Pilaro | The System Truth Layer for Agentic Coding

CATEGORIES

Go Deeper

Pilaro is an AI-native development system designed to keep software systems stable while AI agents build them. Instead of letting coding agents modify a repository freely, Pilaro runs every improvement through a structured pipeline of specialized agents that analyze, plan, implement, and verify changes. Each improvement progresses through six stages: 1. Discover The system analyzes the architecture and determines which components are impacted by the requested change. 2. Define The improvement is converted into a structured definition with clear intent, constraints, and acceptance criteria. 3. Reality Check The current codebase is evaluated against architectural contracts to understand the real state of the system. 4. Eligibility Check Pilaro generates a deterministic execution plan and determines which changes are safe to implement next. 5. Execute Coding agents implement the approved changes in controlled execution waves. 6. Verify & Deploy Verification agents test the results and confirm that the system still satisfies its intended behavior before deployment. At the core of Pilaro is a System Truth Layer. This layer maintains an authoritative understanding of the system’s structure and intent so that AI-generated changes remain visible, stable, and controllable over time. This approach allows teams to safely increase development speed while preventing architectural drift as AI agents become more autonomous. Pilaro can operate in two modes: Drive Mode – Pilaro plans and executes improvements autonomously. Guard Mode – Pilaro analyzes and verifies pull requests created outside the platform. Every improvement and agent execution is tracked, creating a full operational history of how the system evolves. The goal is simple: let AI move fast without letting software systems fall apart. II use Pilaro now to develop Pilaro and currently interviewing Engineers to get feedback if this would also benefit them AI Is providing more and more feedback as to what it wants more to avoid drift and go faster and feeding it the daily laterst research on benchmarks and agentic coding

Stack Used

LLMs (Claude / OpenAI/Gemini) TypeScript Node.js Supabase Vector embeddings GitHub API integration Static code analysis Agent orchestration pipeline System architecture registry