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The last post in this series.
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S3 launched twenty years ago as a simple object store with eventual consistency.
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SIGMOD Companion '26 has a 13-page paper by 50+ Google authors revisiting Bigtable two decades on. 10 EB, 7 billion QPS, and an architecture that barely moved.
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An idea someone had fully worked out back in 2018, yet it took eight years before it could actually be built.
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When agents go to production, they break the four assumptions today's infrastructure was built on: deterministic, human-driven, request-response, stateless.
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A migration retro: forking Langfuse to swap its analytics store from ClickHouse to GreptimeDB, run mostly by two AI agents.
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Part 1 was about how the three pillars split apart.
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Today we treat metrics, logging, and tracing as the natural structure of observability.
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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.
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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.
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TMA1: a local observability tool for AI coding agents with full session trace, tool decision breakdown, latency tracking, and SQL-queryable storage.
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Have we been building infrastructure for humans this whole time, without ever treating agents as users?
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My colleague called Database MCP 'pulling your pants down to fart.'
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AI Coding doesn't fundamentally make you a better programmer.
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The difference between using AI as a tool you pick up occasionally versus treating AI as infrastructure you don't know how to work without.
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When AI can rewrite an entire module in seconds, do the design principles built for 'humans maintaining code' still apply?
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AI's real value isn't in producing the final output—it's in creating Builders.