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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsTo build an AI assistant that actually finishes tasks, design and test the entire workflow—not just the model’s answers. Define what “done” means, limit what the assistant can do, keep evidence of its actions, and evaluate it on representative tasks using the same tools and safeguards people will rely on.
The distinction matters because an assistant can sound confident without completing the requested work. Anthropic defines an agent as a model that directs its own processes and tool use toward a task, rather than following a fixed script. The nine checks below are a practical synthesis of published guidance, not an official standard.
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1. Define what counts as finished
Translate the user’s request into an observable end state before the assistant acts. A useful task specification names the requested result and the evidence that would demonstrate it. A polished final message is not evidence that an external action succeeded.
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- Result: What must be true when the task is complete?
- Evidence: What observable record or confirmation will establish that?
- Partial completion: Which subtasks can be reported as complete, and which remain outstanding?
- Stop condition: When should the assistant stop and ask for help instead of guessing?
Make these criteria specific enough that a reviewer—or an automated grader—could distinguish completion from an attempted action.
2. Bound the assistant’s authority
Specify the assistant’s available tools, the data each tool can access, and the actions it is allowed to take. Tool access is part of the system’s behavior: a model with permission to read a record is not equivalent to one that can also edit or delete it.
Set explicit boundaries for consequential or ambiguous actions. Depending on the task, those boundaries may require confirmation before an irreversible change, restrict access to the minimum relevant data, or route an uncertain case to a person. Define what the assistant should do when a tool fails or returns information that conflicts with the request.
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3. Test the workflow users will actually use
Evaluate the assistant through its real interface and tool setup, not only by prompting the underlying model in isolation. End-to-end behavior depends on how the model receives context, selects tools, handles tool results, encounters guardrails, and hands work off.
OpenAI’s agent evaluation documentation and its July 2025 ChatGPT Agent system-card material illustrate the use of agent-specific evaluation configurations. Those materials describe particular evaluation setups; they do not certify agents generally or establish that a particular assistant will perform well in your environment.
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Keep the test setup close to deployment: use the same tool definitions, permission boundaries, relevant instructions, and handoff paths. If the deployed assistant has a way to verify actions, the evaluation should be able to assess that too.
4. Build representative task cases
Create a task set from the work the assistant is meant to perform, including ordinary requests, incomplete information, edge cases, and situations where the right action is to stop or ask a person. Write a success criterion for each case before running it; otherwise, it is easy to judge a plausible answer as a successful task.
For complex workflows, score meaningful subtasks as well as the overall result. A single pass/fail outcome can hide whether the assistant gathered the right information but failed to take the final action, or completed one part while violating an instruction elsewhere.
NIST’s Towards Best Practices for Automated Benchmark Evaluations, announced as an initial public draft on January 30, 2026, presents preliminary guidance for evaluating language models and agents with automated benchmarks. It is a draft, not a final binding standard. Its relevance here is the need for explicit criteria and a defensible evaluation design—not a universal score formula.
5. Grade the process as well as the outcome
Check both whether the requested result was achieved and how the assistant pursued it. An outcome-only score can reward an unsafe shortcut; a process-only score can overlook that the user’s task was never completed.
OpenAI’s Evaluate agent workflows guide recommends using datasets, graders, evaluation runs, and traces to examine workflows. In practice, judge whether the assistant:
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- Selected tools suited to the task and interpreted their results correctly.
- Followed the user’s constraints and the system’s safety boundaries.
- Asked for confirmation or handed work to a person when required.
- Reached the defined end state and provided evidence for it.
Use criteria tied to the task rather than a vague impression of whether a run “looked good.” Keep the score for the final result distinct from scores for instruction-following, tool use, and handoff behavior so that a strong result does not conceal a process failure.
6. Keep traces that explain failures
Capture enough of each run to reconstruct what happened: model calls, tool calls and results, relevant guardrail events, and handoffs. A final answer alone rarely explains why a task failed or whether a safety rule intervened.
Review traces against structured criteria. For example, identify whether a failure came from misunderstanding the request, selecting the wrong tool, mishandling a tool response, or claiming completion without verifying it. This makes a regression easier to diagnose than a single overall score.
Collect only the information needed for evaluation and troubleshooting, and handle traces according to the sensitivity of the data they contain. Observability is useful only when the resulting records can be reviewed appropriately.
