Conventional observability is very good at catching execution failure. Across exceptions, timeouts, ...
Tell me if this sounds familiar: you’ve set up a comprehensive suite of benchmarks, and your agent i...
Foundations 4 closed on a provocation: the signals that matter for AI systems — quality, cost, and s...
If you've instrumented an LLM system recently, you're likely already aware of how much telemetry the...
Watch the full video on FUTR.tv
Greg Whalenhas spent his career at the intersection of research, engineering, and large-scale techno...
Part 2 of this series laid out the observability stack — six interdependent layers running from infr...
If Part 1 made the case for AI observability, Part 2 provides a blueprint for building the foundatio...
Artificial intelligence (AI) is no longer an experimental novelty technology. Enterprises throughout...
Why AI Observability Requires a New Mental Model You've built a RAG pipeline. It passed your evals, ...
FEATURED
INTERVIEW
Greg Whalen on Engineering Trust Into the Future of AI
As CTO of Prove AI, Greg Whalen operates at the sharp edge of one of technology’s most urgent challenges: transforming generative AI from impressive prototype to production-grade, trusted...
ALL POSTS
ENGINEERING
ENGINEERING
Clean trace, wrong output: the visibility gap nobody talks about
Explore the visibility gap in AI agent workflows, where clean execution masks flawed outputs. Learn how to enhance observability for better results.
ENGINEERING
ENGINEERING
The compound reliability problem: why your 95% agent is failing 40% of the time
Explore the compound reliability problem in AI workflows, revealing why high accuracy can lead to unexpected failures and the importance of effective composition.
ENGINEERING
ENGINEERING
Foundations of AI Observability, Part 5: Why Agentic Debugging Is the Hardest Observability Problem
Explore the complexities of AI agent debugging, highlighting the challenges of observability and the need for advanced tools to bridge the gap between insight and action.
ENGINEERING
ENGINEERING
Cost, Quality, and Safety: The New Signals of AI Observability
Explore the essential metrics for AI observability, focusing on cost, quality, and safety to enhance system performance and trustworthiness.
ENGINEERING
ENGINEERING
Your AI Looks 80% Done. It's Actually 20%. Here's Why!
Discover why your AI project may only be 20% done and the critical steps to achieve production-grade systems with insights from Prove AI's CTO.
ENGINEERING
ENGINEERING
Why AI Reliability Starts Long Before a Model Ships
Explore how Prove AI addresses the critical need for observability and governance in generative AI, ensuring systems are trustworthy and sustainable.
ENGINEERING
ENGINEERING
OpenTelemetry – Comprehensive Observability from a Single Plane
Explore how OpenTelemetry enables comprehensive observability across AI systems, enhancing anomaly detection and performance analysis for improved insights.
ENGINEERING
ENGINEERING
The Anatomy of a Generative AI Observability Stack
Explore the complexities of AI observability, from infrastructure to application layers, and learn how to diagnose and remediate issues in gen AI systems.
ENGINEERING
ENGINEERING
Navigating the Future of AI Gov & Fixing the Telemetry Problem in 2026
Navigating AI governance and fixing telemetry issues with Prove AI CTO Greg Whalen to ensure safe, compliant, and trustworthy AI systems by 2026.
ENGINEERING
ENGINEERING
The Dashboard Is Green and Your System Is Broken
AI observability needs a new approach to detect and diagnose issues that traditional monitoring misses. Learn how to build an effective AI-native observability stack.