Make agents useful. Keep the work reliable.

AI Agent Operations is the practice of defining, running, checking, and improving agent-assisted work. Start with one useful job, a clear boundary, and an output you can verify.

Build your first workflow Assess your readiness

01 / Define the job

02 / Control access

03 / Run and verify

04 / Measure and improve

The operator’s field guide

Guide 1 · A one-page workflow contract and a supervised launch plan.

Launch your first agent workflow

Choose a bounded job, define a useful output, and move from a supervised dry run to a repeatable routine.

Explore Guide 2 · An action-by-action permission and review plan.

Set permissions and human review boundaries

Separate instructions from enforced controls and decide which actions require a person to approve them.

Explore Guide 3 · A recovery procedure that distinguishes safe retries from uncertain external effects.

Recover a failed agent run without duplicating work

Use checkpoints, bounded retries, and a reviewable recovery record when a recurring agent task fails.

Explore Guide 4 · A repeatable evaluation worksheet with explicit failure cases.

Evaluate an agent workflow before trusting it

Build a small repeatable test set, define acceptance criteria, and compare changes using useful outputs and intervention time.

Explore Guide 5 · A transparent cost estimate you can compare with a manual baseline.

Measure cost per successful agent workflow

Include failed attempts, human review, and fixed costs when deciding whether a recurring agent workflow is useful.

Explore Guide 6 · A concise handoff packet and a memory maintenance routine.

Keep useful memory and clear agent handoffs

Separate durable instructions, current task state, and evidence so a new run can continue without replaying the entire conversation.

Explore

Harnesses