Everyone is talking about how AI SREs can transform enterprise IT operations. Built correctly, they should be able to perform root cause analysis for incidents by querying and reasoning over truly massive volumes of observability data. And enterprises are where AI SREs can have outsized impact due to the scale and fragmentation of these environments. 

But there’s a catch. Those queries can easily hit rate limits or strain your observability stack if not managed carefully — precisely when your infrastructure is most fragile. 

Whether your observability platform is self-hosted (Elasticsearch, Prometheus, Splunk), vendor-managed in the cloud (Datadog, New Relic, Elastic Cloud), or deployed in a hybrid model, you’ll face query constraints. These include API rate limits and ingestion quotas in managed services, or performance degradation in self-operated clusters — all of which directly impacts whether an AI SRE can perform RCA at enterprise scale. 

So how should you approach these challenges, and how does Traversal query what it needs without pushing your observability stack past its limits?

The 3 Critical Challenges (+1)

At enterprise scale, observability queries aren’t cheap. As index sizes grow, performance degrades — inefficient queries can push response times from milliseconds to seconds (or worse) and strain underlying infrastructure. For an AI SRE to succeed at root cause analysis, it must navigate three critical filtering challenges (plus one wildcard).

1) Index Filtering

Challenge: During an incident, if an AI SRE uses a “spray and pray” approach — i.e. querying every available index when searching for incident clues — it creates massive, unnecessary load on your observability infrastructure. 

Our approach: Traversal’s AI SRE learns which applications and indices to prioritize first. To do so, it dynamically chooses the right statistical tests to run in real-time and also utilizes contextual intelligence to target the subset most likely to contain root cause information. Just as importantly, it is able to adapt and “hop” across indices as new evidence emerges, chaining together related signals from different parts of the system. 

2) Time Filtering

Challenge: During an incident, if an AI SRE makes queries with overly broad time ranges, scanning weeks of data versus only scanning minutes, it can mean the difference between querying petabytes versus gigabytes of data. Just like searching too many indices, these broad time searches can unnecessarily generate enormous query volumes which can cause your observability infrastructure to roll over or run up against rate limits.

Our approach: Traversal’s AI SRE adaptively and sequentially narrows down time periods, setting smart time boundaries and progressively focusing on when anomalies occurred. This is powered by proprietary causal anomaly detection algorithms that we have built. 

3) Field Filtering

Challenge: Enterprise observability platforms often contain indices with hundreds or even thousands of fields. During an incident, an AI SRE querying them all can quickly overwhelm your infrastructure. 

Our approach: Traversal’s AI SRE dynamically selects the most relevant fields to filter on, based on the evolving context of the investigation — rather than indiscriminately scanning everything. We’ve found such dynamic field selection reduces query load and can improve execution speed by over 3x compared to broad-spectrum approaches.

The Randomness Problem

Challenge: In addition to these three filtering challenges, enterprises must also account for the inherent randomness of AI agents. Left unchecked, stochastic queries can trigger expensive operations that impact production workloads. 

Our approach: To ensure predictable performance, Traversal’s AI SRE includes built-in guardrails that prevent runaway query behavior — checks on query duration, blocking expensive patterns (like leading wildcards), and restricting the number of indices that can be searched at once. 

Ensuring your AI SRE is Enterprise Ready

The bottom line is that enterprise-grade AI SRE isn't just about getting the right answer — it's about getting it without compromising your existing infrastructure or exceeding rate limits. When determining if your AI SRE is able to perform at enterprise scale, ask yourself these questions:

• Can it successfully optimize queries to navigate the 3 critical challenges of index, time, and field filtering? 
• Does it have guardrails in place to prevent randomness from triggering expensive operations? 
• Is there demonstrable proof that it has successfully operated at enterprise-scale before?

At Traversal, our AI SRE passes all of these tests, and we have the case studies to prove it. Our design has been validated in Fortune 100 enterprise environments, where Traversal has scaled to handle more than 100 trillion logs, 10 billion metrics, and beyond.

To help you learn more about AI SRE, we’re publishing a series of articles with topics including understanding the AI SRE landscape and how to evaluate an AI SRE product. If you want to be the first to learn more about AI SRE and what Traversal can do for your enterprise’s infrastructure resilience, sign up for our newsletter here