How an AI agent prototype turned into a system with deadlines, a token budget, and roles in just a couple of days

▤ Summary

The piece describes how a simple AI-agent prototype evolved into a more disciplined system with deadlines, token budgets, roles, and orchestration rules. It explains the stack behind the project, including Python, LangGraph/LangChain, SQLite, MCP, and HTTP APIs. It also walks through practical design decisions such as task routing, supervisor logic, evaluation loops, and guardrails for retries and quality checks. The article focuses on engineering patterns and implementation tradeoffs rather than a company announcement or product release.

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