Enterprise AI Architecture

Solving LLM Hallucinations in Production

How Exogram uses Layer 2 Semantic Conflict Resolution to cross-examine and block hallucinated actions against established graph constraints.

01. The Architectural Threat

  • LLMs hallucinate. They invent API parameters, fabricate IDs, and propose actions that contradict established facts.
  • Retrieval-Augmented Generation (RAG) surfaces correct data, but the model can still ignore it and hallucinate a destructive tool call.
  • When an agent is plugged directly into a database or API, a hallucination is no longer just bad text — it is data corruption.

02. The Exogram Resolution

  • Exogram sits between the model and the tool, enforcing 8 deterministic policy rules.
  • Schema Integrity prevents structural hallucinations (e.g., passing a string instead of an int).
  • Semantic Conflict Resolution checks the proposed action against the verified Knowledge Graph. If the agent proposes deleting a user that was explicitly marked "protected", Exogram halts execution.
  • Zero Trust Execution: If the intent doesn't mathematically map to an allowed policy, the action is blocked.

Technical Implementation Blueprint

// Layer 2 Conflict Resolution mechanism:

1. Agent proposes payload: {"user_id": 105, "action": "delete"}
2. Exogram fetches Layer 2 Graph Context for Node(user_id=105).
3. Graph reveals edge: [Node(105)] -> [Status: Protected]
4. Exogram evaluates the action against the context.
5. Conflict detected: "Cannot delete protected resource".
6. Evaluation returns BLOCKED. Execution is halted.

Frequently Asked Questions

Doesn't more training data solve hallucinations?

No. Hallucinations are inherent to probabilistic transformers. You cannot train away the risk; you must gate the execution.

How fast is conflict resolution?

Exogram's deterministic policy engine executes in 0.07ms. It adds near-zero latency to the execution path.

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