Observability 
When Agents Go to Production: What Actually Breaks
When agents go to production, they break the four assumptions today’s infrastructure was built on: deterministic, human-driven, request-response, stateless. A first-principles walk through what actually breaks.
I Burned 23 Billion Tokens to "Rewrite" Langfuse
A migration retro: forking Langfuse to swap its analytics store from ClickHouse to GreptimeDB, run mostly by two AI agents. Most of the work was paying down the debt of one decision — make a single database both the source of truth and the analytics layer.
The Three Pillars of Observability: The Unification That Never Quite Arrived (Part 2 of 2)
Part 1 was about how the three pillars split apart. This part is about how many smart people, starting in 2018, tried to put them back together — what they actually built, and why none of it quite reached the finish line.
The Three Pillars of Observability: A History No One Planned (Part 1 of 2)
Today we treat metrics, logging, and tracing as the natural structure of observability. But it wasn’t designed; it grew. Part 1, on how it split apart (2010–2017).
When Systems Turn Uncertain: How Datadog Sees Observability in the AI Era
Datadog’s Investor Day deck quietly redefines observability: the object of observation turns probabilistic, AI agents become operators, and the reader of the data shifts from human to model.
TMA1 v2: Making the Agent Loop actually loop
TMA1 v2 adds an MCP server, enhanced hooks that auto-inject build/session/anomaly context, and cross-agent context sharing between Claude Code and Codex.
Observing my coding agents without leaving localhost
TMA1: a local observability tool for AI coding agents with full session trace, tool decision breakdown, latency tracking, and SQL-queryable storage.