We recently read the article “AI Reliability Engineering: Welcome to the Third Age of SRE” by The New Stack and found the discussion highly relevant. Inspired by that piece, we decided to go further and add a more hands-on, operational perspective grounded in real-world experience.

After nearly a decade working with cloud architecture and DevOps reliability, we believe that artificial intelligence introduces operational challenges that require a more explicit reliability mindset from SRE teams.

AI is no longer experimental. It is embedded in critical production systems. As a result, SRE AI now extends beyond infrastructure and application metrics to include model behavior, drift, and decision quality. This shift fundamentally changes how incident management and reliability must be approached.

So, come with us as we explore what it really takes to build reliable AI systems in production.

1. Understand the new failure modes of AI

AI systems fail differently from traditional software. Instead of crashing, they may hallucinate, degrade silently, or behave inconsistently across similar inputs.

In 2023, a Stanford HAI study showed that ChatGPT’s ability to solve math problems fluctuated significantly across model updates, reinforcing how opaque model changes can affect reliability.

For AI SRE teams, this means expanding reliability practices to include:

• 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 failure modes early enables faster root cause analysis and improves time to resolution (MTTR) when incidents occur. 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 systems, GenAI behaves probabilistically. The same input may yield different outputs depending on model state and context.

Effective SRE AI practices acknowledge this reality by:

• Logging output distributions, not just outcomes.

• Applying confidence thresholds.

• Introducing human-in-the-loop approvals when needed.

Airbnb applies confidence scoring to ML outputs and defers to human review when uncertainty rises, reducing risk without blocking innovation.

This approach transforms probabilistic behavior into a manageable reliability variable rather than an unknown liability.

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 Dev, continuously in production environments. ACE Dev 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 Cloud, we design AI agents with reliability as a core requirement. For ACE Dev, 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.

These layers strengthen incident management and allow faster, more confident incident response when anomalies occur.

AI does not just require monitoring. It requires interpretability, limits, and accountability. That is how AI SRE evolves from theory into production reality.

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.