VERITEZEnterprise application integration with AI agents
Securely connect agentic workflows to ERP, CRM, ITSM, cloud and data platforms without bypassing enterprise controls.
From chat to controlled business action
Enterprise AI integration is not just about sending prompts to models. It requires binding agents to trusted application interfaces, limiting data exposure and establishing clear controls for read, recommend and write operations.
Proposed integration stack
Cross-cutting controls: enterprise identity, observability, data loss prevention, audit logs and human-in-the-loop review.
Three integration patterns
1. Read and ground
Agents retrieve authorized reference data or documentation, attach citations, validate timestamps and return a response. This is the preferred initial pattern for research workflows.
2. Draft and approve
The agent prepares a change, ticket, procurement draft or analysis artifact. A human reviews the inputs, impact and destination before execution.
3. Execute with guardrails
For mature, limited workflows, the agent may trigger approved APIs within predefined scopes, rate limits, transaction boundaries and rollback procedures.
Architecture decisions to resolve
- System of record and data ownership for each workflow
- Identity propagation, user delegation and token audience boundaries
- Data residency, retention and logging of prompts and outputs
- Idempotent API calls, retries, dead-letter handling and compensation
- Evaluation, red-teaming, human escalation and incident recovery
VERITEZ use case: comparative product research
A future enterprise customer submits a natural-language request. The orchestration layer extracts requirements, identifies unknowns, launches approved research tasks, produces a candidate shortlist, scores using visible criteria, estimates cost scenarios and drafts a report. All claims should carry provenance; unsupported points should be labeled as assumptions and high-impact conclusions should be reviewed before release.