AI Code · Monitoring
AI Ships 10x More Code. AI-Generated Code Monitoring That Keeps Watch.
Traditional monitoring alerts on the failures you predicted. AI-generated code fails in ways nobody wrote an alert for — and there is 10x more of it. Dstl8 continuously monitors AI-generated code in production and surfaces what is actually breaking, with root cause cited to the exact log line.
brew install control-theory/dstl8/dstl8
2 min
From Install to Live Monitoring
10x
More Code Than You Can Watch
50+
Sources Monitored, No Agents
1
Log Line Each Root Cause Cites
Zero
Alerts You Have to Pre-Write
Four failure modes
Four Ways Monitoring Misses AI-Generated Failures.
AI tools ship more code, faster, in shapes nobody anticipated. Monitoring built to alert on predictable, human-paced failures quietly falls behind — and the gap is exactly where AI-generated bugs live.
01
Alerts only fire for failures you predicted
Threshold and rule-based alerts catch the known. AI-generated code fails in shapes nobody wrote a rule for, so the first signal is a user, not a page.
# alert rules cover the known
error_rate over 5% → page
# AI-generated edge case
null in a required field
no rule · no page
02
10x the code means 10x the blind spots
AI tools generate features faster than any team can instrument or add monitors for. Coverage gaps stop being exceptions and become the default state.
# this sprint, AI-generated
+22 endpoints · 0 monitors
# next incident
lands in an unmonitored path
time to notice: hours
03
Volume buries the one line that explains it
More code, more often, more log noise. The event that explains the outage is one line in millions, and the dashboard averages it away.
# 4.2M log lines today
the failing request: 1 of them
# dashboard view
p99 looks fine · signal averaged out
04
Sampling drops the evidence to control cost
To keep ingest affordable you sample, and the sampled-out request is the one that mattered. You find out which after the incident, not before.
# sampled at 1%
request_id 7a1f… → dropped
# post-incident
evidence: gone
root cause: unrecoverable
Why should you care?
The productivity gain from AI coding tools is real. So is the monitoring gap. Every feature the model ships lands in a part of your system that may have no alert, no monitor, and no one watching — until a user finds it first.
The solution
How Dstl8 Brings AI Observability to AI-Generated Code.
The goal is not to slow down shipping. It is to watch what the model shipped — continuously, across every layer — so the failures it introduces surface as signal, not as a 2am page from a user.
Catch failures you never wrote an alert for
Möbius watches runtime behavior and surfaces regressions, anomalies, and correlations without you predicting them in advance or encoding them as alert rules.
Monitor 10x volume without a 10x bill
Continuous runtime distillation keeps signal at the source, so you can monitor far more code without paying to ingest and store every single line.
Root cause cited to the exact log line
Get the answer, not a dashboard to interpret. What broke, why, and where — delivered to your terminal, IDE, or Slack with its evidence attached.
Watch every source through one continuous view
Vercel, Supabase, Kubernetes, AWS, OpenTelemetry and 50+ more feed one continuous view, so a cross-service failure is not four tabs and a guess.
Live in minutes, no agents to deploy
Point your OpenTelemetry collector at Dstl8 or brew install the CLI. Continuous monitoring starts in minutes, with no instrumentation sprint and no agents to deploy.
What you get
What AI Code Monitoring Looks Like in Production.
Active Incidents
See which AI-generated failures are real, which are noisy, and which are spreading.
Instead of digging through scattered logs when AI-generated code breaks in production, you get a prioritized incident view with timestamps, severity, and evidence connected to the runtime.
Code Quality
Improve code quality without slowing down authorship.
24.2% JavaScript snippets affected in one security study
AI-assisted output can be productive and still carry security and reliability debt. Code quality assurance starts after generation, not before it.
Incident Detail
Root cause, evidence, and next actions in one place.
Get a diagnosis that tells you whether the failure belongs to auth, data shape, environment drift, missing publish artifact, or an actual code path regression from GitHub Copilot code.
