AI agents in business workflows: where autonomy helps and where humans should stay in control
An agent is not simply a smarter Power Automate flow. It is useful when the system must interpret information, choose between several possible next steps or coordinate multiple tools. Predictable business rules should still remain deterministic.
1. Do not use an agent where a workflow is enough
If the steps, conditions and target systems are known in advance, a conventional workflow is cheaper, easier to test and more predictable.
- Power Automate or code for fixed rules
- AI for interpretation
- dynamic tool choice only when necessary
2. Give the agent narrow tools
Avoid general database or admin access. Prefer targeted business operations because each can be validated and audited independently.
- separate read and write tools
- least-privilege service identity
- input schema for every tool
- server-side business validation
3. The model cannot be the only security layer
An agent prompt is not an authorization system. The backend still needs to verify user, role, business state and permitted transaction.
- Entra or application identity
- backend authorization
- data- and action-level checks
- policy independent of the prompt
4. Put a human before irreversible actions
For financial posting, contract sending, external communication, deletion or high-value transactions, the agent can prepare the action but execution should usually require explicit approval.
- draft first
- approval checkpoint
- preview the exact action
- record who approved what and when
5. State, idempotency and auditability make agents operable
For multi-step work, store the current state, tool calls, results and which operations are safe to retry. A retry must not create a duplicate transaction.
- correlation ID
- tool-call audit
- idempotent write operations
- timeout and retry strategy
6. Start with a narrow use case
A good first agent has a clear input, a small toolset and a measurable outcome. Measure accuracy, human-review rate and time saved before granting more autonomy.
- 2–5 well-defined tools
- real test cases
- human-review rate
- cost of errors and rollback