The rise of agentic software is creating a fundamental challenge for traditional quality assurance. For decades, software testing has been built on a foundation of predictable inputs and verifiable outputs, but these methods begin to fail when the “user” is an autonomous AI agent that can think and act for itself.
An AI agent using MCP, particularly, doesn’t follow a simple script; it dynamically assembles its own workflow by chaining together different tools in response to a given task, creating a combinatorial explosion of possible paths that is impossible to cover with pre-written, end-to-end test cases. As a result, the old model of verifying a known path to success becomes obsolete when the system itself is designed to create its own novel paths.
This challenge requires a complete reframing of the purpose of quality assurance, says Akash Agrawal, VP of DevOps and DevSecOps at LambdaTest. He argues that the focus must shift away from testing the static outcome of a known process. Instead, we must begin to evaluate the agent’s dynamic, in-context decision-making process itself. The most critical question for QA is no longer, “Did the system follow the script correctly?” but rather, “Did the agent demonstrate sound reasoning by choosing the right tool for the right context to achieve its goal?”
Putting this principle into practice requires a fundamental change in tooling and collaboration. QA engineers must gain access to the agent’s “decision logs,” which should record not only the final action but also the context it received, the tools it considered, and its rationale for choosing one. The test itself then becomes an audit of this reasoning process, with automated checks to ensure the agent’s choices align with predefined business rules and best practices.
So, How Do You Test an AI That Thinks for Itself?
The solution to this testing challenge, as proposed by Agrawal, is a new paradigm he calls “autonomous workflow validation.” This is not a single technique but a hybrid approach that combines elements from synthetic monitoring, chaos engineering, and model validation. The goal is to build a framework that continuously evaluates an agent’s ability to choose the most appropriate tool for a given context, effectively testing its reasoning rather than a static script.
Practically, this involves creating a new class of “contextual integrity tests.” These tests don’t check for a specific output. Instead, they provide an agent with a carefully crafted context and a clear goal, then audit its decision logs to verify that the chosen tool was logically appropriate for that situation. For example, a test could check if a customer service agent correctly selects the “issue refund” tool for a billing complaint versus the “escalate to support” tool for a technical bug.
This new validation model, however, is only effective if the tools themselves are designed for testability. Sean Falconer, an AI Entrepreneur in Residence at Confluent, argues that this requires a shift away from exposing large, generic APIs. Instead, he advocates for creating small, “purpose-built tools” with clear contracts and bounded inputs that are designed specifically for AI consumption. This approach introduces determinism and makes an agent’s behavior easier to evaluate.
This leads to a clear principle for engineering leaders: the more constrained and well-defined the tool, the easier it is to govern, evaluate, and ultimately, trust.
Even Your AI Needs a Supervisor
To effectively evaluate these dynamic systems, a practical approach is to treat the AI agent not as a piece of software to be tested, but as a digital employee whose work must be verified. Mike Finley, CTO of AnswerRocket, suggests a two-stage validation framework built on this principle. The first stage is to require the agent to provide “proof points” for its work. This means any facts it uses must be documented with verifiable, non-AI sources, and any decisions it makes must be accompanied by the logical steps it took to arrive at them. Just as with a human worker, this requirement for transparent reasoning often improves the quality of the work itself.
The second stage of this framework involves the use of what Finley calls “verifier” or “supervisor” AI agents. These are specialized agents whose sole purpose is to watch the work of other agents and ensure they adhere to established guidelines. This goes beyond simple accuracy; these supervisor agents are tasked with evaluating for subtle cues like tone, bias, or other qualitative measures that are critical for enterprise-grade applications.
This concept of an AI guardrail is already being implemented in production. Chad Burnette, founder and CTO of Wayfound, provides a concrete example. His platform uses an MCP tool called evaluate_session, which acts as a quality control layer for other agents. Before an agent’s response is sent to a customer, this tool can check the agent’s work for any violations of specified guidelines, including tone of voice, formatting issues, or the presence of personally identifiable information.
This model provides a clear blueprint for building responsible AI: by implementing similar supervisor agents, organizations can create a final quality gate to verify an agent’s output for compliance and safety before it ever reaches an end-user.
For engineering teams looking to implement this, the first step is to create a centralized “policy repository.” This involves codifying all critical business and compliance rules—such as brand voice guidelines, data privacy policies for handling PII, and industry-specific regulations—into a machine-readable format. This repository then becomes the knowledge base that the supervisor agent uses to validate the primary agent’s work. By mandating that every AI-driven workflow passes through this final, programmatic quality gate, organizations can ensure that all agents, regardless of their specific function, consistently adhere to the same enterprise-wide standards for safety and quality.
You Canʼt Test What You Canʼt See
These new validation frameworks for agentic AI and MCP, however, cannot exist in a vacuum; they must be built upon a solid MLOps foundation. Etan Lightstone, a product design leader at Domino Data Lab, argues that building trust in AI agents requires applying familiar operational principles. He suggests that for an enterprise with mature MLOps capabilities, trusting an agent is not enormously different from trusting a human user, because the same pillars of governance are in place: robust logging, complete auditability, and the critical ability to roll back any action when needed.
This product-centric mindset also extends to how we design and test the MCP tools themselves before they ever reach production. Lightstone proposes a novel approach he calls “usability testing for AI.” Just as a product team would run usability tests with human beings to uncover design flaws before a release, he advises that MCP servers should be tested with sample AI agents. This is an effective way to discover issues in how a tool’s functions are documented and described, which is critical since this documentation effectively becomes part of the prompt that the AI agent uses.
Furthermore, he suggests we need to build “support links” for AI agents acting on our behalf. When someone gets stuck, they can often click a link to get help or submit feedback. Lightstone argues that AI agents need similar recovery mechanisms. This could be an MCP-exposed feedback tool that an agent can call if it can’t recover from an error, or a dedicated function to get help from a documentation search. This approach treats the agent as a true user, building a more resilient and testable AI ecosystem.
To put these principles into action, development teams should treat agent interactions as first-class citizens within their existing MLOps pipelines. A practical step is to create a standardized “agent event schema” for logging, ensuring that every action an agent takes is captured with the same rigor as any other critical system event. And, teams can establish a “pre-release agent sandbox” as a required step in the CI/CD process. In this sandbox, all new or updated MCP tools are automatically tested against a suite of diverse test agents to validate their usability and robustness before they are exposed to production systems.
So, Where Does QA Go From Here?
The shift to agentic AI fundamentally recasts the role of quality assurance from a final, pre-deployment gatekeeper to a continuous, integrated partner in a dynamic system. The new playbook for AI quality is no longer about writing scripts for every possible path an application can take. Instead, it is about creating a resilient ecosystem that can evaluate, govern, and trust autonomous decision-making in real time through new paradigms like autonomous validation, supervisor agents, and a foundation of strong MLOps.
Agrawal of LambdaTest emphasizes that this evolution is not merely a technical necessity but a core business imperative. As he and other leaders in this space argue, the work of designing these new validation frameworks is about building the essential guardrails for business and compliance in an AI-native world. It is this discipline that will ultimately separate experimental AI projects from the scalable, secure, and trustworthy enterprise systems of the future.
