Who Tests the AI Agents?

How to Assure the Quality of Agentic AI

Objentis | Drvless

AI agents no longer just follow predefined steps – they decide for themselves how to reach a goal. That makes them valuable in customer service, back-office work and process automation. For quality assurance, it means one thing: testing AI agents takes more than classic test cases.

An agent that picks one workflow today may take a different, equally plausible path tomorrow – with exactly the same input. A test case with a fixed sequence and a single expected result then reports a failure, even though the agent did its job. Or it misses a real problem because the end result happens to look right.

In this article, you’ll learn:

why classic testing hits its limits with agentic AI,
what a behavior-based testing approach checks,
why visual, non-invasive test automation is a strong fit,
and who in the organization owns the quality of AI agents.

Agentic AI has reached production

AI agents are no longer a lab experiment. According to LangChain’s State of Agent Engineering 2026, 57% of surveyed organizations already run AI agents in production. At the same time, respondents name quality – especially consistency and hallucinations – as one of the biggest barriers. That’s the tension: companies are rolling out agents faster than they are putting the right testing methods in place.

Why classic tests aren't enough for AI agents

Traditional testing rests on a simple assumption: same input, same sequence, same result. For interfaces, permissions and stable business rules, that remains essential.

AI agents add a new layer: variable behavior. They choose tools autonomously, plan intermediate steps and react to context. Classic testing runs into four limits:

Variable behavior: The same prompt or trigger can lead to different actions.
Many paths to the goal: An agent can reach a correct result in different ways.
Limited transparency: With third-party agents or SaaS tools, you often have no access to source code, logs or decision logic.
Scale: One agent can be watched. Dozens of agent workflows running in parallel can’t be monitored without automation.

Classic tests stay important – but they need to be complemented by methods that assess an agent’s actual behavior and risks.

From outcome testing to behavior testing

The key question is no longer just “Is the result correct?” It’s: “Did the agent reach the goal, follow all the rules and avoid any unauthorized action?”

A behavior-based testing approach looks at four dimensions:

Dimension	Test question
Outcome	Was the business goal achieved correctly?
Compliance	Were business rules, permissions, approval limits and security policies respected?
Interaction	Did the agent use the right systems, data and tools appropriately?
Traceability	Can its way of working be documented and audited?

Example: an agent handling customer complaints

An AI agent reviews incoming complaints, gathers information and proposes a resolution.

Allowed: The agent uses different information sources or chooses a different handling path depending on the case.
Not allowed: It exceeds a defined refund limit without approval, processes personal data in breach of GDPR, accesses data sources without authorization, or closes a case without documentation.

A classic test would only check whether a credit note was issued. A behavior test checks whether the path to it stayed within the allowed boundaries.

Three jobs of test automation for AI agents

Whether human or machine, anyone testing AI agents has to do three things:

Observe: Capture the interface and system behavior the way a person would see the process.
Evaluate: Check whether outcome, rules and risk limits were met – even when the path varies.
Trace: Document processes, decisions and deviations so they can be analyzed and audited.

Visual test automation with Drvless

This is where Drvless comes in – Objentis’ visual AI automation agent. Drvless recognizes on-screen content with AI, operates applications via mouse and keyboard just like a person, and prepares the results for test management.

The Ideation module generates test cases from existing information and, once approved, hands them straight to automated test execution.

Because Drvless can run in production, testing and 24/7 application monitoring come together: what was verified before go-live is continuously watched in operation.

Why visual, non-invasive testing matters for AI agents

Where the need is greatest

When agents or target systems offer limited transparency, a visual approach gives you an independent end-to-end view of what’s really happening:

No internal access needed: Neither the agent’s source code nor its API documentation is required.
Technology-agnostic: Web app, SAP front end or legacy screen – all it needs is a visible interface.
Close to real use: It tests what users actually see and which actions the agent triggers in the live environment.

One caveat: visual tests don’t replace other test layers. They don’t check internal data flows, API calls or security mechanisms. They deliver the most value as an additional layer alongside API, security and data testing. For a comparison of the two approaches, see Drvless vs. Code-Based Testing.

Agentic AI pilots moving into production: Once agents touch real business processes and customer data, the bar for control and evidence rises. Automated monitoring becomes a decisive quality factor.
Customer service and back office: Agents handle requests, fill in forms, transfer data and prepare decisions that directly affect customers.
Regulated industries: Banks, insurers and critical infrastructure must demonstrate risk management, documentation and monitoring. Frameworks such as DORA, NIS2 and the EU AI Act make this mandatory for many companies.

Who is responsible for the quality of AI agents?

The business unit, IT and architecture, IT security, data and AI teams, or quality assurance? All of them contribute – what matters is clearly defined roles and responsibilities.

Deloitte’s The State of AI in the Enterprise 2026 shows there’s work to do: adoption of agentic AI is growing faster than the controls around it. Only about one in five companies has a mature governance model for autonomous AI agents.

Visual test automation makes sure an AI agent works correctly before it goes live. Monitoring makes sure it stays that way in operation. Objentis combines both – with tools for visual test automation, process automation and 24/7 monitoring, plus consulting on testing AI systems.

Conclusion: if the agent is a black box, the test can't be

Classic tests remain indispensable. But for AI agents, outcome- and sequence-based test cases alone fall short, because paths and results vary. What counts is whether an agent makes its decisions strictly within defined boundaries.

A visual, behavior-based testing approach complements existing quality assurance: it observes agents in their real working environment and evaluates outcomes and rules instead of rigid step sequences. Drvless does this by combining AI-based visual recognition with mouse and keyboard interaction – no plugins, no SDKs.

Datum (2. November 2026)