Enterprise AI integration layer¶
The integration layer mediates between probabilistic AI behavior and deterministic enterprise systems. It is the primary enforcement point for context, identity, policy, observability, and containment.
Logical services¶
- AI gateway. Approved model routing, quotas, policy enforcement, content controls, and telemetry.
- Context broker. Assembles authorized, task-specific context with provenance and freshness metadata.
- Retrieval service. Controlled search, ranking, citation, tenancy, and data-classification enforcement.
- Prompt and configuration registry. Versioning, approvals, testing, and rollback.
- Tool registry and execution broker. Allowlisted tools, typed contracts, least privilege, approvals, and transaction boundaries.
- Evaluation service. Repeatable offline, pre-release, and production evaluation.
- Audit and evidence service. Tamper-evident decision and change records with privacy-aware retention.
Integration patterns¶
| Pattern | Appropriate use | Key controls |
|---|---|---|
| Assistive copilot | Human prepares or reviews work | Clear attribution, review, no silent action |
| Bounded automation | Repetitive, reversible, low-impact actions | Limits, validation, rollback, monitoring |
| Retrieval-augmented generation | Answers grounded in governed sources | Access trimming, provenance, citations, freshness |
| Event-driven classification | High-volume triage or routing | Thresholds, abstention, sampling, appeal path |
| Agentic workflow | Multi-step tool use in constrained domains | Capability tokens, planning limits, approval gates |
Contract requirements¶
Interfaces should declare identity and delegation, purpose, data classification, schema, provenance, policy version, idempotency, timeout, error behavior, human-approval state, and correlation identifiers. Treat tool responses as untrusted input and validate them before use.