Direct answer
Use rules for stable decisions, workflow automation for predictable multi-step processes, AI-assisted steps for bounded language or classification work, and AI agents only when the system must plan or adapt across variable steps. Increase evaluation, observability and human oversight as autonomy and consequence rise.
Autonomy should rise only when the work requires it
Use the simplest pattern that can deliver the result reliably.
Use the simplest reliable design
Predictable work usually benefits from deterministic logic. A rule is easier to test, explain and recover than an AI judgement. Workflow automation is useful when several systems and people need to move through a stable sequence.
AI becomes useful when the work contains variable language, unstructured information, classification, summarisation or drafting. An agent becomes relevant when the system must decide which steps or tools to use within a bounded objective.
Compare the four patterns
| Pattern | Best fit | Example | Primary control |
|---|---|---|---|
| Business rule | Stable input and one predictable decision | Route an enquiry by service and location | Versioned rule and exception queue |
| Workflow automation | Known sequence across systems | Create a record, assign an owner and schedule follow-up | State, retries, permissions and recovery |
| AI-assisted step | Bounded judgement over language or documents | Classify an enquiry or draft a response for review | Evaluation set and human approval |
| AI agent | Variable path requiring planning and tool selection | Research a case, gather approved records and prepare a review pack | Tool limits, observability, escalation and stop conditions |
When AI agents genuinely belong
An AI agent is a system that can choose and execute steps towards an objective using defined tools and boundaries. Agentic workflows combine model judgement with programmed control. They are useful when the path cannot be fully written in advance but can still be evaluated.
- The objective and permitted actions are explicit.
- The source information is available and access-controlled.
- Actions can be observed, reviewed and reversed where necessary.
- Exceptions can leave the agent path for a person.
- The business can evaluate output quality and operating value.
A multi-agent diagram is not evidence of value. Use agent orchestration only when splitting responsibilities improves quality, control or maintainability enough to justify the additional complexity.
Control agentic workflows
Agent observability should show what the agent received, which tools it used, what it changed, why it stopped and where a person intervened. Access should be least-privilege. High-consequence actions should require approval, and every implementation needs a safe failure and recovery path.
Choose the first implementation
- Observe the current workflow
Read real examples, exceptions, delays and handoffs before choosing technology.
- Separate stable and variable work
Use rules for the stable path and isolate the part that needs judgement.
- Build the minimum controlled version
Limit data, actions and users, then evaluate quality, time, exceptions and commercial effect.
- Scale only after acceptance
Expand volume or autonomy after the evidence and operating controls survive review.
Sources and guidance
- Anthropic: Building effective agents
- OAIC: Privacy and commercially available AI products
- ASD ACSC: Artificial intelligence for small business