THE SHORT ANSWER
Traditional automation follows predefined rules. A conversational assistant mainly responds to a person. An AI-enabled workflow uses models inside a path designed by software. An agent lets a model choose some next steps or tools within defined limits. Real systems can combine all four patterns.
Compare who controls the path
| Pattern | Who chooses the next step? | Typical strength | Main control need |
|---|---|---|---|
| Deterministic automation | Rules and code | Consistency for known cases | Correct rules and exception handling |
| Conversational assistant | Usually the user through dialogue | Explaining, drafting and answering | Clear boundaries on advice and data |
| AI-enabled workflow | Predefined code, with model judgments inside steps | Flexible interpretation within a repeatable process | Validate every model output |
| AI agent | The model chooses among allowed actions | Adapting when the path cannot be fully predicted | Permissions, observation, limits and escalation |
Evidence & context: Anthropic
These categories are a spectrum
A support system may use rules to identify a customer, a model to classify the request, a fixed workflow for refunds and an agent only for unusual investigation. Calling the whole product an agent hides the more useful question: which decisions are variable and which remain deterministic?
This page differs from AI Agent vs Chatbot. That introduction distinguishes interfaces and basic behaviour. Here the focus is how work is orchestrated behind the interface.
Choose architecture from the task
- Payroll calculation: deterministic rules, audits and explicit exceptions usually matter more than flexible planning.
- Research brief: an assistant can help a person explore without taking external actions.
- Lead enrichment: a fixed workflow can call a model for classification and validate the output schema.
- Incident investigation: an agent may select diagnostic tools as evidence changes, while write actions stay approval-gated.
These are illustrative patterns. Consequences, data access and organisational controls can change the appropriate design.
Prefer the simplest pattern that meets the need
An agent adds flexibility, but also more possible paths to test, observe and secure. Sophistication is not the number of model calls. It is the fit between variability, control and the desired outcome.
Evidence & context: Anthropic
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.
- Using tools
OpenAI Developers. Official documentation showing how models can be given built-in, function and remote tools. Checked 13 September 2026; product-specific tool names and availability can change.
Examples and exercises are illustrative unless attributed to a source. No independent expert review is claimed.
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