What is a custom AI workflow?
A custom AI workflow is a sequence of actions built around a specific business process, its source systems and its operating rules. A model may interpret an enquiry or draft a response, but the workflow also needs data access, validation, approvals, exception handling and a way to confirm that the action happened.
For a business owner, the useful question is not how many agents are involved. It is which unfinished job becomes a reliably completed job. A quotation process that reads an email and produces a draft is different from one that checks product identity, confirms availability, obtains approval and records the buyer’s next step.
Keep the request, supporting evidence and responsible owner together.
Perform the permitted action or resolve the blocker with a specialist.
Check the resulting state in the system of record before calling the job finished.
Read the visual explanation
A practical sequence for this decision. Apply your actual records, responsibilities and acceptance criteria.
Start with a finished state
Write the finish line before selecting a model. “Generate sales emails” is an output. “Every eligible enquiry receives an accurate, approved quote with a recorded follow-up” describes work that can be inspected. Revenue is a further outcome that depends on demand, price, product fit and other factors. Keep these layers separate.
Record the triggering event, the destination system and the evidence required for completion. A successful API response can still leave the wrong account updated. Completion evidence should identify the correct record and the change made to it. If an action cannot be confirmed, the process should remain unresolved rather than report success.
A worked order example
Consider a fictional distributor receiving a request for 24 cases of a product. The buyer’s attachment says ten units per case; the current catalog says twelve. An automated multiplication would produce either 240 or 288 units. Both calculations are correct for their inputs, but only one may match the buyer’s intention.
The system should retain both source references, flag the conflict and hold release. A specialist confirms the required pack with the appropriate owner, corrects the draft and records the reason. The customer’s authorized person approves the commercial commitment. Only after the order appears correctly in the destination system does the process close. This example demonstrates an exception path, not a guaranteed business result.
Define the operating contract
For each step, specify who may read, draft, change, approve and release. These are different permissions. A model that can inspect a price list does not automatically need permission to change prices. A specialist resolving missing information does not automatically inherit authority to grant a discount.
Define the records that take precedence, the maximum age of a stock check, the handling of conflicting documents and the path when the relevant person is unavailable. Add limits for time, retries and cost. Repeated failures should stop the process and create a clear work item rather than generate more attempts indefinitely.
Test ordinary work and difficult work
Build a test set from representative, appropriately permissioned records. Include duplicates, missing identifiers, obsolete attachments, out-of-stock products and messages containing irrelevant instructions. Score factual correctness, policy compliance, destination accuracy and whether a human can understand what happened.
Report the denominator. “95 completed orders” means little without knowing whether there were 100 eligible requests or 1,000. Segment the result by case type and include specialist time. A workflow can look efficient only because difficult work was removed from the measurement.
When custom work makes sense
A custom workflow is a better candidate when the process spans multiple systems, has repeatable demand and loses value in handoffs. A simple built-in automation may be enough when a single tool already has the data, rules and reliable action. Low-volume work with highly variable judgment may not justify a custom system.
Looqus builds and runs agreed workflows with specialists who resolve exceptions. The customer retains business goals, access boundaries and agreed approvals. Start with one bounded process, a baseline and explicit acceptance criteria; expand after the operating evidence supports it.
Workflow acceptance worksheet
Use this starting worksheet to agree the process with the people who will operate it. Replace the examples with your own rules and owners.
| Decision | Define before launch | Evidence to retain |
|---|---|---|
| Trigger | Which event starts an eligible job? | Original event ID and timestamp |
| Source priority | Which record wins when fields disagree? | Conflicting values, source versions and resolution |
| Allowed action | What may the system draft, change or release? | Action policy version and executing identity |
| Human ownership | Who resolves missing facts and operational blockers? | Owner, next action and response target |
| Approval | Which business commitment needs customer authority? | Approver, scope and approved version |
| Completion | What destination state proves the job is done? | Destination record ID and acceptance check |
| Recovery | What stops retries or reopens an incorrect job? | Failure reason, attempt count and recovery owner |
Sources and scope
This is Looqus’s practical design guidance. Worked scenarios are illustrative; they are not client results or guaranteed outcomes. The sources below provide the referenced platform or engineering context.
AI does the work.We own the outcome.
Define the finished business job first, then connect the tools and specialist work needed to resolve exceptions and verify completion within agreed authority.
- Agree the result
- Connect the work
- Own the follow-through