Adding AI agents to a team feels like a free hire — until you realize you've become their manager. You approve their output, re-check their work, and quietly redo the parts that were "done" but weren't.
Here's a real shape of the problem. An agent is asked to fix a checkout bug. It edits the code, the diff looks reasonable, it reports done. In most setups that's the end — until a customer can't pay two days later. The agent's "done" meant the model stopped typing. Nobody ran the checkout.
A manager wouldn't accept that, and neither should your tooling. The fix isn't "trust the AI more" or "check everything yourself" — it's a bar for done that scales:
- Machine-verified where possible — the test passes, the endpoint returns 200, the number reconciles. Cheap, objective, non-negotiable for anything checkable.
- Reviewed where it can't be machine-checked — a second agent or teammate judges it against the acceptance criteria.
- Self-reported only as a labeled last resort, never the default.
The point isn't bureaucracy. It's that "done" stops being a word an agent says and becomes something the work actually cleared — so you can stop babysitting and start trusting the output.
How to set acceptance criteria that scale
The reason teams end up babysitting their AI agents isn't laziness — it's that nobody defined what "done" means before the work started.
Acceptance criteria don't have to be elaborate. They have to be specific enough that an agent (or a second reviewer) can check them without asking you. Two questions to answer for every task before you delegate it:
What would prove this is finished?
For code: the tests pass and the feature works end-to-end in a real environment. For content: it covers the required topics and matches the brief. For data analysis: the numbers reconcile with the source and the conclusion is stated explicitly. If you can't answer this, the task isn't ready to delegate.
What's the failure mode I'd catch in review?
This is the most useful question. If you were going to re-check this yourself, what would you look for? That list is your acceptance criteria. Write it down before you hand the work off — not after you get output back that doesn't meet it.
Once you have specific criteria, the review becomes mechanical: did the work clear the bar, yes or no. You stop relying on judgment at review time because the judgment happened when you set the criteria.
What to automate first
Not all work should be delegated to AI agents immediately. The highest-leverage place to start is work where machine verification is possible — because that's where you can fully remove yourself from the check.
Start with outputs that have objective signals. Code that runs. API endpoints that return the right status. Reports where the numbers can be reconciled against source data. These are outputs where a machine can check the work before you see it. You don't need to build this — you need to ask: does the agent's output have a checkable signal? If yes, that check should happen automatically.
Move to structured outputs next. Content that follows a template, data that goes into a defined schema, summaries that need to cover specific items. These are harder to machine-check, but a second agent can verify them against a checklist. "Does this email cover the three points in the brief?" is a question a reviewer agent can answer without your involvement.
Keep unstructured judgment for yourself. Strategy calls, tone decisions, relationship-sensitive communications — anything where the acceptance criteria can't be written down precisely enough for an agent to check. Don't try to automate these. Spend your review time here, where your judgment actually adds something a machine can't.
The pattern: work that can be machine-verified → automate the check. Work that can be specification-checked → automate with a reviewer agent. Work that requires human judgment → keep it human.
The management layer most teams are missing
Here's what the best teams building with AI have figured out: AI agents need a manager, not just a prompter.
A prompter describes what to do and waits for output. A manager owns the outcome — assigns the work, tracks it through completion, verifies that it actually cleared the bar, and surfaces only what genuinely needs human attention.
Most AI tooling is built for prompters. You write the prompt, the agent runs, you get output, you check the output. The entire verification loop is on you.
The teams that escape the babysitting trap have added a coordination layer between themselves and their AI agents: something that assigns work with real acceptance criteria attached, tracks progress, runs the verification check, and flags issues before they reach the team.
That layer is what Tycoon is built to be. One AI manager — Astra — that owns the work from assignment through verified completion, so the output that reaches your team is actually finished.
The goal isn't to trust AI more. The goal is to build a setup where trust is earned by the work, not assumed from the agent.
Manage your team's AI work without babysitting →