# Execution Traces Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/execution-traces
> Markdown URL: https://aitinkerers.org/technologies/execution-traces.md
> Technology record last updated: 2026-05-05T03:21:37Z
> Generated: 2026-09-20T17:51:18Z

A complete, sequential record of a program's operations: it captures function calls, variable values, and control flow for deep runtime analysis.

Execution Traces are non-negotiable for diagnosing complex software behavior. They provide a granular, chronological log of a program's execution path (control flow, data values, and timing), moving far beyond simple logging. We use them to pinpoint issues like elusive race conditions and subtle memory leaks in production environments. For distributed systems (microservices), tracing tools like OpenTelemetry and Jaeger generate a single, end-to-end trace, mapping latency across dozens of services. This data is critical for performance tuning, enabling teams to optimize code paths and achieve measurable speedups: for example, reducing a 500ms API call to under 100ms by identifying the exact bottleneck function.

- Official technology site: https://en.wikipedia.org/wiki/Tracing_(software)
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Debugging AI agents by analyzing their traces](https://vienna.aitinkerers.org/talks/rsvp_2jnsj0Iz9ks)

I've recently build a simple library that can be used to debug AI agents by analyzing execution traces. At the core I am using the Invariant Analyzer, but there is some nice visual debugger and code around, which makes discovery of agent failures much easier.

- Event context: AI Tinkerers Vienna ICML Edition — 2024-07-24 — Vienna
- Public talk page: https://vienna.aitinkerers.org/talks/rsvp_2jnsj0Iz9ks

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