Enterprise AI deployments are ultimately about the system and not the model. When evaluating real-world systems, the question is no longer “does the model hallucinate?” The critical questions that matter are “So what if it does?” and “Then what do we do?”
Prior hallucination work has largely focused on model-level hallucinations and not the system-level impact. Detecting, classifying, measuring, and mitigating erroneous or unfaithful outputs of a model is necessary, but it is not sufficient. What we need is a Hallucination Impact Assessment.
The 'So What?' and 'Then What?'
Today, hallucination-related guidance is focused on the AI model, such as sanitizing unsafe LLM outputs or appending disclaimers to LLM outputs. This is not enough. Say a car engine misfires. Did the car misfire when it was parked in the garage or when it was getting onto the freeway? The fault is the same, but the consequences or ‘so what‘ are entirely different. Once we know the consequence can be a potential accident, we answer ‘then what‘ by choosing to reduce the impact, such as driving on side roads instead of the freeway.
A true Hallucination Impact Assessment goes beyond the model and considers the impact on the system. Say both a simple intranet chatbot and an automated financial transaction agent use the exact same underlying AI model. If the AI model hallucinates, the subsequent impact for the chatbot is negligible, but for the financial agent, you may have just lost a hundred thousand dollars. Once we know the consequence of financial harm or ‘so what‘, the system can address ‘then what‘ by allowing you to set dynamic transaction limits, such as automatically executing trades under $10,000 but requiring your explicit approval for anything larger.
Hallucination Impact Assessment Framework
The Hallucination Impact Assessment (HIA) is the framework that we at Eydle use to evaluate the potential security and operational consequences of hallucinated AI outputs, considering four factors: agent’s function, autonomy, architecture, and controls.
HIA differs primarily in its unit of analysis and objective. Rather than asking only whether, where, or why a hallucination occurred, it asks what impact that hallucination can have in the operational context of a particular AI agent. In that sense, HIA is best positioned as a risk/impact layer on top of existing hallucination detection and attribution, bridging hallucination evaluation with operational AI-agent assurance.
The Four Critical Factors
To make this operational risk measurable, the HIA framework evaluates an agent across four critical dimensions:
| HIA Factor | Core Question | Impact Considerations |
|---|---|---|
| 1. Agent Type | What is the consequence if the agent’s output is wrong? | A simple chat agent carries vastly different consequences than an automated financial transactional agent. |
| 2. Autonomy Tier | What can the agent do without human intervention? | Assess if the agent operates in Assisted, Supervised, Conditional, or Fully Autonomous modes. |
| 3. Orchestration | How directly can a hallucinated model output become an action? | Determine whether the underlying orchestration architecture is open-ended and LLM-driven, or strict and deterministic. |
| 4. Application Controls | How reliably is a hallucination prevented, detected, contained, or reversed? | Evaluate the specific application-layer controls in place, such as tool registries, schema validation, and entity resolution. |
Containing Operational Risks with HIA
Our goal is to deploy AI systems resilient to hallucinations. The HIA framework helps us do exactly that. It moves us past asking ‘will it?’ to ‘so what?’ and ‘then what?’ With this approach, we can deploy AI agents with confidence, containing operational risks to an enterprise.
