From chatbot to process runner
A chatbot answers. A workflow agent acts. It takes a request or a trigger, works through the steps, calls the tools it needs, and completes a process that would otherwise take a person clicking through several systems.
This is where agents create real leverage: not conversation, but completed work. Invoice intake, order processing, ticket routing, data reconciliation, and report generation are all workflows an agent can carry from start to finish.
- Read incoming requests, emails, or documents and extract what matters
- Take actions across multiple systems in the right order
- Apply your business rules to decide the next step
- Complete routine cases automatically, escalate the exceptions
- Produce a summary or record of what it did for every run
Reliable across the whole chain
A multi-step process fails at the weakest link, so we design for what happens when a step goes wrong: retries, clear error handling, and a clean handoff to a person when the agent cannot safely continue.
We also keep humans in control of the consequential steps. The agent can do the tedious work of a process while a person still signs off where the stakes require it.
- Error handling and retries so one bad step does not silently break the run
- Human handoff at defined checkpoints for approvals or exceptions
- Idempotent actions so a retried step does not double-charge or duplicate
- A full log of each step for audit and troubleshooting
- Monitoring so you know when runs fail and why
- Rules you control, so the agent's decisions stay predictable
More on custom ai agents
Frequently asked questions
How is this different from tools like Zapier?
Rule-based automation is great for fixed, predictable steps, and sometimes it is the right answer. An agent adds judgment: it can read unstructured input, decide what to do when cases do not fit a rigid template, and handle variation that would break a fixed workflow. We often combine both, using each where it is strongest.
What happens when the agent hits a case it can't handle?
It escalates rather than guesses. We define checkpoints where the agent stops and hands off to a person, and it completes the routine cases automatically while flagging the exceptions. You get automation on the bulk of the work without losing control of the tricky cases.
How do we trust it with a real process?
By starting supervised. We run the agent alongside your current process with a person reviewing its actions, measure how it does on real cases, and only expand its autonomy once it has earned it. Every run is logged, so you can always see exactly what it did.