How to evaluate AI models on your own agent
Public benchmarks describe average capability. Your own prompts, tools, data, and failure costs determine which model should ship.
There is no universally best model for an AI agent.
There is only the model that performs best for a particular workflow, prompt, tool set, latency target, and risk budget. Public leaderboards are useful for finding candidates, but they cannot make that product decision for you.
The reliable approach is to evaluate candidate models inside the application they will actually run.
Why public benchmarks are only a starting point
A benchmark holds its task set and harness constant so models can be compared. Your product changes nearly everything around the model:
- The system prompt defines behavior and constraints.
- Retrieval determines what evidence reaches the model.
- Tool schemas affect whether actions are selected correctly.
- The agent loop controls retries, memory, and stopping behavior.
- Your users introduce language and edge cases absent from public datasets.
A model can rank highly on general reasoning and still perform poorly when it must choose between similar tools, preserve state across ten turns, or recover from a failed API call.
Freeze the harness before comparing models
If the prompt, tool definitions, runtime, and model all change at once, the result is impossible to attribute. Pin the non-model parts of the system for each comparison:
| Component | What to hold constant |
|---|---|
| Prompt | Exact version and dynamic instructions |
| Tools | Schemas, descriptions, permissions, and mock behavior |
| Retrieval | Corpus snapshot, chunking, and ranking configuration |
| Runtime | Retry policy, context limits, and stopping rules |
| Evaluators | Rubrics, thresholds, judge model, and judge prompt |
Then change only the candidate model. This turns a vague preference into a controlled experiment.
Build the dataset from real work
Start with production conversations rather than synthetic happy paths. Sample common tasks, valuable tasks, recent failures, and rare but high-risk cases. Remove sensitive data, preserve the relevant context, and label the expected behavior.
A useful case records more than a preferred final sentence. It should describe:
- The user goal.
- Tools the agent should call.
- Tools it must not call.
- Required evidence or constraints.
- The observable definition of completion.
Every meaningful production bug should become a regression case. If an agent issued a refund before verifying an order, preserve that interaction and assert that order lookup occurs before the refund action.
Evaluate the whole trajectory
Final-answer grading misses failures hidden inside a successful-looking reply. Score the complete run:
- Intent understanding
- Tool selection and argument accuracy
- Tool ordering
- Use of returned evidence
- Policy adherence
- Recovery after failures
- Task completion
- Latency and cost per successful outcome
Use deterministic checks for events your system can observe directly. Reserve LLM judges for open-ended qualities such as clarity, tone, and completeness. Keep the trace beside every score so reviewers can inspect why a run passed.
Move evals into the release process
Run a small critical suite on every prompt, model, tool, or harness change. Run the broader comparison suite on a schedule and whenever a promising model is released. A failed safety or task-completion case should block the release, not be averaged away by stronger stylistic scores.
After deployment, compare the offline prediction with production outcomes. When they diverge, add the missing production case to the suite. This is how an evaluation set becomes a living representation of the product rather than a static spreadsheet.
Close the loop with Currai
Currai connects evaluation results to real conversations, model responses, tool calls, errors, and user outcomes. Teams can turn a silent production failure into a reproducible test, compare candidate models on the same workload, and open the trace behind every regression.
Use the Currai integration skill, or start with Currai free.