PillarUpdated April 29, 2026
Once AI usage spreads beyond a few teams, most enterprises either adopt or build a gateway-like control layer. Many start with a lightweight proxy or logging shim, then rebuild it once governance, policy, audit, and cost allocation become real requirements.
PillarUpdated April 29, 2026
Platform engineers built service mesh, identity, and FinOps. AI is the next platform layer they're inheriting — usually before anyone formally assigns it. Here's the mandate, the stack, and the four-stage maturity model.
Updated April 29, 2026
An LLM proxy is the primitive behind many AI platform-engineering capabilities: capture, replay, policy, telemetry, and cost attribution. It's a small piece of infrastructure with a large surface of consequences.
Updated July 9, 2026
If the session isn't captured, it isn't yours. AI session capture is the practice that turns disposable chats into a durable, queryable record — the substrate replay, telemetry, and governance run on top of.
Updated July 6, 2026
Logs tell you an event fired. Replay lets you re-read the captured prompts, responses, tool-use events, and results that produced it — the only honest way to debug a system whose same input doesn't always produce the same output.
Updated July 6, 2026
A complete behavioral record so you can answer what an agent saw, what it decided, and why—not just whether a request failed.
Updated July 6, 2026
Telemetry captures what agents do. Observability makes that record interpretable and actionable. Both are essential when agents run in production.
Updated April 1, 2026
Models are one layer; infrastructure is what makes agents repeatable, safe, and operable at scale.
Updated July 9, 2026
Every working agent session contains a solved problem. Skill extraction is the step that lifts that solution out of the session record and into a draft a team can review, edit, and reuse.
Updated July 9, 2026
One extracted skill helps one run. A skill library is what turns a pile of extractions into something a team can search, review, version, and rely on.
Updated July 9, 2026
A library full of good skills is worthless if the agent never loads the right one. Invocation is the matching step that connects a task to the skill that solves it.
Updated July 9, 2026
A feedback loop: capture relevant sessions, analyze patterns, extract reusable artifacts, apply them to later runs, and measure whether outcomes improve. The model can stay the same. The system around it gets better.
Updated July 6, 2026
One engineer's breakthrough session is a dead artifact until the team can search it, replay it, and invoke the skill extracted from it. Team-shared knowledge is the infrastructure that makes that possible.
