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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Keep predictable workflow steps deterministic: triggers, permissions, explicit business rules, approval gates, and final validation. Use AI for work that needs language interpretation, synthesis, classification, or context-sensitive suggestions. Put deterministic checks around AI inputs and outputs. An agent is warranted only when the task must choose its next steps dynamically across tools or data—and only when oversight, authorization, monitoring, and a way to stop or recover it are in place.
How to choose the right level of automation
Decide based on the workflow, not on whether a tool is marketed as an “AI agent.” Digital NSW’s October 2025 guidance distinguishes fixed, repeatable automation from human-led AI assistance and agents that choose next steps as circumstances change. It is written for NSW government agencies, is non-mandatory and not exhaustive, but offers a useful decision framework. The guidance cautions that choosing the wrong approach can waste budget, increase compliance risk, and reduce user trust. Digital NSW guidance
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| Question | Favor deterministic automation when… | Consider AI assistance or an agent when… |
|---|---|---|
| Are the steps known? | The same ordered steps and explicit rules apply each time. | Inputs need interpretation or the next step depends on context. |
| How stable are the inputs? | Data is structured, and connected systems or APIs change infrequently. | Inputs are varied, unstructured, or depend on changing context. |
| Can the result be checked? | Rules can validate correctness before the workflow proceeds. | The task calls for synthesis, interpretation, or a draft that a person can review. |
| What happens if it is wrong? | Validation, retries, and exception handling contain errors. | Higher-impact actions need explicit approval, tighter permissions, and careful monitoring. |
| How much autonomy is needed? | A schedule, API event, or workflow event can start a fixed process. | The system must pursue a goal by selecting tools or steps at runtime. |
| Can someone oversee it? | Staff can manage exceptions through ordinary change control. | A named owner can monitor anomalies, intervene, and switch the system off. |
These factors reflect Digital NSW’s comparison of features and use cases alongside NIST’s risk-management guidance. NIST recommends defining the system’s scope and risk tolerance, documenting its knowledge limits and human oversight, and considering costs and impacts; it does not supply a universal numerical threshold for adopting an agent. NIST AI RMF, Appendix C NIST AI RMF Playbook
What should stay deterministic
Keep controls that must be consistent, enforceable, and auditable in ordinary workflow logic or systems of record—not in a model’s free-form response.
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- Triggers: Start the process from a defined schedule, event, or API call.
- Identity and permissions: Restrict access to only the records, data, and tools the task requires.
- Business policy: Enforce authoritative rules and authorization decisions through systems that apply them consistently.
- Required fields and validation: Check formats, required information, business constraints, and source records before downstream action.
- Approval gates: Require human approval for consequential, ambiguous, or policy-sensitive decisions.
- Exception handling: Route missing, conflicting, or invalid data to a person rather than letting the process improvise.
- Traceability: Where appropriate, record inputs, workflow or model versions, relevant outputs, approvals, and actions taken.
NIST identifies risks such as data leaks, compliance failures, prompt injection, and unpredictable behavior when agent identity, authorization, and governance are weak. Its AI Risk Management Framework calls for clearly defined and differentiated human roles and responsibilities. NIST AI Risk Management Framework NIST project on AI agent identity and authorization
Where AI assistance fits
AI is useful when the task involves variable language or context but the process can remain under human direction. Keep the model’s responsibility narrow and its output reviewable.
- Classification: Label a free-text request so a fixed workflow can route it.
- Extraction: Suggest candidate fields from an email or document, then validate them against a schema and source record.
- Drafting: Prepare a response from known facts for a staff member to review and edit.
- Synthesis: Summarize relevant material to help a person decide, without making the model the authority for policy or approval.
Digital NSW gives writing customer emails in a CRM as an example of an AI assistant role. The intended pattern is human-led: the assistant helps produce or retrieve material, while the person remains responsible for the work. Digital NSW guidance
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When a workflow genuinely needs an agent
An agent is more relevant when inputs, branches, or available information can change and a system must select the next tool or step at runtime. That flexibility increases governance demands; it does not remove the need for explicit controls.
For example, a public enquiry may require finding current information across sources, preparing a response, and adapting the next step to what is found. Digital NSW illustrates such a sequence with intake, retrieval, human review, and response. Retain a human review point for consequential or uncertain cases, and do not treat the example as a guarantee that any product will perform reliably. Digital NSW guidance
Before granting an agent runtime autonomy, establish who owns it, what it may access and change, how its actions are monitored, when it must stop for review, and how to recover from a failure. If the workflow can be handled by fixed rules plus a bounded AI step, an agent may add complexity without solving a real need.
A practical pattern: fixed workflow around a bounded AI step
- Start deterministically: Use a defined schedule, system event, or API event to start the workflow.
- Constrain the task: Use workflow rules or code to select allowed records, tools, and permissions.
- Use AI narrowly: Ask it to classify text, extract candidate fields, or draft a response—not to make every downstream decision.
- Validate the result: Check its structure, completeness, and business constraints against source records.
- Require review where needed: Send consequential, ambiguous, or policy-sensitive output to an authorized person.
- Log and route: Carry out approved actions through the deterministic workflow, record relevant decisions, and send failures or exceptions to a person.
This is a practical architecture pattern, not a six-step implementation prescribed by NIST. NIST’s DevSecOps demonstration does, however, support its central principle: generative AI is used as an advisor and assistant under human supervision, and generated outputs pass through established review and validation. NIST NCCoE AI software development project
Oversight, risk, and operational ownership
Oversight should match the consequences and autonomy of the use case. NIST’s AI Risk Management Framework recognizes configurations ranging from fully autonomous to fully manual and says some uses require human oversight while others may not. Its Generative AI Profile notes that generative AI’s opportunities, risks, and long-term performance are typically less understood than those of non-generative tools; organizations may therefore need additional review, tracking, documentation, and management oversight. NIST AI RMF, Appendix C NIST Generative AI Profile
For each workflow, document its intended scope, knowledge limits, risk tolerance, likely costs and impacts, oversight roles, and controls for third-party components. For an agent, also make monitoring, intervention, and shut-off conditions operational rather than aspirational. Digital NSW identifies runtime monitoring, bias checks, and shut-off triggers among agent governance needs. NIST AI RMF Playbook Digital NSW guidance
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Examples: apply the boundary to common workflows
Invoice routing
If supplier, amount thresholds, and approval routes are explicit, use deterministic rules to route the invoice and enforce approval requirements. AI may help extract candidate invoice fields, but validate them against the document and business rules before routing. Digital NSW uses fixed-rule invoice routing as a traditional automation example. Digital NSW guidance
Customer email drafting
An assistant can draft from known facts, but a staff member should review or edit the message before sending when the content requires accountability or judgment. Digital NSW lists customer email writing in a CRM as an assistant use case. Digital NSW guidance
Software development
NIST’s NCCoE demonstration uses generative AI for tasks including requirements, decomposition, ticketing, code, configurations, tests, and security analysis under direct human supervision. The project identifies risks including inaccurate output, insecure code, unauthorized actions, excessive privileges, context tampering, and missing provenance. Generated work should therefore remain subject to established review, security checks, and validation rather than being treated as approved because a model produced it. NIST NCCoE AI software development project
A simple decision rule
Use deterministic automation for known steps and enforceable rules. Add AI assistance for bounded tasks involving language, interpretation, or synthesis, then validate its output. Move to an agent only when the workflow must choose among next steps dynamically—and only if the organization can authorize, monitor, review, and stop its actions. The sources support this qualitative, context- and risk-based choice, not a universal score or percentage threshold.
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