AI has changed how software fails, and that, in turn, changes everything about how we observe it.

In traditional systems of yesteryear, failures were relatively predictable. When APIs returned error codes or systems behaved deterministically, observability platforms that were built to troubleshoot after the fact went to work. They would dig through logs, metrics, and traces to understand what went wrong.

But AI systems don’t fail that way. They drift, they hallucinate, and they subtly degrade over time. And often, they eventually fail silently. This has forced a fundamental rethinking of observability as the foundation for building, operating, and trusting AI in production.

From a Debugging Tool to the “System of Truth”

Observability is no longer just about looking into what caused downtime. In the AI-driven world, it serves as the system of truth that informs every stage of the software lifecycle.AI agents now write code, test systems, and troubleshoot issues, but they can only operate effectively with access to high-quality production context. Where does that context come from? Telemetry. This is why observability is quickly evolving into a core component of the agentic software development lifecycle (SDLC). In fact, it has become foundational; as AI systems become more autonomous, observability provides the feedback loop that allows them to learn, adapt and improve.

Why Does Traditional Observability Break Down?

The classic three pillars of observability (logs, metrics and traces) were designed for deterministic systems, but they struggle to keep up with the complexity of modern AI workloads. Consider what happens in an AI-powered application:

• A single workflow might include thousands of spans
• Systems may trigger hundreds of LLM calls per minute
• Outputs are non-deterministic and context-dependent
• Failures may only appear across long-running sessions

These factors introduce two major challenges:

1. The explosion of telemetry. AI systems generate far more telemetry than traditional applications. And unlike before, you can’t always rely on sampling. In legacy systems, sampling worked because errors were repeatable. If something failed one out of 1,000 times, you could still catch it. But in AI systems, failures could be rare, context-specific or non-reproducible. That means if you sample the data, you risk missing the most critical issues entirely.
2. The nature of failure has changed. In traditional systems, a “200 OK” meant success. But with AI systems, a response can look perfectly valid and still be wrong. This forces us to change how we define and detect errors. In many cases, you now need AI to evaluate AI, running model-based evaluations to determine whether outputs are correct, useful or safe.

Ultimately, failures may occur across entire sessions, not just single requests, making validation even more complex.

Observability as the Backbone of AI Trust

As organizations roll out AI initiatives, trust has become the central challenge. You can’t ship AI systems confidently if you don’t know whether they’re behaving correctly, how they’re impacting users, or where they break down.This is where observability becomes critical; it’s not just for reliability, but for accountability and ROI.A year ago, teams focused on uptime, but today, the question is different: Is my AI delivering value faster and better than my competitors’?The answer depends on how effectively you can capture high-fidelity telemetry, analyze it in real time, and feed it back into development workflows. Without that loop, innovation slows down, and your competitive advantage disappears.

Behind the Rise of Agentic Observability

One of the most important shifts happening today is the emergence of agentic observability, or systems that don’t just store telemetry, but actively participate in how it’s used. This includes capabilities like:

• AI agents that analyze telemetry at scale
• Systems that assist with root cause analysis
• Integration with developer tools to feed production context into code generation

In this model, observability platforms aren’t simple passive dashboards. They’re now active participants in the engineering process, collaborating with both humans and AI agents. This evolution is already happening, with platforms like groundcover introducing agent-driven interfaces that navigate complex telemetry datasets, automatically generate insights, and communicate with other AI tools in the developer ecosystem.

Data Ownership and Privacy Matter More Than Ever: Here’s Why

AI observability has introduced the critical dimension of data sensitivity. Think about it: telemetry now includes user prompts, model inputs and outputs, and potentially sensitive business data. This rightfully raises concerns around data privacy, security and sovereignty.With this backdrop, enterprises increasingly demand architectures that allow them to:

• Keep telemetry within their own environment
• Control how data is processed and stored
• Avoid exposing sensitive information to third parties

This is why approaches like “bring-your-own-cloud” are gaining traction. BYOC specifically ensures that observability and AI operate within the customer’s own infrastructure and governance boundaries.

Meanwhile, a “bolt-on” strategy for AI simply won’t work. Many organizations are trying to retrofit AI into existing observability stacks, but this approach has obvious limitations. Bolt-on AI assumes that the right data is already being collected, the data is structured appropriately, and the system can operate without influencing data collection.

But the reality is AI needs dynamic, context-aware telemetry.

For example, during an incident, systems may need to increase data granularity in real time. Or, AI agents need to adjust what’s collected based on the problem. Observability must become adaptive, not static. If you don’t have strong integration between data collection and analysis, your AI systems will be forced to search through massive volumes of irrelevant data, reducing both effectiveness and efficiency.

eBPF and the Foundation of Autonomous Systems

Another trend is the use of technologies like eBPF to enable deeper, more autonomous observability. Unlike traditional instrumentation, eBPF operates at the kernel level, so systems can:

• Capture data without modifying application code
• Eliminate blind spots caused by incomplete instrumentation
• Provide consistent visibility across human- and AI-generated code

This is especially important as AI writes increasing amounts of code. If agents are generating software, they can’t be relied on to instrument it correctly.
Observability must be out-of-band and autonomous. eBPF gives you that foundation and ensures everything is observable, regardless of how it was built.

Observability as an Intelligent Control Plane: The Future is Now

Looking ahead, observability is evolving into something much bigger: a real-time, intelligent control plane for AI-driven systems. In this future, which is already underway:

• Observability platforms will control data collection dynamically
• AI agents will collaborate across tools and workflows
• Telemetry will drive both operations and development decisions

Most importantly, observability will no longer be a tool that engineers open during incidents. It will be the system that powers AI agents, helps guide development, and ensures trust across all levels of production.

At the end of the day, AI isn’t just another workload; it’s a fundamentally different paradigm. That means it demands a fundamentally different approach to observability.

The organizations that succeed will be those that:

• Treat telemetry as a strategic asset
• Build systems around high-fidelity, unsampled data
• Integrate observability deeply into AI workflows

In a world where systems are non-deterministic and constantly evolving, one thing is clear: You can’t trust what you can’t see. And with AI, seeing everything is no longer optional.

Author Bio: Shahar Azulay, Co-Founder and CEO of groundcover, is a serial R&D leader. Shahar brings experience in the world of cybersecurity and machine learning having worked as a leader in companies such as Apple, DayTwo, and Cymotive Technologies. Shahar spent many years in the Cyber division at the Israeli Prime Minister’s Office and holds three degrees in Physics, Electrical Engineering and Computer Science from the Technion Israel Institute of Technology as well as Tel Aviv University. Shahar strives to use technological learnings from this rich background and bring it to today’s cloud native battlefield in the sharpest, most innovative form to make the world of dev a better place.