Start with the shape of the task
Some work follows a stable rule: when a form is approved, create a record with known fields. Other work requires interpreting a variable email, finding relevant context or deciding which tool to use next. Both can belong in the same process. Separate predictable steps from the steps that genuinely need interpretation before choosing an architecture.
Where conventional automation fits
Rules and integrations are useful when inputs, conditions and outputs are well defined. They can validate formats, move approved data, send agreed notifications and keep systems in sync. They still need error handling, duplicate protection and operational ownership. Calling a workflow “AI-powered” does not make those engineering requirements disappear or make a model necessary for every step.
Where an AI assistant fits
An assistant helps someone understand or prepare work. It may find information, summarise a thread or draft a response. The person decides what to do with the output. This can be a strong fit where interpretation is useful but the action requires judgement, accountability or information beyond the assistant’s available context.
Where an agent fits
A tool-using agent can select and call approved functions to progress a task. That introduces questions about permissions, stopping conditions, repeated actions, recovery and escalation. Define exactly what it may read and change. Give consequential actions explicit approval rules where appropriate. The agent’s available tools and instructions should reflect a bounded job, rather than an open-ended instruction to improve the business.
An example of combining the approaches
Consider an incoming supplier request. A model interprets the message and extracts proposed details. Deterministic checks verify required fields and identify an existing record. An assistant presents the evidence to a reviewer. After approval, a fixed integration updates the system. This is an illustrative design pattern: its value comes from matching each step to an appropriate method.
Ask what happens when the task fails
Can the system recognise missing information? What if an API times out after accepting an update? Could a retry create a duplicate? Can misleading source content influence an instruction? Who receives the exception, and can they see what already happened? A useful evaluation covers those scenarios as well as successful completion. Logs and recovery paths are part of the product.
Choose by evidence
Compare approaches using representative tasks, quality criteria and the total cost of operation and review. If a simple integration achieves the objective, that is a useful outcome. If an agent handles meaningful variability, prove that within a defined scope. The goal is dependable progress through the workflow, with appropriate human control, rather than maximum autonomy.
Match the approach to the work
A discussion aid to adapt to your workflow.
| Example task | Approach to assess first |
|---|---|
| Copy an approved field between systems | A deterministic integration with validation and retries. |
| Summarise an unstructured case history | An AI assistant with source checks and human review. |
| Answer from current company guidance | Retrieval with permissions, citations and escalation. |
| Choose and execute several contextual steps | A bounded agent with approved tools and stopping rules. |
Make it specific
How does this apply to your business?
Bring us the workflow, your current systems and the decision you need to make.
Discuss your project