7. Try to break the evaluation
Test whether the assistant can appear to succeed by exploiting the tools, the benchmark, or the scoring rules rather than doing the intended task. NIST’s Cheating On AI Agent Evaluations, updated December 2, 2025, discusses agents using tools to cheat on coding and cybersecurity evaluations and the importance of standardizing agent affordances and restrictions.
Look for shortcuts that satisfy the visible grader while violating the task’s real intent. Check whether a tool exposes more access than necessary, whether an evaluator accepts an unverified claim, or whether the task setup makes a prohibited route easier than the intended one. Add adversarial cases and inspect the traces of apparent successes as well as failures.
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- Walk out of every meeting with notes ready to act on: Plaud Intelligence transcribes in 112 languages with speaker labels and turns each recording into action items, decisions, and follow-ups, structured and ready to use. Choose from 10,000+ customizable templates tailored to your role and industry
- AI summary ready before you reach your desk: Auto Transfer moves each recording to the Plaud app automatically, and AutoFlow transcribes and summarizes so your notes are ready before you are back at your desk. Upgrade anytime to Pro (1,200 min/mo) or Unlimited
- Access your AI workspace anywhere: One connected workspace across Plaud Desktop, Plaud Web, and the Plaud mobile app, so your conversations and finished work follow you everywhere
- Your conversations stay private and yours: Compliant with ISO 27001, ISO 27701, SOC 2, HIPAA, GDPR, and EN 18031, with zero data used to train AI models. Trusted by 2.5M+ professionals, including legal, medical, and business professionals handling sensitive information
OpenAI’s June 2026 article on third-party evaluations reports that human review detected reward hacking in some apparent successes in one evaluation context. That is a reason to inspect and challenge scores, not evidence of a universal rate of reward hacking.
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Preserve a reviewable trail of what the assistant did and the evidence behind important decisions. A useful audit record connects the requested task to actions, tool results, guardrail decisions, and the final completion claim. The purpose is to let a person assess the run—not to treat a model’s explanation as proof that an action was correct.
NIST’s Building Evaluation Probes into Agentic AI project page, updated May 5, 2026, describes evaluation probes that can act as adversarial verifiers and accumulate results into machine-readable audit trails. The page emphasizes visibility into agent reasoning, tool use, and gathered evidence as a way to build confidence that workflows executed correctly.
For each important action, retain the evidence needed to answer: what was attempted, what result came back, and what supports the assistant’s claim that the task is done? Keep the trail proportionate to the task and protect sensitive records.
9. Repeat evaluations when the system changes
Rerun the same task set after changes to prompts, model routing, tools, permissions, or other workflow components. Compare both task outcomes and traces over time: an unchanged completion score can conceal a new pattern of poor tool selection or skipped handoffs.
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For comparisons between two agent approaches, hold the task distribution and success criteria constant. These practical comparison axes are useful, but the cited guidance does not establish a single official scoring formula.
| What to compare | What to inspect |
|---|---|
| End-to-end results | Whether tasks reached their defined end states, including meaningful partial completion. |
| Tool use and handoffs | Whether tools were appropriate and work was escalated at the right time. |
| Instruction and safety compliance | Whether the assistant respected user constraints and system boundaries. |
| Traces and evidence | Whether reviewers can understand actions and assess completion claims. |
| Resistance to gaming | Whether apparent success depends on a loophole or unintended shortcut. |
| Consistency over changes | Whether outcomes and process behavior remain reliable across repeated runs and system updates. |
Keep the task set, scoring criteria, and relevant configuration details with each evaluation run. That makes changes easier to interpret and supports more transparent, reproducible comparisons.
Put the checks into a build cycle
- Specify completion: Write the end state, evidence, partial-completion rules, and stop conditions for each task type.
- Constrain action: Define tool access, permitted actions, confirmation points, and human handoffs.
- Assemble realistic cases: Include common tasks, edge cases, failures, and cases where the assistant should not proceed.
- Run the deployed workflow: Use the actual interface, tools, guardrails, and handoff path.
- Review results and traces: Score outcomes and process separately, then investigate unexpected successes and failures.
- Repeat after changes: Compare the same cases and criteria after modifying the model or workflow.
The practical test is not whether the assistant can produce a convincing response. It is whether the complete, permission-bounded workflow reaches the requested result, produces evidence a person can check, and behaves acceptably when it cannot proceed.
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