AI agent testing against the work your team expects
A convincing demonstration leaves open how an agent behaves when a record is missing, a request is ambiguous or a tool refuses access. Gateflow tests an existing agent against agreed tasks and failure cases. The result is a record of observed behavior that your team can use to decide what needs fixing before release.
AI agent evaluation with Gateflow
Gateflow develops software and controlled agent workflows from Frankfurt. A testing assignment can be scoped separately from building the original agent, provided the necessary access and records are available. The Gateflow company profile describes the implementation work that can follow when evaluation reveals a problem in the agent or a connected system.
Agent test cases from actual user tasks
Your team supplies representative requests and explains what a correct result looks like. Gateflow separates cases for expected operation from cases involving incomplete data or exceptions. The reference set needs clear review criteria, so a fluent answer does not receive a positive result when the agent has failed to complete the underlying task.
Tool-call testing beyond the final answer
The final wording does not reveal every action an agent took. Gateflow checks the available trace of tool requests and responses against the intended process. The evaluation asks whether the correct record was used, whether a failed call was acknowledged and whether the agent attempted an action outside the approved task.
AI integration checks for permission boundaries
AI integration checks examine how the agent behaves when access is restricted or an operation requires approval. The scope identifies which environments and accounts can be tested. Findings describe the observed failure and reproduction steps. A passed sample is not a guarantee that every possible input or future system change will behave correctly.
Project acceptance criteria for AI workflows
Gateflow can help the responsible team turn evaluation findings into project acceptance criteria. Results need a decision: correct the implementation, restrict the supported tasks or defer release. Technical project leadership can coordinate those decisions with suppliers when the agent, data source and business application are maintained by different parties.
Regression tests for prompt and model changes
A prompt adjustment can improve one task while making another result worse. Gateflow reruns the agreed cases against a candidate change and records differences. The comparison should identify the tested configuration, including model and relevant settings, so your team can review the evidence before replacing the version currently in use.
Evaluation reports that support repairs
The report groups failures by the work needed to address them, such as a missing permission check or an unclear tool response. Examples include the necessary context and expected behavior. Gateflow distinguishes reproduced defects from questions that need further investigation, allowing the implementation team to prioritize repairs without guessing what the test actually showed.
AI testing scope and repeatable handover
An agent description, access to an approved test environment and sample cases help define the assignment. Gateflow agrees the checks and how results will be reviewed with your team. The project method covers delivery of the test material and findings, so the same checks can be repeated after a repair or a later release.
Discuss your AI agent evaluation
Gateflow tests your AI agent against agreed user tasks, tool permissions and failure cases. You will receive repeatable test cases and documented findings to decide which corrections are needed before release.