Build Day: Agent Harness & Memory — Key Learnings on Agent Harnesses, Context Engineering, and Memory Systems
Reflections from Build Club & Mem0, Pune (June 2026)
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.