When software acts on its own, testing has to cover what it does
AI agents plan, call tools and take actions, often across many steps. We test whether they complete tasks correctly, stay inside their permissions, handle failure and avoid runaway behaviour, with QA engineers reviewing the traces behind each result.
Overview
What it is. AI agent testing evaluates systems where a model decides what to do next: choosing tools, calling APIs, reading and writing data and working through multi-step tasks. We test the whole run, not just the final message, including planning, tool use, permissions and recovery.
Who it is for. Teams building agents or tool-using assistants, products where AI can change records or trigger workflows, and organisations moving from a chat interface to an assistant that acts.
What you get. Findings backed by run traces, a map of where the agent’s autonomy needs limits or confirmation, a reusable set of task scenarios, and an honest statement of what was not covered.
Why it matters
The problem it addresses. An agent’s mistakes compound across steps and can have real effects: a wrong tool, a loop that never ends, an action taken without confirmation, or instructions hidden in a web page or document that it follows.
When it matters most. Before an agent gets access to real systems or data, when tools or permissions are added, when the underlying model changes, and whenever an action cannot easily be undone.
What happens without it. Problems appear as unexpected actions in production, runaway cost or repeated calls, and incidents that are hard to explain because nobody reviewed the traces.
What we test
Task success
whether the agent reaches the goal correctly across typical, awkward and ambiguous tasks
Tool selection and use
whether it picks the right tool, passes the right parameters and uses the result correctly
Planning and multi-step behaviour
loops, dead ends, skipped steps and drift away from the goal
Permissions and boundaries
actions outside its remit, and confirmation before anything irreversible
Failure and recovery
tool errors, timeouts, partial results and what the agent does next
Injection through content
instructions embedded in pages, files or tool output that the agent might follow
Limits and runaway behaviour
step, time and spend limits, and behaviour when they are reached
Traceability
whether runs can be reviewed step by step to explain what happened and why
How UpDoer approaches AI Agent Testing
Understand
Requirements, tickets, code diffs and product context.
We map the agent’s goals, tools, permissions, data access and the systems it can change, and agree which actions need a person’s approval.
Risk Mapping
Where a failure would cost the most.
Tasks and tools are ranked by consequence: actions that change data, spend money or reach customers come first, along with anything irreversible.
Test Strategy
Coverage chosen, test cases drafted.
We design task scenarios, from normal to ambiguous to hostile, plus tool-failure cases, and decide which run automatically after every change.
Execute
Manual and automated testing runs.
Scenarios run in a safe environment with real or simulated tools, and full traces are captured. Exploratory sessions push the agent with unclear goals, conflicting instructions and failing tools.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI helps draft task variations and hostile inputs, cluster similar run failures and summarise long traces, so review starts from the runs most likely to matter.
Human Verification
A QA engineer checks every flagged result.
A QA engineer reads the traces behind every flagged run, judges whether actions were appropriate, and confirms which failures are real and reproducible.
Report & Improve
The verified quality signal, then better coverage.
Findings are reported by consequence with the traces that show them and the coverage limits stated. Confirmed failures join the scenario set re-run after each change to the model, tools or permissions.
AI Agent Testing: from action risk to release signal
01
Where it breaks
Wrong toolEndless loopsUnapproved actions02
Test workflow
TasksToolsPermissionsRecovery03
AI assists
Draft task variantsCluster failed runsSummarise traces04
Humans decide
Judge each actionSet approval gates05
Release signal
Coverage by riskOpen findingsGo / no-go by a person
Illustrative workflow. Not a client result or a measured outcome.
AI agent coverage matrix
| Quality area | What we validate | AI assistance | Human validation |
|---|---|---|---|
| Task success | Goal completed correctly on typical and ambiguous tasks | Drafts task variations | Judges whether the outcome is right |
| Tool use | Right tool, right parameters, results used correctly | Clusters wrong-tool and bad-parameter runs | Confirms the impact of each misuse |
| Planning | Loops, dead ends, skipped steps, drift | Flags repeated or circular steps in traces | Decides which are real defects |
| Permissions | Actions outside remit; approval before irreversible ones | Suggests boundary scenarios | Assesses consequence and the adequacy of controls |
| Failure handling | Tool errors, timeouts, partial results | Proposes failure injections | Judges whether recovery is safe |
| Injection through content | Hostile instructions in pages, files and tool output | Generates hostile content variants | Rates real exposure |
Quality area: Task success
- WHAT WE VALIDATE
- Goal completed correctly on typical and ambiguous tasks
- AI ASSISTANCE
- Drafts task variations
- HUMAN VALIDATION
- Judges whether the outcome is right
Quality area: Tool use
- WHAT WE VALIDATE
- Right tool, right parameters, results used correctly
- AI ASSISTANCE
- Clusters wrong-tool and bad-parameter runs
- HUMAN VALIDATION
- Confirms the impact of each misuse
Quality area: Planning
- WHAT WE VALIDATE
- Loops, dead ends, skipped steps, drift
- AI ASSISTANCE
- Flags repeated or circular steps in traces
- HUMAN VALIDATION
- Decides which are real defects
Quality area: Permissions
- WHAT WE VALIDATE
- Actions outside remit; approval before irreversible ones
- AI ASSISTANCE
- Suggests boundary scenarios
- HUMAN VALIDATION
- Assesses consequence and the adequacy of controls
Quality area: Failure handling
- WHAT WE VALIDATE
- Tool errors, timeouts, partial results
- AI ASSISTANCE
- Proposes failure injections
- HUMAN VALIDATION
- Judges whether recovery is safe
Quality area: Injection through content
- WHAT WE VALIDATE
- Hostile instructions in pages, files and tool output
- AI ASSISTANCE
- Generates hostile content variants
- HUMAN VALIDATION
- Rates real exposure
Where AI helps
- Drafting task variations, ambiguous goals and hostile inputs from a description of the agent’s tools.
- Clustering similar failed runs by step or tool so one cause is investigated once.
- Summarising long run traces into a short account of what the agent did, for a person to check against the trace.
- Giving runs a first-pass rating against agreed criteria, never as the final judgement.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Deciding whether an action was appropriate given the goal, the permissions and the context.
- Setting which actions need approval, and judging whether the confirmation step is enough.
- Assessing the real-world consequence of a failure, which depends on the business and its systems.
- Confirming that a failure reproduces and is not a one-off in a non-deterministic run.
- Owning the release recommendation and stating what was not tested.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Frequently asked questions
How do you test an agent that behaves differently each run?
We run each scenario several times, judge outcomes against agreed criteria rather than exact steps, and look at the traces. A QA engineer separates normal variation from failures that repeat or carry real consequences.
Do you test against our real systems?
We prefer a safe environment, with simulated or sandboxed tools for anything that changes data or spends money. If real systems are involved, we agree strict limits and approvals first.
Can you check that the agent asks for approval before risky actions?
Yes. We test that confirmation steps appear where agreed, that they cannot be skipped by rewording a request, and that they describe the action accurately.
What do we need to provide?
A description of the agent’s goals, tools and permissions, access to a test environment, and ideally the ability to capture run traces. We can help define the scenarios from there.