Why is an AI agent a workflow?
An AI agent completes a task in several steps: it calls a language model, decides on the next step, queries systems, writes results back and sometimes waits for a confirmation. Each of these steps can fail or take a long time.
This raises the same questions as for any workflow across several systems: what happens if a service fails mid-run? Does the agent then start over – and trigger actions a second time? And who can see later what it did?
What goes wrong without durable orchestration?
Typical problems when agents run as simple scripts:
- Interruption after many steps: the run starts over, and model calls already paid for and work already done are lost.
- Duplicate actions: an email is sent twice, a record is created twice.
- Stuck runs: an agent waits for a response that never comes, and nobody notices.
- No traceability: which tools the agent called and with what result can hardly be reconstructed afterwards.
How does an agent become reliable?
A proven approach is to build the agent as a durable workflow: the control logic – which step comes next – is the workflow; every call to a language model or tool is a separate step with retries and a timeout.
If something fails, the run resumes at the last completed step. Results of earlier steps are kept and not recalculated.
| Building block | Purpose |
|---|---|
| One step per call | model and tool calls retryable individually, with a timeout |
| Retry rules | catch temporary errors, such as an overloaded model service |
| Waiting point | wait for an approval or an external event – even over days |
| Signal | pause, correct or cancel a running agent |
| History | every step traceable with time and result |
Where are people needed?
Not every decision should be made by an agent alone – especially not when it affects people, moves money or cannot be undone. Such steps get a waiting point: the agent prepares, a person approves, and only then does it continue.
Regulation increasingly requires this too: the EU AI Act requires effective human oversight for high-risk systems. A workflow with waiting points and a history makes this oversight verifiable in operation.
And the data?
Agents often process personal data – in prompts, intermediate results and logs. The architecture should already define which data goes to which model service, where intermediate results are stored and when they are deleted.
For stored AI data such as prompts and transcripts, our product Espresso Data Privacy has a dedicated module: AI Privacy Control.