Agent Memory as Infrastructure: The Context Injection Pattern That Replaces Fine-Tuning
Built four production AI specialists off a single LLM using plain markdown files and a 20-line fetch call — no fine-tuning, no frameworks. The real insight is that LLM behavioral specialization is a configuration problem, not a training problem, and applying IaC discipline to memory files gives you diffability, rollback, and CI-gated behavioral regression testing that fine-tuning can never provide. At MAANG scale, this extends to prompt caching economics, governance workflows, distributed session state, and multi-model routing.
Building an SRE Agent: From Playbook to Autonomous Incident Response
What happens when you put an LLM in the incident response loop? We built an SRE Agent on AKS that runs playbooks, queries Grafana, and generates post-mortems. Here's what worked, what didn't, and what we learned about trusting AI in production.
Every SRE Practice Is Downstream of Observability — Here's the Dependency Chain
SLOs, error budgets, burn-rate alerting, incident response, blameless postmortems, capacity planning, chaos engineering — each one consumes observability data as input. On a signal layer you can't trust, they degrade to opinion with a dashboard. The Reactive-to-Autonomous journey is really the maturity of that signal layer, and the 80%+ alert-noise cut came from making it trustworthy enough for automation to stand on.