AI Quality
AI Agent Testing
Test AI agents on tool calls, actions, and multi-step tasks — the right tool, valid arguments, recovery when a tool fails, and regression after you change the agent graph.
The problem
An agent is not a chatbot with extra personality. It chooses tools, passes arguments, and changes the world: tickets, refunds, emails, database writes. A fluent plan that calls the wrong API is a production incident, not a wording issue.
AI agent testing (agent evaluation) is QA for that loop: observe → choose tool → call → read result → act again. LLM evaluation scores text. This page scores actions.
What’s at risk
- Tool selected for the wrong intent
- Arguments that are missing, typed wrong, or unsafe (IDs, amounts, other users’ data)
- Loops that never stop, or stop before the task is done
- No recovery when a tool times out or returns garbage
- Silent breakage after you add a tool or change the planner prompt
What we test
- Tool selection against a labeled intent set
- Argument schema and authorization (can this user do this?)
- Multi-step tasks: order of calls, use of intermediate results
- Failure handling: retries, human handoff, user-visible error
- Side-effect safety: duplicate charges, duplicate tickets, writes in the wrong account
- Regression when the graph, tools, or model change
Related: chatbot testing if the surface is conversation-first with few tools. AI red teaming if the question is “can a user make the agent do something forbidden?”
How we work
- Map the tools and the side effects that actually matter
- Build tasks a real user would give the agent — including incomplete and hostile ones
- Trace each run: which tool, which args, which result
- Mark failures by action, not by how confident the prose sounded
- Re-run the pack after planner or tool changes
See also our insight on testing AI agents, tool calls, and regressions. Parent: AI testing services.
Deliverables
- Agent scenario pack and traces
- Findings on tool-call accuracy and unsafe actions
- A regression list to run after graph or tool changes
Suitable for
Teams shipping support agents, ops agents, or any LLM that can call internal APIs.
FAQs
Do you test the tools themselves?
We test the agent’s use of the tools. Broken tools still show up as failed tasks; fixing the API may be engineering’s job.
What if the agent is non-deterministic?
We run the same task more than once and report instability as a finding, not as “the model being creative.”
Talk to our QA team
Need this tested on a real product?
Tell us the AI surface, the next change you plan to ship, and what a bad answer would cost.