixes.
Compare against the same benchmark
Run baseline and candidate through the same evaluation. Compare the behavior you intended to preserve alongside relevant quality, latency, or cost measurements. Keep the underlying execution evidence available for inspection.
Look for patterns such as:
- A tool failure becoming a confident but unsupported answer.
- A prompt edit helping routine requests while breaking an escalation path.
- A faster response skipping the evidence needed for a decision.
- A model change altering tool use even when the final answer looks similar.
If the benchmark itself needs to change, prepare a new comparison. Editing the test after seeing a candidate’s result changes the question you are answering.
Keep failures and unknowns visible
An execution error is not a passing case. An untested path is not evidence of preserved behavior. Inspect missing results, variation, failed checks, and independent review before accepting a summary.
Agentagon’s fix workflow retains failed attempts and verified alternatives under the frozen comparison. The best candidate depends on your requirements: lower cost may be useful, but it does not automatically justify a loss of quality.
Separate verification from release
Once a candidate is verified, select it and prepare delivery. Inspect the patch and measurement summary. Changes introduced during cleanup need fresh verification too.
Publishing a branch, merging it, and deploying it remain separate actions. A useful regression workflow gives the team evidence for those decisions and makes the limits of that evidence clear.