Engineering AI-Ready Observability: Building High-Quality Data Pipelines

The convergence of AI agents and observability is hard resetting how we approach software development and operations. However, most observability platforms today generate data that's fundamentally incompatible with how AI agents work. This creates a critical gap for organizations to be able to unlock the full potential of agent-driven development.

Traditional observability tools generate overwhelming amounts of telemetry data. Modern applications often produce such high volumes that even a medium-sized customer can generate over 100,000 log entries per second, millions of traces daily, and hundreds of thousands of unique metric series - adding up to multiple terabytes of observability data daily. Even the most advanced AI models have limited context windows, typically 200K tokens, with cutting-edge models reaching 10MB of context.

This creates more than just an operations problem, it’s an immediate mathematical problem, your observability data volume exceeds your agent's cognitive capacity by several orders of magnitude. 

The naive approach of dumping raw logs, traces, and metrics into an agent's context window is simply guaranteed to fail. That’s why we need to engineer observability data specifically for AI consumption through intelligent insights and pattern extraction. 

And it’s not just that context is limited, using large volumes of raw data for your new agentic flow may become impractical, even if the model can handle it. Trucking observability data from where it resides to where your favorite AI agent runs may wind up super expensive, and also inadequate when it comes to data privacy policies. In the era of Vibe Coding, where not all code is written with performance and cost-effectiveness in mind, that could also become risky. Fast.  Imagine an “AI SRE” software written two seconds ago running 1000 API calls per minute against your observability vendor to investigate some root cause.

The evolution of engineering

As AI agents become capable of generating increasingly sophisticated code and handling routine troubleshooting, engineering roles are evolving in fundamental ways. The traditional developer workflow of writing code, testing, and debugging is being augmented by AI systems that can handle much of the execution’s heavy lifting.

This transformation means engineers are shifting their focus toward higher-level concerns where human judgment and expertise provide the greatest value. System architecture becomes more critical as engineers design service interactions, data flows, and integration patterns that AI agents can work with effectively. 

The code itself becomes increasingly ephemeral, easily replaced and modified by agents. The lasting value lies in architectural decisions and the observability infrastructure that makes complex systems understandable and manageable by both humans and AI.

Every efficient development lifecycle requires controls and balances. Observability is a critical part of the engineer’s swiss-army knife. Feeding back insights from live production environments, teams can measure the experience they’re providing their customers and use that feedback to improve and build better products. Like the early stages of writing the code, AI is also becoming a valuable augmentation in the stage of observing it in action.

Querying and analyzing endless amounts of structured and unstructured data, in measured steps and finally reaching a conclusion - that’s where LLM shines.

The need for AI-ready data

LLMs require clean, high-signal input to play to their strengths. When building a modern observability stack, we must think of AI agents as part (or most...) of the users that are going to be using it.

If in the past, an observability vendor would be concerned about optimizing its query language simplicity, allowing a human user an easy “human-readable” way to query massive amounts of data iteratively, this might become less of a concern. AI agents do not perceive complexity in the same way. They could write complex, hard-to-read queries for humans, and actually thrive in a situation where there are lots of different ways to fetch raw and processed data.

They do not require a linear approach of filtering data over and over until it “fits in the screen” for a human. They would get insights and use them to cross-reference and continue the investigation, in some cases, in structures of data that are hard to absorb or visualize for a human.

This requires a different way of thinking about data-user interfaces, with two goals in mind: first, extreme flexibility, since all our new AI agent users are going to be power-users. Second, data efficiency: if you provide an agent with multiple ways to get succinct insights into the data, which are properly documented and explainable you can help it query “raw” data much later in the process. groundcover is taking this exact approach with features like Log Patterns and Log Insights that address these challenges directly. Instead of overwhelming AI systems with raw log data, Log Patterns automatically identify recurring structures in application logs and extract meaningful parameters. This creates a foundation of clean, structured data that AI can actually work with effectively.

With the parallel ability to query data (like logs) in very granular ways, AI agents can find cost-effective, yet very detailed paths, to identify that needle in a haystack.

The solution requires dramatically increasing the knowledge density of observability data. Rather than sending thousands of individual log entries, systems need to extract underlying patterns and statistical insights that reveal system behavior. This starts with identifying recurring patterns in application logs and tracking their statistical properties, frequency, error rates, parameter distributions, and temporal trends - some of those may not fit a human-readable visualization.As an example, Log Insights builds on this foundation by providing contextual analysis that connects log patterns to system behavior, performance metrics, and potential issues. By recognizing these patterns and tracking frequency, distribution, and deviation, AI can instantly spot when something breaks the pattern, a new error, a sudden spike, a strange parameter. Rather than forcing AI to parse through noise, these tools deliver pre-processed intelligence that enables more accurate root cause analysis.

Structured, Contextual Data Delivery

AI systems perform best when they're fed data in a way that aligns with how they process information: structured, contextual, and ready to act on. That’s the goal behind integrations like groundcover’s MCP, which surfaces observability data in a form that’s optimized for autonomous consumption.

Instead of returning raw data that only requires additional interpretation, MCP exposes meaningful insights, guided investigation paths, and domain-aware recommendations. It represents a shift from traditional observability models toward something better suited for machine reasoning - less about dashboards, more about direct understanding into the depths of the data.

Working at the kernel level via eBPF, the integration captures complete service, network, and system interactions, eliminating many of the blind spots typical of conventional APM tools. This high-fidelity data stream enables AI agents to piece together causal chains and system behaviors with far greater precision.

MCP doesn’t just provide access to data, it reframes how that data is structured and delivered. Agents can query statistical summaries, uncover temporal patterns, and explore service dependencies without being overwhelmed by volume or noise.

Real Productivity Gains for Developers

The combination of AI-ready observability data and intelligent tooling delivers measurable improvements in developer productivity. When AI agents can quickly identify patterns, correlate events across services, and surface relevant context without manual data mining, investigations that once took hours can happen in minutes. Instead of parsing logs and correlating metrics manually, developers can focus on architectural decisions while AI handles initial analysis and data processing.

This creates a nearly instantaneous feedback loop between code changes and operational impact. AI agents can deploy code, immediately analyze behavioral changes through comprehensive telemetry, compare performance against historical baselines, and identify statistically significant changes within seconds. This enables a new development model where AI systems can close the loop autonomously, based on real-world performance data.

The future of software development is already emerging in organizations with AI-optimized observability strategies. As AI systems take on more responsibility for code generation, deployment, and troubleshooting, the observability layer becomes the critical foundation enabling autonomous operations. Systems that provide immediate deployment feedback, identify performance regressions, and guide optimization decisions will determine which organizations successfully leverage AI for operational excellence.

The difference between early adopters and those left behind will largely be determined by infrastructure decisions made today. Organizations that build observability infrastructure designed for AI consumption, rather than legacy approaches built for human operators, will have significant advantages as agent-driven development becomes mainstream. The convergence of AI agents and observability represents a fundamental shift in how we build, deploy, and maintain software systems, positioning forward-thinking organizations to lead in the autonomous development era.