Large Language Models are impressive.
They’re also probabilistic.
Production systems are not.
That mismatch is where most AI failures actually happen.
AI failures are usually trust failures
When AI systems fail in production, it’s rarely dramatic.
It’s not “the model crashed.”
It’s quieter and more dangerous:
- malformed JSON reaches a parser
- guarantee language slips into a response
- PII leaks into customer-facing text
- unsafe markup reaches a client
- assumptions are violated silently
These are trust failures, not intelligence failures.
We validate inputs. We don’t verify outputs.
Every serious system treats user input as untrusted.
We validate:
- types
- formats
- invariants
We fail closed when validation fails.
But AI output often skips this step entirely.
Instead, teams rely on:
- prompts
- retries
- “the model usually behaves”
That’s not a safety model.
That’s hope.
An LLM is just another untrusted computation.
Compliance is enforced at boundaries
This is the key insight.
Databases aren’t “GDPR-aware.”
APIs aren’t “SOC2-aware.”
Users aren’t trusted.
Compliance is enforced at boundaries:
- validation layers
- policy checks
- explicit allow/block decisions
- audit logs
AI systems need the same treatment.
Trying to make AI “behave” by adding more AI only increases uncertainty.
Deterministic verification beats AI judging AI
Many AI safety tools rely on:
- LLMs evaluating LLMs
- probabilistic moderation
- confidence scores
That fails quietly.
A verifier should:
- never hallucinate
- never guess
- never be creative
It should be boring — and correct.
Gateia: verifying AI output before it ships
This is why I built Gateia.
Gateia does not generate AI output.
It does not orchestrate agents.
It does not manage prompts or models.
Gateia runs after generation and answers one question:
Is this output allowed to enter my system?
It enforces:
- schema contracts
- deterministic safety & compliance policies
- explicit pass / warn / block decisions
Everything is auditable.
Failures are explicit.
Security fails closed.
A missing layer, not a framework
Gateia isn’t an orchestration framework.
It’s deliberately narrow.
Every production AI system eventually needs a gate — either by design or after an incident.
Verification is not exciting.
But it is inevitable.
Final thought
AI doesn’t fail in production because it’s not smart enough.
It fails because we trust probability where we should enforce rules.
Production systems don’t need smarter models.
They need stronger boundaries.
If you’re interested in deterministic verification for AI outputs,
Gateia is available as an open-source TypeScript SDK:
npm install gateia
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