Synthetic Monitoring Is Broken. Your Production Traffic Can Fix It.
Synthetic monitoring depends on scripts engineers maintain by hand. Capturing and replaying real production transactions turns customer behavior into tests.
Posts by Ken Ahrens
Co-founder and CEO of Speedscale, passionate about performance engineering. • 9 posts published
Two confident fixes missed this production bug
A production bug survived two confident fixes and green tests. Replaying the captured request exposed the missing state and proved the real fix.
AI writes code in seconds, but delivery still takes days
AI pushed throughput up 59%, yet median delivery got worse. The bottleneck moved to validation. How replaying production traffic in CI closes the gap.
Which Bugs AI Agents Fix Better With Traffic
A second run of our AI bug-fixing benchmark shows where captured traffic lifts agents toward 90%, why service maps barely help, and which bugs still fail.
Capture once, test forever
Production traffic is the most complete record of what your system does, and most teams throw it away. One capture powers reproduce, validate, and sandbox.
We won't train on your data is not a security architecture
MSA clauses and contractual guarantees aren't an architecture. If your production traffic leaves your cloud, you're trusting a policy, not a system.
Logs told me something broke. Traffic showed me what.
Capture production traffic and store it in your own Elasticsearch with Speedscale BYOC. Pull it locally with es-gather.py and reproduce bugs with proxymock.
Your AI agent is fixing the wrong service
I tested 100 bugs across 240 microservices the model has never seen. Alert only: 51% pass rate, wrong service 34% of the time. Traffic captures: 77%.
From Traffic Context to Confirmed Fix in 3 Minutes
Traffic Context is the missing piece in most debugging workflows. We ran our autonomous agent on a real production bug — it found the root cause, wrote a reproduce harness, applied the fix, and confirmed it. Here's what that looked like.
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