As companies rush to explore the potential of LLMs and autonomous agents, there’s a recurring pattern I’ve observed: everyone talks about reliability, safety, and continuous improvement — but when it comes to implementation, speed often takes priority over sustainability. And that’s understandable. The pressure to prove ROI, move fast, and outpace competitors is real.

But as we integrate AI agents deeper into our business processes, it's critical to remember that short-term wins can lead to long-term costs if foundational principles are skipped. The good news? By integrating core DevOps and Site Reliability Engineering (SRE) practices into your AI agent strategy, you can stay ahead of the curve and build systems that are not just smart, but stable, measurable, and resilient.

Let’s walk through three areas where applying DevOps and SRE principles can make all the difference: observability, rigorous testing, and interdisciplinary collaboration.

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1. Observability: Know What Your Agents Are Doing (And Why)

As companies begin adopting LLMs and agentic workflows, one common challenge is a lack of visibility into how these systems behave in real-world conditions. It’s not due to negligence — often, it stems from a shortage of subject matter expertise around how to operationalize AI effectively.

Many teams are experimenting with impressive prototypes, but when things break or results are inconsistent, there’s little insight into why. Without proper observability, it becomes incredibly difficult to diagnose issues, learn from them, or improve outcomes over time.

What happens without it: An AI sales agent accidentally sends incorrect discount codes to high-value clients. The root cause? A faulty tool call — but no logging or decision traceability is in place to catch it.

What’s possible with it: Product and marketing teams can review tool usage, identify patterns in customer interactions, and fine-tune prompts based on measurable outcomes. Continuous feedback loops are established, making agent behavior more predictable and performant.

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2. Rigorous Testing: Functional, End-to-End, and Post-Deployment

Testing LLM agents isn’t just about seeing if a response sounds right — it requires comprehensive, layered testing strategies. Agents operate across multiple systems, depend on APIs, and interact with real users, which makes them vulnerable at every touchpoint. Effective testing should include:

• Functional unit tests (for deterministic logic and tool integrations),
• End-to-end scenario testing (to evaluate agent behavior across full workflows),
• Post-deployment monitoring (to catch drifts or regressions once live).

What happens without it: An HR agent built for resume screening starts rejecting qualified applicants after a backend schema update. The change seemed unrelated — until candidate pipelines began drying up and no alerts were triggered.

What’s possible with it: Regression tests validate workflows, monitor edge-case behavior, and enable safe updates. Teams can iterate with confidence, rollback quickly, and trust that new features won’t quietly break critical paths.

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3. Interdisciplinary Collaboration: Bridge the Gap Between Data, Ops, and Product

AI agents touch nearly every part of a business — from infrastructure to UX to compliance. Yet, many teams still approach agent development in silos. When AI is treated purely as a data science or innovation initiative, it often misses the nuance needed to integrate reliably into production systems.

This leads to well-intentioned agents that fail at the seams of business logic, user expectations, or operational constraints.

What happens without it: A customer support agent fails to escalate Tier 2 issues because product rules weren’t factored in. The data team built it with best intentions, but no one looped in product or ops to add additional context to agents workflow.

What’s possible with it: Collaboration across functions leads to shared ownership, realistic metrics, and clear definitions of success. Agents are designed with guardrails that reflect real-world expectations, and institutional knowledge is captured rather than siloed.

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Conclusion: Stay Ahead by Starting With the Fundamentals

There’s a lot of excitement around AI agents right now — and rightly so. But in the rush to build what’s next, let’s not forget what already works. Just by implementing observability, testing, and cross-functional collaboration, you’ll be well ahead of most in the field.

These aren’t advanced techniques reserved for AI-first companies — they’re foundational practices from DevOps and SRE that are perfectly suited to the complexity of agentic systems.

And when you start from a foundation of reliability and continuous improvement, you don’t just build agents that impress. You build agents that last.