Acceptance criteria for AI agent workflows are the observable conditions a task output must meet before it counts as complete. They name the artifact you expect, the evidence that supports it, the boundaries it must respect, the person who accepts it, and what the agent should do when it cannot meet those conditions. Writing them down turns an unanswerable question — is this good enough? — into a decision a reviewer can make in minutes.
This article is for practitioners who already run agents, whether through hosted tools or locally, and who keep finding that fluent output is not the same as finished work. It covers what to specify, how to phrase it, and where the approach has limits.
Why vague task descriptions fail
The failure mode is documented in practitioner writing. In a May 2026 post on AgentCenter, the author describes giving an agent the task “Draft a weekly summary report.” The agent produced something, but he could not tell whether it was what he wanted without spending about twenty minutes reviewing it. With three agents running, review time exceeded the time the agents saved. His conclusion was not that the agents were broken, but that he had never defined what done looked like (AgentCenter).
A September 2026 guide from Commonly frames the same problem more generally: fluent output can look complete before it is useful, supported, in scope, or safe to act on (Commonly). That gap is what acceptance criteria are meant to close.
The five components of a reviewable criterion
Commonly defines AI agent acceptance criteria as observable conditions that tell a team whether an agent’s result is ready for its next handoff. Its guide breaks those conditions into five parts:
- The expected artifact. What the agent produces — a file, a diff, a message, a table — and in what shape.
- The facts or checks that support it. Evidence a reviewer can inspect, such as source links, a declared list of checks that ran, or the raw input a claim came from.
- Scope and boundaries. What the agent must not do or touch, including systems it must not call and content it must not publish.
- The review owner. The named person who accepts or returns the result.
- A safe stop condition. What the agent should do when it cannot meet the criteria.
Two of these are easy to skip and expensive to omit. Without a named owner, nothing is ever formally accepted or rejected — output simply accumulates. Without a stop condition, an agent that cannot find supporting evidence has no instruction other than to keep going, which is how plausible-looking filler gets produced.
Specify the output format before the content
AgentCenter’s guide frames the work as a five-step process; the step it treats as first is stating the output format explicitly. The distinction it draws is between “summarize the feedback” and “produce a bullet list of 5 to 10 items, each starting with the category name in bold, followed by a one-sentence description.” Likewise, “write a report” becomes “write a 300 to 500 word Markdown report with three sections: Summary, Key Findings, and Recommended Actions.”
The reason is practical rather than stylistic: agents follow formatting instructions well when those instructions are specific. Format is also the cheapest thing to check. A reviewer can confirm a word count and the presence of named sections in seconds, which frees attention for the harder question of whether the content is right.
Define the conditions that must be true
The second step in AgentCenter’s process is to list the things that must be true for the output to be considered correct. In practice, these conditions are where the evidence and boundary elements from Commonly’s five-part definition get written down. A condition should be phrased so that a reviewer can mark it met or unmet without interpretation — “every figure in the summary is traceable to a row in the supplied CSV” rather than “the summary is accurate.”
Teams coming from software delivery often borrow the Given/When/Then phrasing used for user stories, because it forces a condition into a testable shape: given this input, when the agent runs, then this observable result must be present. The phrasing is optional; the testability is not.
A reusable task template
A short template makes the five components hard to forget. Adapt the field names to whatever your workflow tool calls them.
Task: <one sentence>
Artifact:
- Format: ...
- Length or size: ...
- Required sections: ...
Evidence:
- Each claim must cite ...
- Unsupported items go under ...
Boundaries:
- Allowed inputs: ...
- Do not: ...
Review owner: ...
Stop condition:
- If <condition>, stop and report ...
An illustrative example
The following rewrite is a design example, not a tested configuration. It shows how the five components can turn one vague task into something reviewable.
Original task: “Draft a weekly summary report.”
- Artifact: A 300 to 500 word Markdown report with three sections — Summary, Key Findings, Recommended Actions.
