Experience the magic
Copy. Paste. Instrument your app.
Paste the prompt below into your coding agent. It will add useful telemetry across your codebase, run your app, and show you what’s happening. No signup required.
The decision loop
From prompt to verified improvement.
The agent instruments before it optimizes. Then it deploys, queries the outcome, finds an opportunity, makes the fix, redeploys, and confirms the result actually improved.
01
Define what better means
Give the agent a goal and guardrails: improve checkout completion without increasing latency or errors.
02
Ship the measurement first
It instruments the outcome, tests delivery, and deploys the event contract before changing behavior.
03
Improve, then verify
It queries the results, finds an opportunity, ships a fix, and confirms the outcome improved after redeploy.
Decision proof
Ask what changed, not just what happened.
In this loop, the agent compares checkout releases, finds a slow path, ships the fix, and checks that completion improved while p95 fell. The same pattern works for growth, reliability, AI quality, and cost.
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%
Better decisions
The agent sees the tradeoff, not one metric.
A faster run is not better if acceptance falls. A cheaper model is not better if handoffs rise. Telemetry lets an agent reason across the outcome and the cost of producing it.
Track OpenAI costs with SQLConnect changes to outcomes
Compare releases, prompt versions, models, routes, and workflows against the result users actually received.
Make tradeoffs explicit
Keep quality, success, latency, and cost in the same query so an optimization cannot hide its downside.
Keep the reasoning auditable
The event contract, SQL, result, and caveats stay visible to the human responsible for the decision.
Decisions worth measuring
Give the agent a question that changes the next move.
Start where guessing is expensive. Each workflow becomes a repeatable evidence loop, not another dashboard to maintain.
01
AI agents
Did this release improve successful runs without raising handoffs or cost?
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 moved activation and retained usage?
06
Security events
Which access pattern changed enough to deserve human review?
Trustworthy inputs
A good decision starts with a bounded event.
Give the agent stable outcome, release, duration, and workflow fields it can compare before and after a change. Keep secrets and raw payloads out so the evidence stays safe and legible.
Completed outcomeStable dimensionsNo raw prompts
checkout.ts · completion boundary
Ingestingtelemetry.log("checkout_attempt_completed", {
attempt_id,
status: "completed",
duration_ms,
step,
release,
});JSONEvent
AutoSchema
SQLReview
Humans in the loop
The evidence is shared, not hidden in the agent.
“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
Teach the loop
Give your agent better analytical context.
Give your agent a feedback loop.
Start with sample outcomes. Review the query. Connect real traffic when the decision is useful.