Chapter 5

To architect an inbound interface for enterprise AI, you must move away from the "black box" model and treat the AI as a high-availability distributed service. This requires a rigid, multi-layered approach to handle the non-deterministic nature of LLMs within a deterministic enterprise environment.

1. Multi-Layered Interface Topology

The architecture is divided into three distinct zones to ensure a separation of concerns.

A. The Gateway Layer (The Enforcer)

This is the first point of contact. It doesn't care about the "logic"—it cares about the protocol and security.

* Authentication & AuthZ: Identity propagation from the source system.

* Rate Limiting: Protecting downstream model quotas and compute resources.

* Schema Validation: Ensuring incoming payloads meet defined integration contracts before hitting the LLM.

B. The Orchestration & Mediation Layer (The Logic)

This is where the agentic logic resides. It manages the observable execution paths.

* Prompt Templating: Decoupling the business logic from the specific model version.

* State Management: Tracking session context across asynchronous execution steps.

* Tool Dispatcher: A governed registry that controls which external APIs the AI can invoke.

C. The Observation & Governance Layer (The Auditor)

A sidecar or interceptor pattern that records every "thought" and action.

* Traceability: Implementation of OpenTelemetry or similar standards to map a request ID to every model turn.

* Policy Guardrails: Real-time scanning of outputs for PII, bias, or "hallucination" thresholds.

2. Deterministic Integration Contracts

To treat AI as production infrastructure, you must eliminate "fuzzy" inputs.

* Versioned APIs: Never point an interface at a "latest" model alias. Every interface must target a specific, tested model version (Model_{v2.1} vs Model_{v2.2}).

* Strict Output Parsing: Use structured output formats (JSON/Pydantic) to ensure that the interface between the AI and legacy systems follows a hard contract.

* Fallback Logic: If the AI fails to produce a valid schema after N retries, the system must trigger a deterministic failure path rather than passing "best-guess" data.

3. Operational Reliability & SLAs

Reliability in AI is measured differently than in standard CRUD apps. You must define and monitor:

| Metric | Definition | Production Standard |

|---|---|---|

| TTFT | Time to First Token | Critical for user-facing latency. |

| TPOT | Token Per Output Token | Measures the "throughput" of the generation. |

| Fidelity Score | Accuracy against a gold dataset | Must be >95\% for automated execution. |

| Guardrail Intercepts | % of requests blocked by safety layers | High % indicates prompt injection or model drift. |

4. Controlled Autonomy Escalation

AI systems shouldn't have "root" access. Architecture mandates a Human-In-The-Loop (HITL) trigger based on confidence scores.

* Low Confidence: The system logs the intent but routes to a human for approval.

* High Confidence: The system executes the tool call but logs the state change in a reviewable audit trail.

* Circuit Breaker: If the cost per request or the token count exceeds a predefined threshold, the interface must kill the execution path to prevent "infinite loops" in agentic reasoning.