Use case
AI agent oversight and quality monitoring
Once a business runs five or six agents, it has recreated the problem it had with people: nobody is checking the work. An oversight agent reads what the others produced this week, grades it against each one's own job spec, and writes up what is slipping.
Who checks the work of AI agents?
Another agent can. An oversight agent samples each agent's outputs on a schedule, grades them against that agent's own job spec, and reports what is drifting. On Qoren it runs in the same office as the agents it watches, with read access to their outputs, and any change it suggests to another agent is a proposal a human approves.
The problem
A single agent is easy to supervise, because someone still reads its output. At five or six the reading stops, and the failure mode is quiet: the morning brief gets vaguer week by week, the competitor watchdog has flagged nothing in three weeks, the support agent's escalation rate doubles after a knowledge-base edit nobody connected to it. Nothing broke loudly enough to notice, which is exactly why it is still happening in month four.
How an agent handles it
- Read the outputs and the job specs of every agent in the office.
- Sample each agent's work on a weekly schedule and grade it against its own spec.
- Catch quality drift: vaguer summaries, dropped sections, a rule that stopped being followed.
- Notice silence and go check whether a quiet watchdog is asleep or its sources are genuinely quiet.
- Compile one Monday memo covering every agent's week.
- Propose staffing changes, such as retiring a task or revising a skill, with the diff attached for approval.
Why it sells
- Quality drift surfaced while it is still small.
- One weekly read that covers the whole agent office instead of five inboxes.
- A written record of what each agent produced and what was changed about it.
How it runs, step by step
Give the overseer read access
The agents you want reviewed run as one office sharing a single environment, and the overseer joins it as a member with read access rather than its own separate island. It can see what the others sent this week and, just as importantly, the job spec each one runs on: the skill, the instructions, the schedule, and the rules about when to escalate. Reviewing an output without its spec only produces opinions, so the spec is the standard it grades against.
The weekly sample
On a schedule, typically late Sunday or early Monday, the overseer pulls a sample of each member's outputs from the past week rather than every artifact. A sample is what makes this affordable and repeatable: five briefs read closely tell you more about a reporter than two hundred skimmed, and the sample is drawn fresh each week so no agent learns which day it is being watched.
Grade against the job spec
For each sampled output it asks the spec's own questions. Did the morning brief include the sections it promises? Were claims linked to sources? Did the support agent escalate the refund it was told to escalate? Grades come out as a short note and a rating per agent, with the sampled output quoted underneath, so a human who disagrees can see exactly what was read and overrule it.
Drift checks and going to verify
Grades from earlier weeks are the baseline, so the interesting signal is the trend: a reporter scoring lower on specificity three weeks running, an escalation rate that doubled right after a knowledge-base update. Silence gets the same treatment. A watchdog that flagged nothing in three weeks is either asleep or correct, and the overseer distinguishes the two by going to a few of that agent's sources itself and checking whether there was anything to find.
The Monday memo
Everything lands in one memo, the standup the humans stopped doing: a paragraph per agent covering what it produced, how it graded, what changed since last week, and anything it wants a decision on. It arrives in the inbox or channel you point it at, at the same hour every week, so the whole agent workforce takes one read instead of five.
Proposals and the approval that applies them
The memo ends in staffing proposals: retire this scheduled task, it has found nothing in ninety days; tighten the reporter's skill so it names sources again, revised text and diff attached; move this agent from draft to send now that ten weeks of drafts went out unedited. Each proposal is a proposal. The overseer never edits another agent silently, and a human approving the diff is what applies the revised skill, which keeps one person accountable for what the office is instructed to do.
When this is not the fit
This is management overhead, and it only pays for itself once there are enough agents that nobody is reading them all. For one or two agents a person already reviews, it is a layer of ceremony you do not need yet. It also grades outputs against their specs, which is not a guarantee against every error: it samples rather than reads everything, it can be wrong about a grade, and a spec that was vague to begin with gives it a vague standard. High-stakes output still needs a human review at the point where it is used.
Templates to deploy for this
Start from a ready-made agent and tailor it to the client. Each one runs on a managed environment, online on schedules and triggers.
Founder Os
The operating cadence good companies run on — quarterly goals, a Monday plan of three bets, a daily pulse that only speaks when something slips, a Friday review with your real numbers, decisions graded against what actually happened.
Contract Redliner
Drop a contract in the inbox — get back a risk summary and a redline draft checked against your standard positions, before you pay a lawyer to read it.
Inbox Manager
Inbox triage every 30 minutes — urgent flagged, newsletters archived, replies drafted, nothing ever auto-sent
Security Hygiene Auditor
The boring security checks nobody runs, run monthly — leaked credentials, email auth, MFA gaps, dependency alerts on your own assets, each with the exact fix.
Frequently asked questions
The overseer re-reads a sample of that agent's outputs against the job spec the agent runs on, and answers the spec's own questions: were the required sections there, were claims sourced, was the escalation rule followed, is it more or less specific than a month ago. The result is a short written note and a rating per agent with the sampled output quoted, not a hidden score.
Related use cases
Deploy this for your clients.
Pick a template, configure it per client, and go live in minutes. Plans from $39/mo.
Get started