Event-Driven Workflows
Our automation layer can initiate work directly from changes in company state rather than relying exclusively on scheduled polling or a person noticing that something happened.
The event as a boundary
Relevant events can include payment state changes, contract execution, customer-status changes, employee departures, incoming complaints, and KPI threshold crossings.
- Business event
- Validate + identify
- Load relevant state
- Apply deterministic rules
- Invoke intelligence where required
- Validate outcome
- Execute, escalate, or request approval
- Record resulting state
Event handling
An event must be identified before it can be trusted. The execution path may validate the source, event type, entity, timestamp, and current state before triggering downstream work.
This matters because external systems can retry events, deliver them out of order, or emit multiple events describing the same underlying change. Idempotency and state checks help prevent repeated delivery from producing repeated consequential actions.
Why event-driven execution
Polling asks repeatedly whether something has changed yet. Event-driven execution allows the source change to initiate the next permitted step directly.
This can reduce unnecessary polling, lower latency, and create cleaner causal boundaries between operating state changes and automated responses.
Intelligence inside the workflow
An event does not imply an AI model should decide every subsequent step. Known business logic remains deterministic.
Models are inserted where interpretation adds value — for example, evaluating the content of a complaint, investigating related history, or classifying an exception that fixed rules cannot adequately represent.
Failure handling
Events can arrive while dependencies are unavailable or while downstream workflows are already running. Production execution therefore needs retries, state, logging, deduplication, and recovery behavior.
The objective is not merely faster automation. It is a traceable path from a change in company state to the appropriate controlled response.
Ordering and recovery
Events are not guaranteed to arrive in the same order the underlying business changes occurred. A later state can be delivered before an earlier event, or the same event can be replayed after a timeout. Downstream logic therefore needs to compare incoming events with known state rather than treating delivery order as ground truth.
For workflows with side effects, the system can persist execution state so recovery resumes from a known boundary instead of restarting blindly. This makes event-driven automation a state-management problem as much as a messaging problem.
The advantage appears when the system can respond quickly to change without trading latency for duplicate actions, race conditions, or untraceable workflow state.