Your logs already hold the answer to most incidents. The problem has never been a lack of data. It’s the time it takes a human to read through thousands of log lines, correlate them with a metric spike, and figure out what actually broke.
Large language models change that on both sides of the equation. They can do the reading for you, turning raw telemetry into an answer faster than a human could. And increasingly, they’re also something you need to watch directly: your teams are using tools like Claude and ChatGPT every day, and that usage generates its own telemetry, API calls, data access, etc. that most organizations have zero visibility into.
LLM observability uses LLMs to monitor and troubleshoot your systems, giving you real visibility into how your organization actually uses them.
The real bottleneck: interpretation, not collection
Most observability stacks are good at collection. You’ve got metrics, events, logs, and traces flowing in from every service. The bottleneck shows up downstream, when an on-call engineer has to turn that raw telemetry into an answer at two in the morning.
That’s where LLMs earn their place in the stack. They’re good at exactly the task that burns engineering hours: reading unstructured text, finding the pattern, and explaining it in plain language.
What this looks like in practice
Condensing noise into signal. Sumo Logic’s LogReduce clusters similar log messages so thousands of lines collapse into a handful of patterns you can actually scan. Add an LLM on top, and those patterns become plain-language summaries instead of regex-shaped clusters.
Turning questions into queries. You know what you want to ask. You don’t always remember the exact query syntax. Sumo Logic’s Query Agent translates a natural-language question directly into a precise query, so you spend your time interpreting results instead of debugging syntax.
Writing the incident narrative. When an alert fires, you don’t just get a spike on a graph. Sumo Logic’s Mobot condenses fragmented alerts into a narrative: what happened, what it touched, and what to check next. That’s the difference between staring at a dashboard and reading a briefing.
Drafting the postmortem. Once an incident resolves, someone still has to document it. An LLM can draft the timeline, summarize the actions taken, and suggest prevention steps, so your team spends its time on the fix, not the paperwork.
Sumo Logic brought these agents together under Dojo AI, a multi-agent platform built for security and observability workflows. Each agent has a job. They work together, with a human in the loop at every step, to quickly detect, investigate, and resolve issues.
The other half of the problem: how your org uses LLMs needs observability too
Your team probably isn’t just building with LLMs, they’re using them every day inside tools like Claude and ChatGPT. That usage generates its own telemetry, and most organizations have zero visibility into it.
The Claude Compliance App ingests Claude Enterprise and Claude Platform activity logs, giving you centralized visibility into API usage, authentication, billing, data access, integrations, SSO, and policy changes, with prebuilt dashboards and out-of-the-box detections already in place.
The ChatGPT Compliance App does the same for OpenAI’s tools, surfacing interaction patterns and flagging data-sharing behavior that violates policy before it becomes an incident. Both give you a searchable, timestamped audit trail, so when someone asks how your team is actually using these tools, you have an answer instead of a guess.
That visibility matters for the same reason your service telemetry matters. You can’t manage what you can’t see. An engineer pasting proprietary code into an unmanaged AI tool is a blind spot the same way an unmonitored service is a blind spot. One shows up on your infrastructure dashboards. The other doesn’t show anywhere.
For teams building their own LLM-powered applications on top of this usage, Sumo Logic covers that layer too, with apps like OpenLLMetry for custom AI applications that capture traces, metrics, and logs from LLM calls, vector databases, and AI agents in standard OpenTelemetry format. And LiteLLM for multi-model routing that tracks request volume, latency, token consumption, and spend across 100+ models for teams routing traffic through a multi-model proxy.
What this means for your team
You don’t have to choose between using AI to observe your systems and observing how your organization uses AI tools. Both are the same discipline, applied to a stack that now includes models and the people prompting them alongside your services.
Start with the gap that costs you the most today. If alert fatigue is the problem, look at how an LLM changes your triage. If you’re flying blind on how Claude or ChatGPT are being used across your org, that’s the visibility gap to close first.
The volume of data isn’t going away. What’s changing is how to gain visibility into AI tools and how fast you can turn issues into an answer.
Explore Sumo Logic’s AI-powered observability capabilities or talk to a Sumo Logic expert about your stack.
