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To build an AI assistant that actually finishes tasks, define an observable finish line, limit what the assistant can do, and evaluate the complete workflow—not just the model’s final answer. A confident response is not proof that an external action happened. The checks below help engineers make completion verifiable, failures diagnosable, and changes safer to ship.
An AI agent is more than a model that generates text. Anthropic’s April 9, 2026 article, “Trustworthy agents in practice,” defines an agent as a model that directs its own processes and tool use to accomplish a task rather than following a fixed script. That distinction matters: when an assistant can choose tools and steps, its behavior depends on the model, the surrounding software, permissions, and handoffs working together.
The nine checks here are a practical synthesis of guidance from OpenAI, NIST, and Anthropic; they are not a published standard or a guarantee of performance.
1. Define what “done” means
Translate the request into a result that can be checked. Before execution, identify the expected end state and the evidence that would demonstrate it. A natural-language reply such as “I’ve updated the record” is not sufficient evidence if the system has not verified that the record changed.
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- Specify the outcome: State what must be true when the task is complete.
- Name acceptable evidence: For an external action, this might be a successful tool response or a verified change in the relevant system.
- Define partial completion: Identify which subtasks may succeed or fail independently, and what the assistant should report if only some are complete.
- Set stop conditions: Decide when missing information, an error, or an ambiguous result requires a question or handoff rather than another attempt.
For example, an instruction to update a customer record should not be graded as complete just because the assistant drafted the change. The completion condition should distinguish the proposed edit from a confirmed update.
2. Bound the assistant’s authority
List the tools the assistant may use, the data each tool can access, and the actions each one permits. Grant only the authority needed for the intended tasks. Treat the tool scaffold, permissions, and confirmation rules as part of the system being evaluated, not as background configuration. NIST’s December 2, 2025 article, “Cheating On AI Agent Evaluations,” discusses how agent affordances and restrictions can affect evaluation outcomes.
- Require confirmation for actions with significant or hard-to-reverse consequences.
- Provide a human handoff when the assistant lacks permission, the request is unclear, or a policy requires review.
- Separate read access from permission to change or submit data where the workflow allows it.
- Make denied actions and escalation paths explicit so the assistant has a safe alternative to improvising.
A narrow set of tools can reduce the damage from a mistaken action, but it can also prevent legitimate tasks from completing. Evaluate the permissions the deployed assistant actually receives; a test run with broader or different access may not represent production behavior.
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Run evaluations through the same interface, tool connections, guardrails, and handoff paths that users will rely on. A model-only test cannot show whether the deployed assistant selected the right tool, passed the right arguments, handled a tool error, or stopped for confirmation. OpenAI’s “ChatGPT Agent System Card – Expert Deep Dives,” published July 17, 2025, describes agent-specific evaluation configurations and grading; it is an account of that system’s setup, not a general certification for agents.
Keep the evaluated configuration identifiable: record which model and prompt were used, what tools and permissions were enabled, and which application or orchestration changes were in effect. Without that context, a result may not be reproducible or comparable to a later run.
4. Build representative task cases
Use tasks drawn from the work the assistant is intended to handle, including ordinary cases and the complications users actually encounter. Each case needs a clear expected result and a way to judge it. Include missing details, conflicting instructions, tool failures, and cases that should trigger a clarification or handoff when those situations are relevant to the intended workflow.
For multi-step tasks, score meaningful subtasks as well as the overall result. A single pass-or-fail label can hide whether the assistant completed most of the work but failed at one consequential step—or reached the end through an unsafe shortcut. NIST’s January 30, 2026 announcement describes NIST AI 800-2 as an initial public draft offering preliminary best practices for automated benchmark evaluations of language models and agents; it is not a final, binding standard.
5. Grade the process as well as the result
Check both whether the requested outcome was achieved and whether the assistant got there appropriately. OpenAI’s “Evaluate agent workflows” guide recommends using traces, graders, datasets, and evaluation runs to review agent workflows. This is vendor documentation about evaluation design, not independent proof that a particular product performs well.
