RK's Rambling

Aegis-Chaos · Post 1 of 1 · → View on GitHub From “Vibe Coding” to Closed-Loop SRE How zero-trust policies, Git isolation, and math-based budget guardrails let an autonomous agent say “no” — and mean it. Aegis-Chaos: An autonomous SRE control plane with real-time zero-trust guardrails. Most AI coding assistants today operate on trust. You prompt, they generate, and you ship. That works—until it doesn’t. A single destructive command, an uncaught runaway loop, or a stale approval can turn an autonomous agent into a production incident. Project Aegis-Chaos was built to answer a simple question: what happens when the AI says “no”? This post walks through the zero-trust architecture, parallel isolation strategy, math-based budget guardrails, and end-to-end visual verification pipeline that make up our closed-loop SRE control plane—designed for the Google Developer Expert Sprint and built on the Antigravity SDK. The Decla...

Racecraft · Part 3 of 5 · ← Prologue Splitting the Brain to Beat the Clock How a "brake!" lands in 5 milliseconds while a cloud model thinks for five seconds — in the same app, on the same frame, without ever colliding. Two posts in, we have a coach that knows who's driving and what to say. This post is about the only thing that lets it say anything useful: structure. Specifically, the decision to give the system not one brain but three, each on its own clock, with an ironclad rule about which one is allowed to make the driver wait. I call it the Split-Brain engine , and the whole design collapses out of one observation. The three jobs a coach does — react, strategize, prepare — have wildly different deadlines. Trying to serve all three from one code path means the fastest job inherits the latency of the slowest. That's the original sin of every cloud-first coaching app. So I refused to let them share a path. The Spli...

This article is an expanded write-up of the talk I recently delivered as a Google Developer Expert during a talk in Denver. The full slide deck is embedded below for easy reference. Why another “agent framework”? Large-language models (LLMs) are superb at generating prose, but production-grade systems need agents that can reason, plan, call tools, and respect enterprise guard-rails. Traditionally, that means: Hand-rolling connectors to databases & APIs Adding authentication, rate-limits, and connection pools Patching in tracing & metrics later Hoping your YAML jungle survives the next refactor Google’s new duo— MCP Toolbox and the Agent Development Kit (ADK) —eliminates that toil so you can treat agent development like ordinary software engineering. MCP Toolbox in one minute ⏳ What Why it matters Open-source MCP server Implements the emerging Model Context Protocol ; any compliant age...