Mobius
Ask what changed, what broke, and what is correlated.
Natural language over real telemetry. Use it to investigate AI-generated code, recent deploys, or legacy services touched by AI suggestions without starting from a blank terminal.
Get Started
Start with Gonzo — free, open source, 2 minutes.
2K+ GitHub stars
Use Gonzo to inspect what your AI-generated code is doing in production before you ask for another fix, another review, or another autocomplete.
Monitoring that catches it
AI Monitoring Tools Compared: Your Options.
Capability
Catch failures you never pre-defined an alert for
Monitor 10x code volume without a 10x bill
Root cause cited to the exact log line
Cross-service correlation in one continuous view
Catch the failure before the user does
Time to first insight
Log Search
Hours
Threshold Alerts
Prompt by prompt
ControlTheory
2 minutes
Why it’s different
AI-Generated Code Monitoring Isn’t Traditional Monitoring.
When you monitor code you wrote, you start with a mental model of what it was supposed to do. With AI-generated code, that model doesn’t exist — and that’s what makes AI-generated code monitoring its own discipline. The failure modes, the signal-to-noise ratio, and the time horizon are all different. Here’s what changes.
You’re monitoring unfamiliar code.
You didn’t write it, so you can’t reason from intent. You have to reason from behavior — what the code actually did at runtime, not what it was meant to do.
The failure modes are novel.
AI tools autocomplete against patterns, not against your runtime. Edge Runtime mismatches, hallucinated APIs, and silent type coercion produce failures nobody wrote an alert for — the exact blind spots AI-generated code monitoring has to cover.
The volume is 10x.
AI ships more code, more often, generating more log volume. The one line that explains the failure is buried deeper than it has ever been.
Speed of detection matters more.
Code ships in seconds. If detection takes hours, the gap between “shipped” and “found the bug” is where outages live. Monitoring built for human-written code can’t close it.
Common questions
AI-Generated Code Monitoring — Questions from Engineering Teams.
Get started
Install & Configure Dstl8 in Under 2 Minutes.
Try the Dstl8 CLI and TUI for continuous runtime feedback. Install it, add sources, connect the MCP server into Claude Code, and more.
brew install control-theory/dstl8/dstl8
dstl8 signupcurl -fsSL https://install.dstl8.ai/script/dstl8-cli | shnpx dstl8nix run github:control-theory/dstl8Download from https://github.com/control-theory/dstl8/releasesQuick Start
# 1. Install the CLI
brew install control-theory/dstl8/dstl8
# 2. Create a Dstl8 account (or `dstl8 login` if you already have one)
dstl8 signup
# 3. Add a source so logs flow in
dstl8 sources add vercel
# 4. Connect your AI agent, auto-detects MCP-compatible clients on your machine and configures them
dstl8 install --all
dstl8 install claude-codeAdd Sources
# Add Sources
dstl8 sources add kubernetes
dstl8 sources add cloudwatch
dstl8 sources add vercel
dstl8 sources add supabase
dstl8 sources add otlp
dstl8 sources add githubStart Here
See what’s actually happening.
Connect your deployment chain. Surface emergent patterns. Get root cause analysis with fix recommendations — right in your editor.
? Intelligence that compounds — every runtime signal makes the next one sharper.
Dstl8 — Supabase runtime analysis
Open Source
Not ready for Dstl8? Start with Gonzo.
Free, open source log analysis TUI. Real-time charts, pattern detection, AI-powered insights — right in your terminal. No account, no config.
brew install gonzo
Monitor What the Model Shipped.
Free account. Gonzo running against your production logs in 2 minutes. Early access to Dstl8. No credit card, no sales call.
Related pages
More on monitoring AI-generated code.
The Model Ships With Confidence.
Now Monitor With Confidence.
Continuous monitoring for AI-generated code, root cause cited to the log line. Free 14-day trial, no credit card.