AI SRE Done Right: Why Your Data Foundation Matters

Observe by Snowflake reports that complex incident investigations cost engineering teams 10 minutes to detect, 120 minutes to investigate, 15 minutes to remediate, and 370 minutes for root cause analysis, with only 30% completion. The company argues that most AI SRE tools fail because they are bolted onto architectures not designed to support them, and outlines three must-haves: unified telemetry, cost-efficient storage, and relationship modeling.

Data teams have more telemetry data available than ever before, but incident investigation has not gotten meaningfully faster. The natural assumption is that AI is the fix, so many organizations have added an AI layer and called it AI site reliability engineering AI SRE . However, this rush to meet organizational and customer reliability demands means most AI for observability is bolted onto architectures that are not designed to support it. Teams may get fast output but later discover that important information is missing because the tools don’t integrate deeply into the data platform. That’s not efficient or effective. This blog post explains the characteristics that separate AI SRE tools that actually improve investigation speed and accuracy from those that simply summarize telemetry data. To understand what effectiveness looks like, let’s start with what the current investigation baseline actually costs. Incident investigations impact engineering productivity Based on information gathered from Observe by Snowflake customers, the investigation baseline for a complex incident is 10 minutes to detect, 120 minutes to investigate, 15 minutes to remediate and 370 minutes to conduct a root cause analysis — with only 30% completion.1 It requires hundreds of on-call engineers to stitch together the issue using multiple tools, writing custom queries and piecing together the chain of events. This is not an Observe-specific view. Across the observability industry, the exponential growth of telemetry from modern distributed systems has created a shared challenge. Engineering teams are unable to process alerts and missing critical signals, and incidents are taking longer to resolve as a result. Even a moderately complex incident involves multiple steps to find the issue and complete the investigation cycle. The reasons investigating has gotten harder are structural: Data volume has outpaced what legacy observability platforms were built to handle. System dependencies are more complex due to an increase in microservices and interconnected systems. Root cause expertise is concentrated in a small number of engineers. The actual work of investigation — navigating trace hierarchies, conducting log analysis across time windows and synthesizing findings across services — is not easily automated. Incident response and its aftermath can consume substantial engineering capacity https://www.observeinc.com/resources/10x-faster-incident-troubleshooting-the-ai-sre-playbook in organizations running complex systems. Most teams reach for an AI SRE as the solution, but not all AI SREs operate the same way. An AI SRE needs to be accurate, low latency and cost efficient How well an AI SRE responds to simple natural-language queries is not enough to determine its worth as an observability tool. The deeper question is whether the data foundation can support the implemented AI SRE to make it genuinely useful. An observability AI SRE must be more than just a chat function layered on top of telemetry data or other observability tools. It needs to be accurate enough to trust, fast enough to use during an active incident and efficient enough to query without compounding costs. Most AI SREs available today fall short because they were designed to deliver fast responses to basic queries, not operate autonomously on fast-moving, complex observability workloads. Three must-haves for an effective AI SRE Before asking which AI SRE to use, determine whether you have the right data foundation in place to support one: Is all of the telemetry in one place, or is it siloed across tools? Is the storage layer cost efficient enough to retain the necessary data for the desired retention window without sampling? Does the system model the relationships between infrastructure, applications, services and business data? Most conversations about AI SRE tools begin with the top layer, but this is the wrong starting point. Effective AI-driven investigation requires three layers working together — and the AI layer is only as accurate as what it’s built on. Layer 1: Unified, cost-efficient telemetry storage: The more data the AI layer has to operate on, the more accurate the results will be. This requires two things working together: unified storage across logs, metrics and traces and storage affordable enough to keep data at scale without forcing tradeoffs. Layer 2: A context graph that models semantic relationships: Raw telemetry can provide some insight into an incident, but a context graph explains why it happened and where else it connects. Context graphs model semantic relationships between various objects in the environment, so when the AI layer is following a thread, it’s going through a map of how different entities relate to one another. Layer 3: An AI SRE capable of leveraging the underlying data and semantic foundations: The AI SRE sits on top of Layers 1 and 2 and is built to utilize agent-optimized interfaces with unified storage and context. An agent querying through agent-optimized interfaces returns more accurate results, with lower latency and less overhead, than one sending queries to a platform not designed with AI access in mind. Observe by Snowflake is built with agents in mind Observe by Snowflake is built with all three layers together. The data lakehouse stores logs, metrics and traces at high fidelity and low cost. The context graph structures that data with context from various sources business, application, infrastructure and so on . The AI SRE sits on top of both, designed to work with the other layers from the start. The result: several Observe customers could troubleshoot up to 10x faster https://www.observeinc.com/resources/10x-faster-incident-troubleshooting-the-ai-sre-playbook with an average of over 4x .2 Here’s what that actually looks like in practice. The investigation loop remains with AI, but it is highly accelerated. These tasks can now be completed in a fraction of the original time, and AI helps make this process possible for all users, not just a few experts. Using a methodology from Anthropic’s framework for measuring AI productivity https://www.anthropic.com/research/estimating-productivity-gains , we compared manual investigation time and AI-assisted completion time and found that AI assistance resulted in the following: - Productivity gains consistently clustered in the 3x-10x range 30% of the interactions showed more than 5x improvement- 5% of the interactions exceeded 10x improvement The largest gains came from investigations that required synthesizing large volumes of data across multiple sources quickly — areas in which unified telemetry and a context graph can help reduce investigation time. The improvements aren’t just in our research; we’ve also heard direct feedback from customers: A location intelligence company: Observe’s AI SRE helped improve incident response across the company’s engineering teams. The company said Observe’s AI SRE and MCP Server could transform how they investigate incidents. These features also helped reduce the time their engineers spent on resolving issues. A sports and entertainment operator: The value for this company extended beyond faster investigation into proactive reliability. They said Observe helped empower their teams to detect system issues, leading to greater system stability. An automotive SaaS provider: Incident investigation time was reduced from over three hours to minutes, directly improving customer experience. The provider got consistent feedback across teams: fewer escalations, faster support ticket resolution and less manual review time overall. In each case, the difference stemmed from the AI layer’s complete telemetry and context. These results reflect what’s possible when the underlying architecture supports the AI SRE layer. Architecture determines whether the AI SRE delivers results When evaluating any AI SRE offering, it’s important to ask three questions related to your underlying architecture: Does the AI SRE have unified access to all the telemetry data? Does the AI SRE understand the relationships between services, deployments, users and business context? Is the AI SRE optimized to take advantage of underlying layers, or is it just sitting on top of them? Your answers determine whether the AI SRE will help accelerate investigation or simply give you a chat interface for your telemetry. Learn more about observability at scale by registering for an on-demand video of Observe’s August webinar on agent-first observability https://mkto.observeinc.com/building-agent-first registration.html register . 1 “What’s New: AI-Powered Observability at Scale with Observe by Snowflake,” Snowflake Summit 2026, Session WN201B https://reg.snowflake.com/flow/snowflake/summit26/sessions/page/catalog/session/1773336009188001TLRV . 2 Based on Observe’s analysis of 3,163 AI SRE conversation spans from October to November 2025.