Chapter 6

Building on the Inbound Interface, the Canonical Core represents the internal nervous system of the enterprise AI. While the interface manages how the world talks to the AI, the Core defines how the AI processes, reasons, and maintains state.

In this architecture, the "Model" is merely a replaceable engine; the Core is the chassis that makes it roadworthy for production.

1. Structural Boundaries: The "Core" Anatomy

The Canonical Core is composed of three rigid sub-systems that prevent the AI from becoming an unmanageable "spaghetti" of prompts and scripts.

A. The Context Registry (Short-term Memory)

This isn't just a database; it’s a governed state machine.

* Context Pruning: Algorithms that ensure the context window doesn't overflow with "noise," maintaining a high signal-to-noise ratio for the LLM.

* Security Labeling: Every piece of data injected into the context is tagged with a sensitivity level (e.g., Public, Internal, Restricted).

B. The Reasoning Engine (The Logic)

This layer separates the Intent Discovery from Action Execution.

* Plan Validation: Before any tool is called, the Core generates a "Proposed Execution Plan."

* Static Analysis: The system checks the plan against business rules (e.g., "An AI cannot authorize a refund over $500 without a human signature").

C. The Semantic Memory (Long-term Knowledge)

The RAG (Retrieval-Augmented Generation) pipeline, treated as a versioned data source.

* Vector Database Indexing: Ensuring that the retrieval mechanism is as queryable and auditable as a SQL database.

2. Observable Execution Paths (The "Trace")

To move from "experiment" to "infrastructure," you must implement Deep Traceability. Every decision must be reconstructible during a post-mortem.

* Thought Logs: Capturing the "Chain of Thought" (CoT) as a distinct metadata object, separate from the final user response.

* Unique Execution IDs: A single trace ID must link the User Request \rightarrow Context Retrieval \rightarrow Model Inference \rightarrow Tool Call \rightarrow Final Output.

* Step-Level Latency: Monitoring the duration of each "hop" within the core to identify bottlenecks in reasoning versus data retrieval.

3. Failure Handling & Circuit Breakers

In a distributed system, you expect failure. In a Canonical Core, you engineer for it.

| Failure Type | Deterministic Response |

|---|---|

| Hallucination Detected | Interrupt execution; return "Low Confidence" status code. |

| Tool Timeout | Trigger exponential backoff or fail over to a simplified reasoning path. |

| Context Poisoning | Flush the session state and alert the Governance layer. |

| Token Exhaustion | Graceful degradation (switch to a smaller, faster "emergency" model). |

4. Controlled Autonomy: The "Sandbox" Principle

The Core must treat AI agents as untrusted code running in a secure container.

* Capability Scoping: An agent's "permissions" are injected into the system prompt at the Core level, not the Interface level.

* Shadow Execution: For high-risk decisions, the Core can run two different models in parallel. If their outputs diverge by more than a set threshold (\Delta > \epsilon), a human intervention is triggered.

* Governance-First Deployment: No model update or prompt change is pushed to the Core without passing a "Regression Suite" of canonical test cases.

5. Measurable SLAs for the Core

Standard uptime is insufficient. The Core must report:

* Reasoning Accuracy: % of plans that passed the static analysis on the first try.

* Context Relevance: The "hit rate" of the retrieval system in providing useful data.

* Governance Overhead: The latency added by safety checks (must be kept <100ms).