We recently read the article "AI Reliability Engineering: Welcome to the Third Age of SRE" by The New Stack and found the topic so relevant that we decided to go further. Inspired by that piece, today we bring you a more technical and hands-on perspective to complement the conversation. 

After nearly a decade working with cloud architecture and DevOps reliability, we believe AI systems deserve an even more specific operational approach.

AI is no longer just an experimental capability, it's being embedded into critical production systems. That means SREs must now monitor and manage not only infrastructure and application performance, but also the unique failure modes, drift risks, and unpredictable behaviors of large language models (LLMs) and GenAI agents.

So, come with us in today’s article as we explore what it really takes to build reliability into AI systems.

1. Understand the new failure modes of AI

AI systems fail differently than traditional software. Instead of crashing, they may hallucinate, deliver biased responses, or silently degrade in performance.

In 2023, Stanford HAI published a study showing that ChatGPT's ability to solve math problems fluctuated dramatically from March to June due to model updates, highlighting the inconsistency risks of opaque model behavior. 

As SREs, we must now think beyond logs and metrics. We need:

• Behavioral testing: check for hallucination and divergence in responses.

• Input sensitivity audits: monitor how inputs affect model performance.

• Guardrails and fallback logic: define safe behaviors for when the model fails.

Understanding these modes early allows teams to anticipate failure instead of reacting to it. This mindset shift is essential as we integrate AI into more user-facing products.

2. Design for observability in AI pipelines

Observability doesn’t stop at infrastructure anymore. You need transparency into:

• Which model version was used.

• What prompt triggered what output.

• Response latency and confidence levels.

• Drift in results over time.

For example, Netflix's ML infrastructure team uses a system of shadow deployments to test new models against real traffic in real time before replacing production models. This allows them to detect regressions before they impact users.

Your observability stack for GenAI should include:

• Model version tagging.

• Prompt logging with sanitization.

• Metrics around token use, cost, latency, and error rates.

Building this visibility empowers engineering teams to trust what the model is doing and course-correct when performance shifts. Without it, you're flying blind. And when users are impacted, it's already too late.

3. Treat GenAI as a probabilistic system

Unlike deterministic applications - where the same input always yields the same output - GenAI is inherently probabilistic. Its outputs are shaped by complex models and dynamic weights, meaning the same input can result in different outputs depending on multiple factors.

You must treat it as such:

• Expect variation in outputs for the same input.

• Log distribution of outputs, not just single predictions.

• Use confidence thresholds and human-in-the-loop approval for critical tasks.

For instance, Airbnb's machine learning team leverages model "confidence scoring" in their pricing predictions. When scores fall below a reliability threshold, the system defers to human review.

By embracing the probabilistic nature of GenAI, we can design safeguards that reduce risk while unlocking automation at scale. And those safeguards are what turn experimental prototypes into business-critical systems.

4. Build infrastructure that supports iteration

As you know, models evolve rapidly. Therefore, your platform should:

• Allow safe rollout and rollback of models.

• Support A/B and shadow testing.

• Enable fast re-training and versioning.

Uber's Michelangelo platform is a benchmark here. It enables ML teams to deploy models with CI/CD practices while maintaining tight integration with monitoring and alerting systems.

At EZOps Cloud, we follow similar principles to evolve our AI agent, ACE, continuously in production environments. ACE is an Agentic AI cloud engineer backed by human intelligence and built to coexist with the expertise of DevOps professionals.

Infrastructure built for change is the only kind that survives in a GenAI-driven world. Iteration must be frictionless, or your innovation will stall at the proof-of-concept phase. AI must enable great developers, not overwhelm them. 

5. Add AI-specific reliability layers

At EZOps, we build AI agents like ACE Dev with reliability by design. This includes:

• Role-based access control (RBAC) for all AI operations.

• Audit logs for every decision the AI makes.

• Synthetic tests to simulate prompts and edge cases.

• Real-time monitoring of AI-driven actions.

We also isolate environments, ensuring clients never share data implicitly, and we apply intelligent failovers to ensure critical cloud operations complete even if the AI misfires.

AI doesn't just need monitoring. It needs interpretability, testing, and strategic limitations. By embedding reliability into the AI agent itself, we’re building smarter systems that are safer by default.

Final thoughts

AI reliability is not just a technical challenge, it's a systems problem. It requires a blend of SRE rigor, ML understanding, and product pragmatism.

At EZOps Cloud, we don't just monitor AI. We engineer reliability into every layer, from infrastructure to inference.