Start by deciding whether the work is actually a loop
Many organisations label every recurring process a loop. That hides an important design difference. A task produces an output once. A checklist makes required steps visible. A pipeline moves work through stages. A recurring cycle repeats on a schedule. A genuine loop uses evidence from one pass to change the next pass.
If no observation can change the following action, forcing the work into an agent loop adds complexity without creating learning. Keep stable, predictable work deterministic and reserve agent judgement for interpretation, variation, research, or exception handling.
- Task: one bounded output
- Checklist: required steps are confirmed
- Pipeline: work moves through known stages
- Loop: evaluated evidence changes a later pass
Frame one operating outcome
Name the beneficiary, accountable owner, desired result, consequence of failure, and evidence of success. A loop designed around a broad ambition such as improve customer service will be difficult to control. A loop designed to prepare complete, policy-compliant responses for one category of service case can be tested and improved.
The owner should be a person with authority over the business outcome—not merely the person maintaining the agent. Technical ownership and business accountability can support each other, but they are not interchangeable.
Map the current work before adding an agent
Inspect the existing SOP, informal practice, systems, data, approvals, exceptions, metrics, and hand-offs. Identify where people currently make judgements, where information is missing, and where the process relies on a screen or undocumented knowledge.
This prevents the agent from automating an incomplete description of the work. It also exposes controls that may be invisible in the happy path, such as commercial approval, customer commitments, privacy checks, or the right to alter a system of record.
Define the smallest useful loop
The first loop should have one trigger, clear entry criteria, a short set of permitted actions, observable evidence, an evaluation rule, and a feedback path. Bound it by scope, elapsed time, attempts, cost, and authority. Name the conditions that end the loop successfully, stop it safely, or escalate it to a person.
A small loop is easier to simulate with real cases, easier to evaluate, and easier to explain to the people responsible for the outcome. Expansion should follow evidence of reliability rather than enthusiasm for autonomy.
Place human authority where consequence enters
Agents can inspect, classify, compare, draft, validate, and prepare. Consequential, subjective, regulated, irreversible, or externally binding actions normally require stronger control. The system should state what the agent may decide, what it may recommend, what requires confirmation, and what it must never do.
Human-in-the-loop does not mean reading every intermediate step. It means placing accountable judgement at the point where uncertainty, risk, commitment, or exception makes that judgement valuable.
Close every pass with evidence and a hand-off
Evidence may include a source record, data comparison, policy check, test result, trace, screenshot, calculation, approval, or readback from the system of record. The required evidence should be agreed before the agent acts so completion is not based on a confident statement from the model.
The hand-off should contain the result, sources, actions, assumptions, uncertainty, exceptions, approvals, unresolved questions, and the next accountable owner. Useful learning then changes the instruction, skill, tool, context, check, or boundary for the next pass.
Pilot with representative work
Start with a small number of real, sanitised cases that include normal work, edge conditions, missing information, conflicting evidence, and prohibited actions. Run the loop with close human supervision before granting execution authority.
Measure outcome quality, cycle time, human effort, escalation quality, exceptions, cost, and user acceptance. A successful pilot demonstrates that the operating system can detect and handle failure—not only that the agent performs well on an ideal example.