Eliminating AI Hallucinations Through Config-Driven Constraints

Hallucinations Are Predictable (And They’re Your Fault, Not the Model’s)

Developer Patrick identifies that AI agent hallucinations cluster around stale context and vague constraints during 24/7 production runs. His analysis of four continuous agents reveals that hallucinations are a signal of missing uncertainty instructions rather than model limitations.

Why This Matters

Engineers often mistake model stochasticity for a lack of capability, leading to unnecessary model swaps or temperature tuning. The technical reality is that without a stop-and-escalate protocol, LLMs are programmed to provide the most plausible next token, forcing them to guess when data is missing or stale. Implementing a structured outbox pattern for uncertainty shifts the burden of validation from the model’s internal logic to the system’s architectural constraints, ensuring safety in unsupervised environments.

Key Insights

• Fact: Analysis of four agents running continuously for months by Patrick (2026) shows hallucinations are predictable config problems.
• Concept: Context freshness rules prevent agents from filling gaps with plausible-but-wrong data when working with stale information.
• Tool: Structured output validation using JSON schema and task-spec.json prevents improvised and unvalidatable agent responses.

Working Examples

Uncertainty and output rules for a SOUL.md configuration file to prevent agent hallucinations.

## Uncertainty Rule
If I do not have verified information for a required field:
- Do NOT infer or guess
- Write a structured escalation to outbox.json with: what I needed, what I tried, and why I couldn't verify it
- Stop the current task and wait for resolution
## Output Rule
All outputs must conform to the task schema defined in task-spec.json.
Validate before writing. If validation fails, escalate rather than write invalid output.

Practical Applications

• Use Case: Production agents using outbox.json to stop and wait when uncertainty is detected instead of inferring data. Pitfall: Allowing agents to ‘figure things out’ without an escalation path, which guarantees hallucinations.
• Use Case: Systems requiring JSON schema output for every response to enable automated validation. Pitfall: Lack of output format constraints which makes agent responses impossible for downstream systems to parse safely.

References:

• https://dev.to/askpatrick/hallucinations-are-predictable-and-theyre-your-fault-not-the-models-510a