Context Change Impact Analysis: A Framework for Governing AI Agent Behavior Through Structured Context Versioning

This technical report introduces Context Change Impact Analysis (CCIA), a new discipline for systematically predicting how modifications to an AI agent's context configuration affect its observable behavior. I outline Compound Behavioral Impact Analysis (CBIA), a six-tier pipeline that classifies context changes across twelve behavioral dimensions at five severity levels, completing deterministically in under 100 milliseconds without LLM inference. I provide methods for context composition analysis, multi-agent handover governance, behavioral drift detection, compliance-aware impact classification, behavioral contracts/SLAs, automated remediation, context-aware deployment orchestration, cross-provider portability, and cost/latency impact analysis. I describe a reference implementation in ctxwitch, an open-source CLI with a typed context schema (witch.yaml), git-backed versioning, collaborative context pull requests, and eval-gating. This technical report establishes the CCIA framework, its extensions, and the CCIA term. A full research paper with the complete CBIA formalization and expanded evaluation is forthcoming. Reference implementation: https://github.com/ctxwitch/ctxwitch