Stop AI Hallucinations & Errors | Exogram AI
When AI generates false, fabricated, or inconsistent content — and why it's worse with tool use.
AI hallucinations occur when a model generates content that is factually incorrect, fabricated, or inconsistent with its training data or provided context. In chatbots, hallucinations are annoying. But in agentic AI (with access to databases and credit cards), hallucinations are dangerous — an invented parameter or wrong query can wipe data, trigger unauthorized refunds, or bring down production.
Types of AI Hallucinations
(1) Factual hallucinations — stating false information as fact. (2) Contextual hallucinations — contradicting previously stated or provided information. (3) Citation hallucinations — fabricating references, papers, or URLs. (4) Schema hallucinations — inventing API parameters, database fields, or function names that don't exist. Schema hallucinations are the most dangerous in agentic contexts because they directly affect tool execution.
Why AI Hallucinations Get Worse with Tool Use
In a text-generation context, a hallucination produces wrong text. In a tool-use context, a hallucination executes wrong code. A hallucinated database column name in a SQL query causes a runtime error — or worse, writes to the wrong column. A fabricated API endpoint routes data to an unauthorized server. An invented function parameter triggers unexpected behavior. The consequences escalate from "misleading content" to "system failure."
Current Defenses and Their Limits
RAG (Retrieval-Augmented Generation) grounds model outputs in retrieved documents — but the model can still hallucinate while summarizing retrieved content. Fact-checking models verify stated facts — but are themselves susceptible to hallucinations. Constrained decoding limits model outputs to valid tokens — but doesn't prevent semantically invalid combinations of valid tokens. These defenses reduce hallucinations frequency but don't guarantee prevention.
The Execution-Level Defense
Exogram's schema enforcement rule validates every tool call against known schemas before execution. Hallucinated parameters, invented endpoints, and fabricated field names are blocked deterministically — not by another model, but by code. The conflict detection system also catches factual contradictions across sessions. Defense against hallucinations at the execution boundary is deterministic: schema match = pass, no match = block. No probability, no error rate.
Frequently Asked Questions
Can AI hallucinations be eliminated?
Not at the model level — hallucinations are an inherent property of autoregressive language models. But at the execution level, hallucination-driven failures can be eliminated through deterministic schema validation and action governance.
Is RAG enough to prevent hallucinations?
RAG reduces factual hallucinations by grounding responses in retrieved documents, but models can still hallucinate while summarizing retrieved content. And RAG doesn't address schema hallucinations (inventing non-existent API parameters) at all.
How does Exogram handle AI hallucinations?
Exogram validates tool call schemas deterministically, catches factual contradictions through conflict detection, and blocks actions that reference non-existent parameters, endpoints, or fields. Schema hallucinations are blocked at the execution boundary.