An AI workflow should coordinate established applications without becoming an ungoverned shadow database or bypassing the approvals that make the operation reliable.
Preserve the authority of existing systems
CRM, ERP, ticketing, document-management, and identity platforms exist because the organization needs an authoritative record, access rules, lifecycle controls, and auditability. An AI layer should retrieve from and act through those systems under explicit permissions. It should not quietly reproduce records in a prompt, invent durable state, or become the place where a business decision lives.
A reference architecture for controlled integration
User or workflow trigger → orchestration service → identity and policy check → approved APIs/retrieval → model task → reviewer or rules-based approval → system-of-record update → audit event and monitoring.
Each element has a different responsibility. The orchestration layer applies workflow state; the system of record keeps authoritative data; the model performs bounded reasoning or language work; the audit trail captures what happened without exposing sensitive content unnecessarily.
| Design decision | Question to answer | Failure to avoid |
|---|---|---|
| Identity | Which user or service account is acting? | A shared superuser credential. |
| Data scope | What minimum fields are needed? | Sending whole records “just in case.” |
| Write path | Which approved API changes a record? | Model output directly mutating a database. |
| Observability | What event is logged? | No record of source, action, reviewer, or result. |
| Resilience | What happens on timeout or API failure? | Duplicate or partially completed work. |
Use least privilege and traceability
Access must be designed at the task level. A workflow that drafts a customer update may need read access to case records but no authority to close the case. Record the request, retrieved sources, tool calls, approval outcome, and change reference. This supports operational support, compliance review, and root-cause analysis.
Integration is where consulting matters
The difficult work is not simply calling an API. It is mapping business ownership, data definitions, exception paths, and acceptance criteria across teams. That is familiar systems-integration work, applied to a new interaction layer.
Sources and further reading
- NIST AI Risk Management Framework — a practical risk-management vocabulary for AI systems.
- OWASP Top 10 for LLM Applications — implementation risks to test and control.
- NIST AI RMF Playbook — actions for governing, mapping, measuring, and managing AI risk.
Turn the framework into a working plan
SoloSoft can help define the workflow, system boundaries, success measures, and delivery sequence before implementation begins.