Meet First Responder: The AI That Never Sleeps on the Job

At Gemini, we're always looking for ways to move faster and work more efficiently. Increasingly, that means finding the right places to put AI to work. One area that's long been a source of friction for every engineering team, ours included, is on-call alerts.

When something goes wrong with a software service, engineers rely on automated monitors to catch it and sound the alarm. But the volume of those alerts, the noise, the false positives, and the manual investigation required to make sense of them adds up to a significant drain on engineering time and sleep.

Our team built something to change that. It’s called First Responder, an AI agent that monitors our systems around the clock, investigates alerts automatically, and decides whether to wake up a human or handle things on its own.

Here's how it works, and why it matters.

The Problem With the Way We Used to Do Things'

When a software service breaks–a payment fails, a webpage won't load, or an app slows to a crawl–engineers rely on monitors to catch it. A monitor is essentially a rule: "If error rates go above X, send an alert."

The traditional approach to building these monitors is reactive: something breaks in the real world, engineers figure out what went wrong, and then they add a monitor for that specific problem so it doesn't go unnoticed again. Repeat this process over several years and you end up with thousands of highly specific monitors.

The problem? You always have to get burned before you start watching. By definition, reactive monitoring means production has already failed before you start paying attention. The smarter approach is proactive monitoring–casting a wide net to catch any unusual pattern before it becomes a crisis. Things like: error rates creeping up, pages loading slower than usual, servers using more memory than expected. You don't wait for a known failure; you watch for anything suspicious.

But here's the catch: proactive, wide-net monitoring generates an enormous volume of alerts. Far too many for any human team to meaningfully review. So engineers limit the number of alerts to just those that monitor the most challenging issues. This is the problem that First Responder was built to solve.

What First Responder Does

First Responder is an AI agent that sits between the flood of monitoring alerts and the humans who need to respond to them. Its job is to do the investigative work that would otherwise fall on an on-call engineer.

When an alert fires, First Responder doesn't just forward it. It investigates. Here's what that looks like step by step:

• Read the alert. What is it saying? What service is affected?
• Look up the service. Who owns it? How critical is it? What other services depend on it?
• Search the logs. Application logs are the detailed diary a piece of software keeps about everything it does. First Responder searches them for error messages and stack traces.
• Find the relevant code. What part of the codebase is involved? Is there a known bug or pattern here?
• Reason about severity. Given everything it just learned, does a human need to be woken up right now?

All of this happens automatically, in seconds, for every single alert.

Two Paths: Escalate or Handle It

After investigating, First Responder makes a decision.

Path 1 — Escalate. The situation needs human attention. First Responder pages the on-call engineer and prepares a detailed briefing document. When the engineer opens it, they don't have to start from scratch figuring out what's happening. The AI's reasoning, the relevant logs, the affected services, and the evidence are all laid out. The human's job starts at "validate and act," not "figure out what's going on."

Path 2 — Handle it. The situation doesn't warrant waking anyone up. Maybe it's a known, benign pattern. Maybe the impact is small and contained. First Responder shares its findings with the team and creates a ticket in our project management tool. If there's a clear root cause and a fixable bug, it can even propose a code fix.

The bar for escalation isn't one-size-fits-all, either. Teams can configure the sensitivity of their alerts directly in the monitor's description, and First Responder factors it in.

What's Coming Next

Interactive triage. Right now, the agent posts its findings and tags the appropriate on-call engineer. Next, we want engineers to be able to ask follow-up questions directly to the agent and have the agent continue investigating in real time, within the same conversation thread.

A learning feedback loop. Every question an engineer asks the agent is a signal. Over time, those interactions become training data that makes the agent's first-pass investigation more thorough and accurate. The system gets smarter the more it's used.

Self-healing systems. The longest-range goal is an agent that doesn't just identify bugs — it fixes them. It writes the code, submits it for automated review, and deploys the fix. The system heals itself while engineers sleep.

Both feedback loops — the live triage conversations and the autonomous fix cycle — reduce the number of times engineers get paged over time. The smarter the system gets, the less it needs to bother humans.

Why This Matters

Software reliability has always been a tradeoff between coverage and noise. You can watch for everything and drown in alerts, or watch for very specific things and miss the unexpected. First Responder breaks that tradeoff.

By putting an AI agent in the middle, we can finally afford to cast the wide proactive net that good monitoring demands. Engineers get woken up when they need to be, and not when they don't. Problems get caught earlier. Systems get better over time.

The ultimate goal is to make sure that when an engineer gets involved in an alert, it's because their judgment is genuinely needed, and when they arrive, the hard investigative work is already done.

Team Gemini