Context Budget: Governing AI Context with KIRO CLI

Context Budget is an AI governance system that treats context as a finite, measurable, and enforceable resource. In this video, we walk through how Context Budget is designed and executed using KIRO CLI, focusing on real-world agent behavior such as: - Context growth and degradation - Agent lifecycle and activation - MCP tool trust and isolation - Budget thresholds and enforcement order - Evidence logs and auditability This is not a chatbot demo. This is not prompt engineering. Context Budget is infrastructure. The system uses strict separation of responsibilities across four isolated agents: - Context Auditor - Budget Enforcer - Summarizer - Risk Sentinel All decisions are logged, all enforcement is deterministic, and no critical rules are ever summarized or bypassed. The video demonstrates how natural language prompts can be used safely within a governed system, without sacrificing correctness, reproducibility, or control. If you are working with agentic AI systems, long-running workflows, or production AI infrastructure, this video is intended to show how governance can be designed as a first-class concern rather than an afterthought. --- Topics covered: - Context as a budgeted resource - Governance vs intelligence - KIRO CLI agent lifecycle - MCP trust states - Evidence-based enforcement - Enterprise-grade AI control patterns 📂 ContextBudget – GitHub Repository https://github.com/pradykpk/ContextBudget This repository contains the full ContextBudget implementation, including: - KIRO CLI agent definitions - Steering and governance rules - Context budgeting and enforcement logic - MCP trust and observability hooks - Audit logs and execution workflows The project is intended as a reference implementation for governed, enterprise-grade agentic systems built on KIRO CLI.