Function-calling agents
Models selecting and calling your functions with structured arguments, then acting on the results within your application.
SERVICES / MODEL PROVIDERS
Evaluate agents built with OpenAI.
Independent, application-specific evaluation for agents built with OpenAI models, the Responses API and the Agents SDK, including fair comparisons when you change models or settings.
How teams build with OpenAI
Agents SDK workflows
Multi-agent setups with handoffs between specialist agents, built on OpenAI's Agents SDK or similar frameworks.
Structured output pipelines
Agents that must return data in strict formats for downstream systems, where a single malformed field can break a process.
What we assess
Task outcome and tool use
Whether the agent completes the workflow, calls the right functions with valid, business-correct arguments and leaves the right state behind.
Handoffs between agents
Whether work is routed to the right specialist, context survives the handoff and nothing is dropped or duplicated.
Output correctness
Schema validity plus semantic correctness: a perfectly formatted answer can still contain the wrong value.
Model and setting changes
Model choices
Compare model families and sizes on your tasks. A smaller, faster model may be sufficient for some steps and not others.
Reasoning and parameter settings
Settings that trade speed for depth change quality and cost. We measure the effect on your workflows rather than assuming it.
Prompt and tool definition updates
Small wording changes in instructions or tool descriptions can change behavior widely. Regression runs catch side effects early.
Working with OpenAI evaluation tools
Platform evals where they fit
OpenAI offers evaluation features for running graded test sets. We supply the reviewed datasets and calibrated graders that make them useful.
Independent checks alongside
Outcome verification and business-rule checks run in your own environment, so conclusions don't depend on a single tool.
Recorded configuration
Model snapshots, prompts, tool schemas and settings are recorded for every run.
Plan the engagement
Your inputs
API or test environment access, current prompts and tool definitions, business rules, example tasks and a domain reviewer.
Your deliverables
A benchmark, prioritized findings, a change recommendation where relevant and reusable evaluation assets.
Support after handover
We can retest fixes, extend coverage to new OpenAI workflows or move to recurring Managed Evaluations as your agent changes. Platform usage and licenses stay in your account.
Make your next decision with evidence.
Start with one workflow, clear success criteria, and an evaluation plan your team can use.