Telemetry for your coding agent
Copy. Paste. Instrument your app.
Choose a use case and paste the prompt into your coding agent. It adds telemetry to your app and checks the results. No signup required.
How it works
Measure whether the fix worked.
The agent records how your app behaves before a fix, then compares it with what happens after deployment.
01
Choose what to improve
Tell the agent what needs to change. For checkout, that might mean more completed purchases without slower requests or more errors.
02
Add telemetry first
The agent adds events and checks that they arrive. It deploys that instrumentation before changing how your app works.
03
Improve, then verify
The agent queries the data to find a problem, then deploys a fix. It checks the next set of results to see whether the fix helped.
Checkout example
Did the checkout fix help?
In this example, the agent finds a slow checkout step and deploys a fix. Then it compares releases. Did more customers finish checkout? Did p95 latency fall?
Live
Agent question
Did v43 improve checkout without slowing p95?
EVIDENCE
Completion
68.4%
p95
1.1s
Abandoned
8.7%
Checkout completion by release
v43: +9.2%
Cost and quality
A cheaper run can still cost you.
A cheaper model may send more work to a human. A faster run may produce an answer the user rejects. Telemetry lets your agent compare those results alongside cost and speed.
Track OpenAI costs with SQLCompare releases
Compare task results by release, prompt version, or model. Filter by route or workflow to see where results changed.
Compare cost and quality together
Query quality, success, latency, and cost together. See when a cheaper model needs more human handoffs.
Check the agent's reasoning
Read the SQL and event definitions behind a decision. Check the results and caveats before acting on it.
Use cases
What should your agent measure?
Choose a question below. Your agent can use the same measurements to check each release.
01
AI agents
Did more runs succeed after this release? Did handoffs or costs rise?
02
Model costs
Which model produces the most accepted results per dollar?
03
API reliability
Did the new release reduce errors without increasing tail latency?
04
Background jobs
Which workload is stalling, and what changed before retries rose?
05
Product analytics
Which onboarding change helped users get started and keep coming back?
06
Security events
Which changes in access patterns need someone to investigate?
Event design
Log how each checkout ends.
Use consistent outcome, release, duration, and workflow fields so the agent can compare results before and after a change. Leave secrets and raw payloads out of events.
Final statusConsistent field namesNo raw prompts
checkout.ts · checkout result
Ingestingtelemetry.log("checkout_attempt_completed", {
attempt_id,
status: "completed",
duration_ms,
step,
release,
});JSONEvent
AutoSchema
SQLReview
Customer reviews
What customers say about Telemetry.
"Telemetry makes it super easy to track, analyze, and visualize what's going on in my business."
Namu Kang
Founder, Browserflow
"One of the easiest ways to go from dumping in your data to actually understanding it."
Shayan Taslim
Founder, LogSnag
"It's straightforward, the SQL workbench is fast, and I don't have to worry about the schema upfront."
Prem Viswanathan
Founder, SwiftCX
Agent resources
Give your agent schemas and SQL examples.
Let your agent check its work.
Try a query on sample data. Once it answers your question, connect your app.