Traces on Nicole van der Hoeven

Asimov's Zeroth Law of Robotics: Observability for AI (ExpoQA Madrid 2026)

Tue, 26 May 2026 10:00:00 +0200

This week I gave the opening keynote at ExpoQA Madrid 2026 in Madrid, Spain. I presented a talk called Asimov’s Zeroth Law of Robotics: Observability for AI, which I had previously given at KubeCon EU 2025 in London, at Dutch Cloud Native Day 2025 in Utrecht, and at Newcrafts in Paris. Here’s the abstract:

A robot may not harm humans. A robot must obey humans. A robot must protect its own existence. These are Isaac Asimov’s three Laws of Robotics, created to govern the ethical programming of artificial intelligences. From the Butlerian Jihad to Skynet to cylons, we’ve been immortalizing our collective nightmares about artificial intelligence for years. But there’s an unmentioned law that comes as a prerequisite to all of that: a robot must be observable.

In this talk, I discuss the different types of AI, the factors that make observing AI different from observing applications, and the telemetry signals specific to AI that we might want to listen to. How do we deal with large data sets? How do we observe for model drift? How do we take into account the costs of LLMs? How can we use distributed tracing to follow event sequences? Part cautionary tale and part technical demo, this talk shows how to instrument and monitor AI apps using OpenTelemetry, Prometheus, OpenLit, and more.

This was a keynote slot at a testing-focused audience, which gave me the chance to lean more into the evals vs. observability framing — what evals can and can’t tell you about an AI system in production, and how distributed tracing fills the gap that benchmarks leave behind. The demo app (a Flask-based “play.py” instrumented with OpenTelemetry, flowing into Tempo for traces, Prometheus for metrics, and Loki for logs) is still on GitHub, and I’ve been refining it with each iteration.

Here’s the recording of the talk:

Resources

Demo app on GitHub

Asimov's Zeroth Law of Robotics: Observability for AI (Newcrafts 2025)

Thu, 06 Nov 2025 12:23:19 +0100

Today I presented at Newcrafts in Paris, France. I gave a talk I’ve given before, called Asimov’s Zeroth Law of Robotics: Observability for AI, which I had previously presented at KubeCon EU in London and at Dutch Cloud Native Day 2025 in Utrecht. I was happy to present it again, as it gave me the chance to further refine my demo application, which you can find on GitHub.

The main change I made was to use the “AI as judge” approach that I had previously only talked about in theory. In this version of the demo app, I use AI in both the game as well as in the testing of the game, showing how AI can be integrated into both the application and its observability/testing stack. It was really interesting to note that testing with AI ended up exposing more defects than my hardcoded test cases did, which goes to show the potential of AI-driven testing.

Asimov's Zeroth Law of Robotics (Dutch Cloud Native Day 2025)

Thu, 03 Jul 2025 15:01:24 +0200

Today I presented at the Dutch Cloud Native Day 2025 in Utrecht, the Netherlands. I gave a talk titled Asimov’s Zeroth Law of Robotics: Observability for AI, which I had previously presented at KubeCon EU earlier on in the year, in London. I was very happy to be able to present it again, because it gave me the opportunity to improve the demo app I used, which you can find on GitHub.

Asimov's Zeroth Law of Robotics: Observability for AI (KubeCon EU 2025)

Wed, 02 Apr 2025 13:46:42 +0200

Today I gave a talk at KubeCon EU 2025 in London called “Asmiov’s Zeroth Law of Robotics: Observability for AI”. Here’s the abstract:

A robot may not harm humans. A robot must obey humans. A robot must protect its own existence. These are Isaac Asimov’s three Laws of Robotics, created to govern the ethical programming of artificial intelligences. From the Butlerian Jihad to Skynet to cylons, we’ve been immortalizing our collective nightmares about artificial intelligence for years. But there’s an unmentioned law that comes as a prerequisite to all of that: a robot must be observable.

In this talk, I discuss the different types of AI, the factors that make observing AI different from observing applications, and the telemetry signals specific to AI that we might want to listen to. How do we deal with large data sets? How do we observe for model drift? How do we take into account the costs of LLMs? How can we use distributed tracing to follow event sequences? Part cautionary tale and part technical demo, this talk shows how to instrument and monitor AI apps using OpenTelemetry, Prometheus, OpenLit, and more.