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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. When AI output can affect records or trigger actions, constrain it with deterministic checks and human review appropriate to the consequences.
How to choose between rules, AI assistance, and agents
Choose the least autonomous approach that can reliably handle the task. A fixed workflow is a good fit when its steps and rules are known. An AI assistant can help with a bounded task that involves variable language or synthesis. An agent is relevant only when the system must choose next steps or tools at runtime to pursue a goal.
Digital NSW’s October 2025 guidance offers a practical comparison for NSW government agencies. It is explicitly general, non-mandatory, and not exhaustive, so treat it as a framework rather than universal law. Its comparison also shows governance needs increasing from traditional automation to assistants to agents. Digital NSW AI agent usage and deployment guidance
| Question | Deterministic automation is favored when… | AI assistance or an agent is more relevant when… |
|---|---|---|
| Are the steps known? | The same ordered steps and explicit rules apply each time. | Inputs or circumstances need interpretation, or next steps depend on context. |
| How stable are the inputs? | Data is structured and stable, and connected systems change infrequently. | Inputs are varied, unstructured, or depend on changing context. |
| Can the output be checked? | Rules can validate whether the result is correct. | The task involves synthesis, interpretation, or a useful draft for a person to review. |
| What is the consequence of an error? | Validation, retries, and exception handling can contain errors. | Higher-impact actions need explicit approvals, 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 handle exceptions through ordinary change control. | An owner can monitor anomalies, intervene, and stop or switch off the agent. |
These decision axes combine Digital NSW’s feature and use-case comparisons with NIST’s risk-management guidance. NIST recommends defining scope and risk tolerance, documenting system knowledge limits and human oversight, and examining costs and impacts. NIST AI Risk Management Framework
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What should remain deterministic
Deterministic steps give the workflow a predictable frame before and after any AI contribution. Keep the authoritative decisions and controls in components that enforce them consistently, rather than relying on free-form model text alone.
- Triggers: Start on a defined schedule, API event, or workflow event.
- Permissions and identity: Limit each system or agent to the tools and data its assigned task requires. NIST’s agent identity work flags data leaks, compliance failures, prompt injection, and unpredictable behavior as risks when identity, authorization, and governance are weak. NIST AI agent identity and authorization
- Business rules: Keep thresholds, required fields, eligibility rules, and authorization decisions explicit and enforceable.
- Approval gates: Require the appropriate person to approve consequential, ambiguous, or policy-sensitive actions.
- Validation and exception handling: Check required formats, business constraints, and source records before outputs can cause downstream effects; route failures to a person.
- Traceability: Where appropriate, record inputs, workflow or model version, relevant outputs, approvals, and resulting actions.
NIST’s AI Risk Management Framework calls for clearly defined and differentiated human roles and responsibilities. Its DevSecOps demonstration also emphasizes review, validation, provenance, and traceability for generated artifacts. NIST NCCoE generative AI demonstration
Where AI can help
AI is most useful when the input or task is not easily reduced to a stable list of rules, but the result can still be bounded and checked. Give the AI a narrow job, such as classifying free text, extracting candidate fields, or drafting a response from known facts. Keep the workflow responsible for deciding what data the AI sees and what happens after it returns an answer.
- Classification: Categorize a free-text request, then validate that the category is permitted before routing.
- Extraction: Identify candidate fields in a document or message, then compare them with required fields and source records.
- Drafting: Prepare a customer response from established facts, with a staff member reviewing or editing before sending.
- Retrieval and synthesis: Help gather current information across steps and prepare a response, with a human review point when a case is uncertain or consequential.
AI output is a suggestion or candidate result—not, by itself, proof that a policy condition has been met or an action is authorized.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA practical architecture: fixed workflow around a bounded AI step
A useful pattern is to let deterministic orchestration control the start, scope, checks, approvals, and actions, while AI handles one limited judgment task. This six-part sequence is an implementation synthesis, not a specific NIST-prescribed design.
- Start deterministically: Use a defined schedule, API event, or workflow event as the trigger.
- Limit scope and access: Use code or workflow rules to select allowed records, tools, and permissions.
- Delegate a narrow task: Ask AI to classify a request, extract candidate fields, or draft a response—not to decide its own authority.
- Validate the result: Check schema, required fields, business constraints, completeness, and relevant source records.
- Review when needed: Send consequential, ambiguous, or policy-sensitive decisions to an accountable person.
- Act and record: Let the deterministic workflow carry out approved actions, log relevant decisions, and route exceptions to a person.
NIST’s DevSecOps demonstration supports the underlying principle: its current phase uses generative AI as an advisor and assistant under human supervision, and generated outputs pass through established review and validation. The demonstration covers software-development tasks such as requirements, decomposition, ticketing, code, configurations, tests, and security analysis. It also identifies risks including inaccurate output, insecure code, unauthorized actions, excessive privileges, context tampering, and missing provenance. These examples do not establish that any particular product will perform reliably. NIST NCCoE generative AI demonstration
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When an agent is justified—and what it adds
Ask, “At what point does an AI workflow actually need an agent?” The practical dividing line is runtime choice: use an agent only when the task genuinely requires the system to decide which tools or steps to use as circumstances change. If the sequence can be defined in advance, deterministic orchestration with a bounded AI step is usually the more controlled design.
An agent’s ability to act autonomously brings additional operational needs. Digital NSW identifies runtime monitoring, bias checks, shut-off triggers, and governance as considerations for agents. NIST’s agent identity work highlights risks that can arise from weak authorization and governance. An organization deploying one should assign an owner, restrict permissions, monitor for anomalies, and define how to intervene or stop the process.
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Examples: match the method to the work
Invoice routing
If supplier, amount thresholds, and approval routes are explicit, deterministic rules can route the invoice and enforce the approval path. Digital NSW uses invoice routing by fixed rules as an example of traditional automation. Digital NSW AI agent usage and deployment guidance
Customer email drafting
An assistant can draft an email from known facts while a staff member reviews or edits it before sending. Digital NSW includes writing customer emails in a CRM as an assistant example. Digital NSW AI agent usage and deployment guidance
Public enquiry with changing policy context
An agent may help retrieve current information and prepare a response across several steps. A human review point can remain for uncertain or consequential cases. This is among Digital NSW’s illustrative agent sequences, not a guarantee of a product’s capabilities. Digital NSW AI agent usage and deployment guidance
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set oversight to match the risk
There is no universal numerical threshold in these frameworks for when a workflow should move from rules to an AI agent. The choice depends on the task, its consequences, and the organization’s ability to control and oversee the system. NIST’s AI Risk Management Framework recognizes configurations ranging from fully autonomous to fully manual; some uses need human oversight while others may not. NIST AI Risk Management Framework
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NIST’s Generative AI Profile notes that generative AI’s opportunities, risks, and long-term performance are typically less understood than those of non-generative tools. Depending on the use, organizations may need additional human review, tracking, documentation, and management oversight. NIST Generative AI Profile
Before deployment, document the system’s intended scope, knowledge limits, risk tolerance, likely costs and impacts, human oversight, and controls for third-party components. Those are among the risk-management considerations identified by NIST. NIST AI Risk Management Framework
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