- Evidence: Every item in Key Findings cites the source file and line it came from. Items without a source are listed separately under Unverified.
- Boundaries: Use only the material supplied in the week’s folder. Do not call external services. Do not publish or schedule anything.
- Review owner: The team lead accepts or returns the draft.
- Stop condition: If fewer than three source items are available, stop and report which inputs are missing instead of writing the report.
Notice what changed. The task now has a checkable shape, a named decider, and a defined failure behaviour. The reviewer’s job shifts from guessing intent to verifying conditions.
Where criteria attach in a workflow
Acceptance criteria matter most at handoffs. In a chain such as research → draft → edit → publish, each stage’s criteria become the next stage’s input contract. Commonly makes this explicit: the criteria tell a team whether a result is ready for its next handoff, and its examples differ by role. A research brief needs source-linked findings and labelled uncertainty. A coding change needs a bounded diff and declared checks. A support triage note needs the facts the next owner can use without a promise the agent cannot keep.
For a WordPress and automation pipeline, the handoff is usually from agent to human editor. The criteria should therefore state that the draft is not published, name the person who reviews it, and describe what a reviewable draft contains — for example, a title, a body, and a list of any claims that still need a source. The automation can create the draft and stop there; publication remains a human decision.
What acceptance criteria are not
Commonly is careful about the limits of this practice, and the cautions are worth repeating:
- A written checklist is not an enforcement control. Making criteria and results visible in a shared workspace does not prevent an agent from doing something else.
- An agent’s self-assessment is not a release approval. An agent can report that it believes it met the criteria; that report does not authorise the result.
- A task description does not grant technical permission. Naming an outcome in a task does not give the agent access rights or scopes.
- Criteria are not a replacement for tests. As AgentCenter notes for software engineers, user stories have acceptance criteria and tests are expressions of them — the criteria state the condition, the test checks it.
Failure patterns to watch for
Most weak criteria fail in one of five recognisable ways:
- Format without evidence. The output looks right but nothing supports its claims.
- Evidence without boundaries. The agent can justify its claims but also reached outside the intended scope to get them.
- No stop condition. Missing inputs become invented inputs.
- No review owner. Output sits in a queue because nobody is accountable for accepting it.
- Adjectives instead of conditions. “High quality,” “engaging,” or “concise” cannot be marked met or unmet, so they are ignored in practice.
Local agents and offline constraints
The same structure applies when the agent runs locally, but the stop condition carries more weight. A local model working from a fixed document set has no way to fill a gap except by generating text, so an explicit instruction to report missing inputs rather than substitute for them is the difference between a usable draft and an unverifiable one. The criteria can live in the same place as the rest of the workflow configuration — a system prompt, a task template, or a workflow step — as long as they are visible to the reviewer as well as the agent.
Next step
Pick one agent task you run repeatedly and rewrite it using the five components: artifact, evidence, boundaries, review owner, stop condition. Run it once and compare how long review takes against the vague version. If you hit a case where the criteria were met but the output was still unusable, that is useful information — it usually points to a missing condition rather than a missing capability.
Readers working through this on local models or WordPress automation are welcome to share their task description, the criteria they wrote, and what the agent actually returned in the community discussion.
FAQ
Can an AI agent write its own acceptance criteria?
An agent can draft them, but Commonly's guidance is explicit that an agent's self-assessment is not a release approval. The acceptance decision should belong to a named human reviewer.
Do acceptance criteria replace testing?
No. AgentCenter notes that for software engineers, user stories have acceptance criteria and tests are expressions of them. The criteria state the condition; a test or check verifies it.
How specific should the output format be?
Specific enough that two reviewers would agree on whether it was met. AgentCenter's examples use concrete constraints such as a 5 to 10 item bullet list or a 300 to 500 word Markdown report with three named sections.
What should an agent do when it cannot meet the criteria?
It should follow the safe stop condition: stop and report the gap rather than filling it. Without that instruction, missing inputs tend to become invented ones.