/ THE SHORT ANSWER
Use deterministic automation when the inputs, rules, and actions are stable. Add model assistance when one step requires interpretation, extraction, classification, or drafting while a person or rule retains control. Use a bounded agent only when the system must plan or choose among multiple tools across a variable path and the added autonomy creates enough value to justify stronger permissions, evaluation, monitoring, budgets, and recovery controls.
- 01Start with the narrowest action space that can work.
- 02Separate reasoning from permission to act.
- 03Bound tools, data, spend, duration, and irreversible actions.
/ dotSuper point of view
Agency is not a maturity level. It is a risk-bearing architectural choice that should be introduced only when a simpler path cannot deliver the outcome.
What the evidence says
OWASP’s guidance identifies excessive agency and unbounded consumption as important LLM-application risks, alongside prompt injection and insecure output handling.
NIST’s Generative AI Profile recommends risk management across lifecycle stages and system interactions rather than treating the model as an isolated component.
A practical decision framework
The following framework is dotSuper’s operating synthesis of the cited guidance. It is designed to make the decision inspectable, not to imitate a platform ranking formula, certification checklist, or legal test.
- Rule: fixed input, fixed condition, fixed action, predictable exception.
- Assist: variable interpretation with a bounded output and explicit review or rule gate.
- Agent: variable sequence and tool choice inside constrained permissions and budget.
- Stop: unacceptable consequence, weak observability, unreliable recovery, or no accountable owner.
| Step | Decision to record |
|---|---|
| 01 | Rule: fixed input, fixed condition, fixed action, predictable exception. |
| 02 | Assist: variable interpretation with a bounded output and explicit review or rule gate. |
| 03 | Agent: variable sequence and tool choice inside constrained permissions and budget. |
| 04 | Stop: unacceptable consequence, weak observability, unreliable recovery, or no accountable owner. |
How to put it into practice
Draw every tool and action the proposed agent could access. Classify actions as read, propose, reversible write, external communication, financial, or irreversible. Start with read and propose.
Create scenario evaluations for normal, adversarial, missing-data, loop, tool-failure, and permission cases. Add time, cost, step, and action limits before production access.
- Name the accountable owner and the decision this work must enable.
- Record the current evidence, assumptions, exclusions, and next review trigger.
- Measure a useful outcome rather than treating publication or deployment as success.
What this page cannot conclude
- 01Agent and automation terminology varies across platforms.
- 02Some workflows require specialist safety, security, financial, or legal controls beyond this guide.
- 03Publication, technical eligibility, or good practice cannot guarantee ranking, referral traffic, citation, adoption, or a business outcome.
Sources
- 01OWASP Top 10 for LLM Applications 2025OWASP GenAI Security Project · accessed Aug 30, 2026
- 02Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence ProfileNational Institute of Standards and Technology · accessed Aug 30, 2026
- 03Agents GuideOpenAI Platform Documentation · accessed Aug 30, 2026
Test the workflow before funding the solution.
The AI Readiness Sprint turns one operational constraint into a ranked decision, an accountable owner, and an implementation-ready first move.
Explore the readiness sprint