We built PingPulse because debugging AI agents in production is painful.
As a DevOps Engineer, I have literally faced this problem of tracking what stage is the ML training is in by scrolling the logs forever to find out that the process has terminated few seconds after the start due to race-condition. I have wasted hours waiting for the process to complete while also wasting the compute costs of provisioned huge machines.
Logs tell you what happened, but not always how the agent behaved step-by-step. When agents retry, branch, call tools, or make decisions across stages, it becomes hard to trace unexpected behavior.
PingPulse works by letting you instrument your agent with a simple key and send structured “pings” at each stage. We turn those into: - A stage-by-stage execution timeline - An audit trail of agent actions - Alerts for deviations (retries, delays, out-of-order steps, prohibited interactions)
We launched on Product Hunt yesterday. The goal is to make agent behavior visible and predictable in production environments.
Getting started is simple: 1. Give your key to your Agent 2. Share a doc with your AI Agent 3. See how the workflow is created, visualized, audited, and has alerting options too.
Would love feedback — especially from teams running multi-step AI workflows.