Useful process questions include whether the assistant selected a suitable tool, followed the relevant instructions, respected confirmation requirements, and handed the task off at the right time. A correct end state reached by bypassing a required review is not a clean success. Conversely, an assistant that stopped safely when a required permission was unavailable should be assessed against that task’s stated criteria, rather than treated as if it completed the unavailable action.
6. Keep traces that explain failures
Capture enough of each run to reconstruct the workflow: model and tool calls, relevant inputs and outputs, guardrail decisions, errors, handoffs, and the completion evidence. Use a consistent format so runs can be inspected and compared. A final answer alone usually cannot show whether the assistant verified a result or merely claimed success.
Grade traces against structured criteria to locate the point of failure. For example, distinguish an incorrect tool choice from a rejected tool call, a misleading success response, or an incomplete handoff. This makes it easier to tell whether a regression came from the model, prompt, tool behavior, permissions, or another workflow change.
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Keep trace collection proportionate to the data involved. Preserve the information needed to audit the task while applying the organization’s privacy and access controls to sensitive content. Visibility into actions and evidence need not mean treating a model’s private internal reasoning as the audit record.
7. Try to break the evaluation
Do not assume that a high score means the assistant followed the intended path. Look for ways it might pass by exploiting overly broad permissions, loopholes in the task, or shortcuts that satisfy a grader without satisfying the user’s actual request. NIST’s “Cheating On AI Agent Evaluations” addresses risks of agents using tools to cheat on coding and cyber evaluations and recommends standardizing agent affordances and restrictions.
- Check whether the assistant can alter, skip, or fabricate the evidence used to grade a task.
- Test cases where a superficially successful result conflicts with an instruction or required review.
- Review whether the grader rewards the requested outcome or an easy-to-game proxy.
- Run adversarial cases against the same permissions and tools that the ordinary evaluation uses.
OpenAI’s June 2026 article, “A shared playbook for trustworthy third party evaluations,” reports that human review detected reward hacking among some apparent successes in one evaluation context. That is a reason to scrutinize apparent successes, not evidence of a universal rate of reward hacking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Make actions auditable
Preserve an action trail that lets an authorized reviewer assess what the assistant did and what evidence supported its decisions. NIST’s project page “Building Evaluation Probes into Agentic AI,” updated May 5, 2026, describes probes that can act as adversarial verifiers and accumulate results into machine-readable audit trails. Its stated goal is greater visibility into agent decisions and the evidence gathered during execution.
For each consequential step, make it possible to identify the action, the relevant tool result, and whether the assistant verified the expected state. An audit trail should support review of the workflow without implying that a record of tool activity alone proves the user’s goal was achieved.
9. Repeat evaluations when the system changes
Rerun the same task set after changes to the prompt, model, routing, tools, permissions, or guardrails. Compare outcome quality and trace-level behavior so a new version is not judged only by whether a few visible examples still work. Keep the task set, grading rules, and configuration stable enough to make comparisons meaningful, and document changes that affect the results.
When comparing two assistant approaches, use the same task distribution and evaluate these practical dimensions. They are comparison axes, not a universal scoring formula endorsed by the cited sources.
| Dimension | What to compare |
|---|---|
| Outcome quality | Whether tasks reached the defined end state, including partial completion where the task criteria allow it. |
| Tool use and handoffs | Whether tool choices were suitable and confirmation or human review occurred when required. |
| Instruction and safety compliance | Whether the assistant followed task instructions and stayed within its permissions. |
| Observability | Whether traces and evidence let reviewers understand the action sequence and diagnose failures. |
| Resistance to evaluation gaming | Whether apparent success depended on exploiting permissions, task design, or a weak grader. |
| Consistency over time | Whether outcomes and workflow behavior remain dependable across repeated runs and system changes. |
Benchmark practices should support validity, transparency, and reproducibility, as emphasized in the NIST AI 800-2 initial public draft. If a score improves while traces show more policy violations, weaker verification, or unreliable handoffs, the change should not be treated as an uncomplicated improvement.
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