An AI reliability playbook for Heads of AI who need a system the product team can plan around.
Product can't plan around it.
AI reliability is a different problem from traditional software reliability.
The first symptom we see in unreliable AI is a product team that has stopped committing to AI-dependent features in roadmap reviews.
The second symptom is sales that has started apologizing for AI behavior to customers.
Latency, uptime, and quality. Quality is the one most teams skip because it is harder.
No prompt change ships without passing the eval. The eval suite is treated like the test suite.
When the model provider releases a new version, your behavior changes. Your customers notice before you do.
Latency, uptime, and quality.
No prompt change ships without passing the eval.
When the model provider releases a new version, your behavior changes.
If your product team has stopped trusting your AI, the answer is not a better model.
Most of it is regular SRE applied to AI systems. The new parts are eval gates, behavior fingerprints, and quality SLOs. Those concepts do not exist in traditional SRE.
No. Reliability is engineered around any provider. We have run this on Anthropic, OpenAI, AWS Bedrock, and self-hosted models.
The program runs on a team of three AI engineers and one platform engineer. We have run it with smaller teams when paired with our embedded engagement.
