THE SHORT ANSWER
Rule-based automation follows predefined paths. AI-assisted workflows use models inside defined steps. AI agents select actions and respond to results within a goal and constraints. Greater autonomy can handle variation, but it also increases evaluation, permission and oversight needs.
Compare behavior, not labels
| Dimension | Rule-based | AI-assisted | Agent |
|---|---|---|---|
| Path | Predefined | Predefined with probabilistic step | Selected within constraints |
| Best for | Stable rules | Variable content in bounded tasks | Adaptive multi-step work |
| Predictability | Usually highest | Output can vary | Path and output can vary |
| Oversight | Exceptions and change control | Output review and fallback | Permissions, action review and stopping |
Choose the least complex reliable design
A fixed reminder, validation rule or data transfer does not need an agent. Classification or summarization may need a model but not autonomous action. An agent may help when the next step depends on changing context and tool results.
The existing AI Agents & Automation module provides the deeper architecture and evaluation path.
Match control to consequence
- What can the system read, write, send or spend?
- Can it explain the evidence for an action?
- Which actions require approval?
- Can duplicate or looping actions be stopped?
- Who owns failures and customer impact?
Apply the idea to one real workflow
Choose one current workflow. Record the present outcome, the proposed change, the accountable owner, the most important exception or failure, and one before-and-after measure. Test the smallest safe version before expanding it.
Sources & further reading
- Building effective agents
Anthropic. A provider's engineering taxonomy of agents and workflows, not a universal industry definition. We use the conceptual distinction, not its changing product recommendations.
- Demystifying evals for AI agents
Anthropic. A provider's engineering guidance on multi-turn agent evaluation, checked 13 September 2026. Examples inform evaluation design but do not establish universal pass thresholds.
- Model Context Protocol tools
Model Context Protocol. The current official tools specification checked 13 September 2026. Draft details can change; OpenSkool relies on the durable separation between tool discovery, model selection and host-controlled execution.
Examples and exercises are illustrative unless attributed to a source. No independent expert review is claimed.
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