October 1st, 2026 by Kendra Stansel
A recent Associated Press report published by PBS NewsHour highlights growing calls for stronger, independent evaluation of advanced AI systems. As AI agents take on more complex tasks and interact with real systems, successful outputs alone may not tell the whole story. What should teams evaluate before an AI agent reaches production?
For enterprise teams, the growing discussion around AI safety and evaluation has practical implications today. AI agents can use tools, interact with external systems, maintain information across multiple steps, and take actions within larger workflows. Software quality and engineering teams therefore need ways to determine whether those agents are ready for production.
OpenAI has called for mandatory safety requirements and independent safety assessments for advanced AI. Anthropic has also announced initiatives focused on independent evaluation of frontier AI, including model evaluation, red teaming, alignment assessments, and safeguard testing.
While these discussions often focus on frontier AI, organizations deploying AI agents face a related challenge: determining what acceptable behavior looks like and gathering enough information to make an informed release decision.
AI agents should be evaluated against defined behavioral boundaries using realistic, adversarial, and edge-case scenarios. Evaluation should consider not only the final output, but also tool use, permissions, policy adherence, and behavior throughout the workflow.
Why AI Agents Require a Different Approach to Evaluation
Traditional software gives teams a relatively predictable relationship between inputs, actions, and expected outcomes. That makes scripted testing effective: establish the expected result, execute the test, and compare the actual result.
AI agents complicate that model. They can generate different responses to similar inputs, interact dynamically with external tools and data, and behave differently after changes to a model, prompt, configuration, or operating environment.
OpenAI has acknowledged this shift in its guidance on third-party evaluations. Earlier evaluations often treated models much like chatbots: provide a prompt, receive an answer, and evaluate that answer. More capable systems can operate within larger workflows, meaning their behavior depends on both the model and its environment.
Traditional Software Testing vs. AI Agent Evaluation
| Dimension | Traditional Software Testing | AI Agent Evaluation |
|---|---|---|
| Execution | Typically Predictable: Expected outcomes can usually be defined in advance | Inherently Variable: Outputs and multi-step paths can differ across runs |
| Evaluation Focus | Output Match: Compare results against predefined expectations | Behavioral Boundaries: Evaluate tool use, policies, permissions, and intended behavior |
| Testing Scope | Scripted Scenarios: Defined test cases and anticipated edge cases | Dynamic Scenarios: Adversarial inputs, prompt injection, unexpected conditions, and edge cases |
| Evidence | Execution History: Test results and run logs | Behavioral Evidence: Scenarios, observed behavior, findings, and decision rationale |
Recent evaluations have exposed cases where advanced AI systems behaved outside intended boundaries, including models obtaining unauthorized access to third-party systems during cybersecurity evaluations or extending activity beyond defined testing parameters.
Evaluation itself is becoming an engineering discipline.
How to Evaluate an AI Agent Before Production
There is no single evaluation that can prove an AI agent is safe or reliable in every situation. Instead, teams need a structured process for discovering how the agent behaves under conditions relevant to its intended use.
1. Define the Agent's Boundaries
Start by establishing what the agent should and should not be allowed to do.
Define its intended purpose, accessible data, permitted tools, authorization levels, expected behaviors, and explicit restrictions.
An agent designed to answer questions from public documentation has a very different risk profile from one capable of modifying customer records or initiating financial transactions.
Without defined boundaries, there is nothing meaningful to evaluate against.
2. Challenge the Agent with Realistic Risks
Successful task completion is only a baseline.
Introduce scenarios designed to expose weaknesses, such as ambiguous requests, conflicting instructions, unexpected tool responses, attempts to access unauthorized information, misleading context, or prompt injection.
The objective is not simply to confirm that the agent works. It is to discover where and how it fails while there is still an opportunity to address those failures.
3. Evaluate the Agent's Behavior
The final answer tells only part of the story.
Consider an agent that ultimately produces the correct response but attempts to access an unauthorized database along the way. An output-only evaluation could classify the interaction as successful while missing a serious boundary violation.
Evaluation should therefore examine what the agent did throughout the interaction, including the actions and decisions that led to the result.
4. Capture Reviewable Evidence
Document what happened during evaluation in a form that other teams can review.
Useful evidence can include:
- Scenarios evaluated
- Actions taken by the agent
- Behavioral criteria applied
- Failures or unexpected behavior
- Findings requiring human review
This gives engineering teams information for debugging, QA teams reproducible scenarios, security teams visibility into boundary violations, and governance teams a basis for reviewing release decisions.
5. Reassess When the System Changes
An evaluation reflects the system at a particular point in time.
Changes to a prompt, model, tool integration, configuration, permissions, or workflow can introduce new behavior. Meaningful changes should therefore trigger reassessment rather than relying indefinitely on previous results.
This makes AI agent evaluation part of the software development lifecycle rather than a one-time certification exercise.
Where AI Assurance Fits
The growing discussion around independent AI evaluation highlights a broader challenge for organizations deploying AI: how do you make an informed production readiness decision when the system's behavior cannot be completely predicted in advance?
AI Assurance provides a framework for addressing that challenge by bringing together the activities required to define acceptable behavior, identify relevant risks, challenge the system under realistic conditions, assess what happens, and document the findings. Independent evaluation can add another layer of scrutiny, particularly for high-risk or highly capable systems, while enterprise teams can apply repeatable assurance within their own development and release processes.
Production monitoring remains important for identifying emerging issues after deployment. AI Assurance extends that scrutiny earlier, giving teams an opportunity to uncover, investigate, and remediate potential problems before they affect real users.
From "It Works" to "We Have Evidence"
As AI systems become more capable and autonomous, successful demonstrations and basic test cases provide only part of the information teams need to make release decisions.
This is the problem SureWire™ by Inflectra is designed to help address.
SureWire dynamically evaluates AI agents against real-world, adversarial, and policy-driven scenarios to help teams surface issues such as:
- Prompt injection and manipulation
- Unintended data leakage and authorization bypasses
- Unsafe behaviors and hallucinations
- Risky or improper tool usage
SureWire's Key Findings create a reviewable audit trail of evaluated scenarios, observed behaviors, and the reasoning behind pass, fail, or partial findings.
The goal isn't to promise that an AI agent will never fail. It's to move from:
"We think it's ready."
to:
"Here is the evidence we used to make that decision."
