Aristidis Vasilopoulos presents a codified context infrastructure developed while building a 108,000-line C# distributed system, addressing a fundamental problem: LLM agents lack persistent memory across sessions, losing project coherence and repeating mistakes.
The architecture is organized into three tiers. Tier 1 (Constitution) is a single 660-line Markdown file, always loaded in memory, encoding conventions, patterns, failure modes, and routing tables to specialized agents. Tier 2 comprises 19 specialized agents (9,300 lines) functioning as domain-priming mechanisms — more than half of their content consists of codebase facts rather than behavioral instructions. Tier 3 is a knowledge base of 34 documents (16,250 lines) retrieved on demand via an MCP (Model Context Protocol) server.
The quantitative evaluation covers 283 sessions over 70 days, 2,801 human prompts generating 1,197 agent invocations and 16,522 autonomous turns. The context infrastructure accounts for 24.2% of total documentation. Sessions are 87% ad-hoc and 13% structured (plan-execute-review). More than 80% of prompts are under 100 words, demonstrating that pre-loaded context considerably reduces the need for explanation.
Four observational case studies illustrate the system's value. A 283-line backup specification, referenced in 74 sessions, produced zero corruption bugs. Documentation of UI synchronization patterns enabled first-attempt success on the following feature. Creating a spec before a drop-system refactor accelerated dozens of subsequent interactions. A 915-line network agent identified three subtle synchronization bugs that had resisted five prior attempts.
The author proposes six guidelines: a basic constitution delivers immediate gains; planning agents should automatically surface required specs; routing tables replace human memory; repeated explanations signal the need to create a spec; specialized agents unblock stalled sessions; and outdated specs constitute the primary failure mode.
Maintenance cost is estimated at 1-2 hours per week. The article demonstrates that documented knowledge compounds: each documented subsystem accelerates both its own future modifications and every adjacent feature that depends on it. The author acknowledges limitations (single-developer, observational) but provides one of the most detailed quantitative evaluations of AI agent-assisted development on a real, complex project.