How to define done for AI work that actually holds up

Deep blue light settling in the middle of a dark field

To define done for AI work, pair explicit acceptance criteria with independent automated checks before marking deliverables complete. Agents must satisfy pre-declared tests, pass verification from a separate model family, and present a clear diff before work is approved.

Why is it hard to define done for AI work?

Plausible text and clean syntax mask deep errors in AI output. A report reads well at a glance, or a script runs without throwing immediate errors, so teams mark the task complete. Underneath, missing edge cases or unaddressed policy rules remain hidden until execution.

The gap lies between completed generation and verified completion. Generating a draft takes seconds, but generation alone does not equal finished work. When teams rely on subjective human reviews as agent output scales, the review process breaks down. Reviewers get tired, skim long documents, and miss structural flaws.

When you define done for AI work, you shift from completion optimism to explicit acceptance tests. You cannot evaluate AI output by asking if it looks complete. You must test whether it meets exact requirements written down before the agent started working. If you want to know why an agent should not grade its own work, look at how fast unchecked text hides incomplete reasoning.

How do you build explicit acceptance criteria for agents?

Clear criteria form the foundation of automated verification. Before an agent starts execution, you define what success requires. These criteria detail required outputs, expected side effects, and strict policy constraints.

A vague prompt leads to ambiguous results. Explicit criteria set firm boundary tests that an automated check can evaluate without human intervention. The rules state what must be present, what must be absent, and what thresholds the work must satisfy.

In Brainis, approved strategies compile into dependency-aware missions with owners, budgets, and acceptance criteria. This structure binds each task to its requirements from the start. You can read more about setting up missions and turning a business plan into a mission graph to see how work stays aligned across teams.

Why must verification be isolated from generation?

An AI model that checks its own output faces a clear conflict of interest. Language models tend to confirm their own previous assumptions and overlook their own mistakes. Letting the generator inspect its own work produces false approvals.

To prevent this, verification must be isolated from generation. Independent quality agents review deliverables against declared criteria. In Brainis, independent quality agents verify work against acceptance criteria and can reject it. Furthermore, the producer of a deliverable never solely verifies it; high-risk work uses a different model family. When a check fails, the deliverable returns to the agent with concrete rejection details showing exactly what failed.

Example: A five-person logistics consultancy uses an automated pipeline to draft weekly client summaries. The worker agent creates the summary from log files. An independent verification agent from a separate model family checks the draft against three constraints: "all client names match the database, no missing shipment dates, and word count is between 400 and 600." The check catches two missing dates, rejects the draft, and leaves line notes. The worker agent fixes both lines in ninety seconds.

You can explore how Verify works and read our Verify rejections dogfood report to see how independent checks catch errors before humans ever see them.

Deep blue light gathering in a dark field, crossed by two thin seams of light

How does the review workflow inspect deliverables?

Even with automated checks, human inspection remains a key step for final approval. The review workflow must present deliverables in a format built for fast, precise evaluation.

In Brainis, every deliverable arrives as a reviewable diff — old versus new, inline threads, Verify findings on exact lines — before it counts as done. Reviewers do not read static text documents from scratch. They view changes side by side, read automated findings pinned to specific lines, and leave inline comments for necessary adjustments.

External client review mounts the same PR-review component in the client portal — client approval becomes a Verify input. This gives external stakeholders the same clean interface for approving work. Every comment, check result, and approval is recorded in the audit trail. Our agent fleet documentation details how these workflows coordinate across complex teams.

How do you standardize quality criteria across teams?

Defining criteria for a single task is useful, but scaling quality requires standardization. Teams need a way to reuse proven rules across projects without rewriting them for every new task.

With Brainis, your own acceptance criteria become Verify checks — authored once, composed into libraries, enforced per domain. You write security, formatting, and compliance rules once, then apply them across every mission in that domain. When a new edge case or failure mode appears, you update the central check, and every future task enforces it automatically.

Standardized checks establish a dependable baseline for quality. They allow teams to delegate work to agents without risking standards or introducing manual review bottlenecks. Look at our MCP integration guide to see how these libraries integrate with existing tools and context sources.

What to do next with your agent workflows

To define done for AI work across your organisation, start by auditing your current AI outputs. Identify where plausible text has masked incomplete work or missed requirements in recent deliverables.

Next, take your next three automated tasks and write explicit acceptance criteria before running them. Specify exact outputs, required constraints, and measurable tests. Separate your generator agents from your evaluator checks so that no model marks its own work as complete.

Finally, begin building a shared library of domain criteria. As you identify recurring mistakes, turn those lessons into permanent verification checks. You can learn more about how to set up this system on our how it works page.