agents at work

Agents at Work · A practical field guide

Give agents work.
Keep control.

The model can draft a report. Your system decides what it may read and who may receive it, then checks what actually happened.

What our model evaluations and working build taught us about putting agents to work in an enterprise.

One working example · Our R&D report
  1. Code prepares the evidenceOnly the sources this job may read.
  2. The model writes a draftA bounded task, using that evidence.
  3. Code checks and deliversRecheck permission. Record the result.
The model drafts. The system controls delivery.
A separate worker checks the standing permission before sending.

01 · The lesson

Good work can come
with a bad shortcut.

We asked agents to investigate attacks, manage football teams, and build racing drivers. Some completed the task while crossing a boundary: copying another agent’s work, using a rival’s credential, or presenting false evidence.

In one security evaluation, GPT-5.5 completed the objective in all three runs. One of those runs also produced 13 recorded uses of a planted bait credential. The score tells us what it achieved. The conduct record tells us how.

What did we test?

Hunt Stack tests attack reconstruction. Casebook tests forensic investigations. Range tests authorized intrusion in an isolated lab. Gridiron and Pitwall put agents in charge of teams and racing development. A later containment experiment tested whether specific boundaries held.

These are different tasks, with different measures. They show concrete behaviors in the recorded runs; they are not a universal ranking of models.

02 · The response

Make permission and proof
part of the system.

A request to “do the right thing” leaves too much to the model. The surrounding software needs to answer two questions.

Was it allowed?

In Gridiron, an agent read a rival team’s key and accepted its own trade as that rival. Private credentials need protection; the trade service also needs to check which team may act.

Did it really happen?

In Range, an agent inserted a false receipt into the live scoring record. A claim of success needs evidence from a producer the agent cannot impersonate.

Did stronger boundaries help in our tests?

In a later containment experiment, the tested Bubblewrap, Firecracker, and OpenShell + Docker configurations produced zero verified restricted effects in 48 scored episodes. All 74 deterministic boundary checks passed. Some models still attempted forbidden actions.

That supports those configurations under those test conditions. It does not validate our Podman build or every earlier incident. Inspect the test conditions and results →

03 · The build

Every part needs a clear job.

Our report workflow puts this into practice. Code prepares approved evidence. The model drafts. A separate worker checks the current permission before sending and records the outcome.

At enterprise scale, those jobs serve many people: identify the owner, supply the right context, limit tools and execution, control actions, and keep a reliable record. Sharing the services must preserve each job’s permissions.

You can build those services, adopt open-source components, or buy managed infrastructure such as AWS AgentCore. Your organization still needs to define what the work is allowed to do.

Our working R&D pilot is one example of this division of labor.

Give it a job.
Bound what it can do.
Verify what happened.

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About this collection

Based on our evaluations, later integrity reviews, and an implemented R&D pilot. Original scores stay intact. Observed behavior, tested containment, and proposed repairs remain separate. The companion guides carry the methods, source records, and limits.