Let’s trace an incident from the moment it happens to the moment it’s resolved. Not in theory—in practice. What does it actually look like when agents handle the investigation instead of a bleary-eyed engineer?

The scenario: It’s Tuesday, 2:47 AM. A disk usage alert fires on one of your application servers. Within seconds, cascading alerts follow—slow API responses, failed health checks, elevated error rates. Your monitoring system is lighting up.

Here’s how Simplifai handles it.

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Phase 1: Alert Ingestion

The first alert lands via webhook from your monitoring platform. Nagios, Datadog, Prometheus, CloudWatch—doesn’t matter. Simplifai ingests alerts through standard integrations.

But here’s what’s different: the system doesn’t just receive the alert and page someone. It immediately begins correlating.

Within 15 seconds, four related alerts have been grouped into a single incident. The agent recognizes that slow API responses, failed health checks, and elevated errors are symptoms—not separate problems. Instead of four notifications hitting an engineer’s phone, there’s one: “Incident detected. Investigation in progress.”

That correlation alone saves ten minutes of human triage. No more “wait, are these related?” confusion at 3 AM.

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Phase 2: Autonomous Investigation

Now the agent goes to work. It needs to understand what’s happening, and it has multiple ways to find out.

First, it connects to the affected server via SSH. This is where safety matters—Simplifai operates with configurable guardrails. Read-only commands by default. No rm -rf anything. The agent runs diagnostic commands: check disk usage, examine recent logs, look at running processes, review system metrics.

The disk on /var/log is at 98%. That’s the trigger. But why?

The agent digs deeper. It examines what’s consuming space. Log files from the application service have ballooned—one file alone is 47GB. It checks the application logs and finds a tight loop spewing errors. The error? A downstream service timeout causing retry storms.

Simultaneously, the agent queries your documentation through RAG. “Disk full /var/log” returns a runbook from six months ago when this happened before. The previous fix: rotate logs, restart the service, investigate the downstream dependency.

The agent also checks the deployment history. A config change went out yesterday that modified retry behavior. Interesting.

All of this happens in parallel. What would take a human 20-30 minutes of sequential investigation happens in under three.

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Phase 3: Root Cause Analysis

With data gathered, the agent runs the 5 Whys.

Why did the alerts fire? Disk space critically low on /var/log. Why is disk space low? Application logs grew to 47GB in 18 hours. Why did logs grow so fast? Error logging in a retry loop—thousands of entries per second. Why is there a retry loop? Downstream payment service is timing out. Why is it timing out? Config change yesterday reduced timeout threshold from 30s to 5s.

Root cause identified: an aggressive timeout setting is causing premature failures, which trigger retries, which spam logs, which fill the disk, which degrades everything else.

The agent doesn’t stop at identification. It validates each step against the evidence. The timing of log growth correlates with the config deployment. The error messages match the timeout pattern. The downstream service metrics confirm it’s responding slowly but not actually down.

Confidence level: high.

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Phase 4: Runbook Generation

Now comes the output. The agent synthesizes everything into a runbook—not a generic template, but a specific action plan for this incident.

Immediate remediation:

1. Clear space: rotate and compress logs in /var/log/app/
2. Restart the application service to clear the retry backlog

Root cause fix: 3. Revert timeout configuration to previous 30s threshold (or tune to 15s as middle ground) 4. Add circuit breaker to prevent retry storms on downstream failures

Prevention: 5. Set up a log rotation policy to prevent single files exceeding 10GB 6. Add disk usage alerting at 70% threshold for earlier warning

The runbook includes the commands to run, the config files to modify, and links to the relevant documentation. An engineer reviewing this doesn’t need to figure out what to do—they need to decide whether to do it.

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Phase 5: Human Decision

This is where autonomy meets oversight.

The engineer’s phone buzzes at 2:52 AM—five minutes after the initial alert. But instead of “CRITICAL: Disk usage 98%” with zero context, they see a summary:

Incident: Cascading failures from disk exhaustion on prod-app-03. Root cause: timeout config change causing retry storms. Confidence: high. Runbook attached. Approve automated remediation?

The engineer can review the reasoning, check the evidence, and either approve the fix or take manual control. If they trust the analysis, they tap approve. The agent executes step one, monitors the results, proceeds to step two.

By 3:05 AM, the immediate issue is resolved. The engineer goes back to sleep knowing what happened and why, with a ticket already created for the permanent fix.

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What Changed

The incident still happened. Alerts still fired. An engineer still got notified. But the experience was completely different.

No scrambling to figure out which alerts matter. No twenty minutes of SSH and grep. No “I think it might be the database” false starts. No waking up a teammate to ask if they remember that thing from six months ago.

The agent did the grunt work. The human made the decision. Total time from alert to resolution: 18 minutes. Traditional approach? Easily an hour, assuming nothing went wrong along the way.

That’s the anatomy of agentic incident response. Not replacing engineers—amplifying them.

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Next up: “The Agent Engineering Playbook for Infrastructure Teams”