Major AI companies are converging on identical architectural patterns for agentic systems, according to Veso Research. Claude Code, OpenAI Codex, Gemini CLI, and other platforms built independently have arrived at the same design principles. This convergence stems from fundamental constraints: finite context windows, tool protocols, and safety requirements that cannot rely on model obedience alone.

Veso identifies three primary system types: domain context substrates that provide structured access to specific domains, personal AI runtimes with persistent state, and multi-agent shells that orchestrate across platforms. Each category demands different pattern weights, helping teams avoid over-applying irrelevant solutions. The research stems from observing that any team building agents long enough naturally discovers these same constraints.

The framework outlines eight non-negotiable postulates for production systems, including persistent instruction files, external safety enforcement, and context window budgeting. Teams should start with basic instruction files and hooks before advancing to complex multi-agent coordination. The reading order matters: begin with Prompt and Control, then tackle Context and Operate where failure modes typically emerge.

These patterns represent hard-won lessons from production deployments across industries. Rather than experimental approaches, they provide battle-tested foundations for teams building reliable agentic systems at scale. The architecture serves as both roadmap and warning system for common pitfalls.