AiAgentArchitect: A Structured Construction Framework for Multi-Agent Systems from Concept to Production Grade

AiAgentArchitect is an open-source framework designed to help developers transform vague requirements into deployable production-grade multi-agent systems. It addresses pain points in agent development such as vague requirements, structural chaos, and deployment difficulties through a three-step pipeline (Process Discovery, Architecture Design, Entity Implementation), and supports seamless switching between Google Antigravity and Claude Code platforms.

Current agent system development faces three major challenges: vague requirements (lack of clear boundaries and function definitions), structural chaos (difficult code maintenance and collaboration due to varying architecture understandings), and deployment difficulties (many obstacles in transitioning from prototype to production). The design philosophy of AiAgentArchitect is to standardize and pipeline the agent development process, allowing developers to build complex systems like assembling building blocks.

The core of the framework is a three-step pipeline:

1. Process Discovery: Through interviews between agents using BPM/BPA technology and users, reverse-engineer vague requirements to generate AS-IS flowcharts and structured handover documents.
2. Architecture Design: Convert the output of Process Discovery into a complete blueprint, determine entity types, assign responsibilities, generate To-Be flowcharts, and produce entity architecture plans.
3. Entity Implementation: Convert the blueprint into directly usable .md files with high automation, reducing manual configuration workload.

AiAgentArchitect defines six atomic entity types, each with a clear prefix and responsibility:

• Workflow (wor-): An orchestrator that coordinates agents and execution steps;
• Specialized Agent (age-spe-): Performs domain-specific tasks;
• Supervisory Agent (age-sup-): Reviews and verifies outputs from other agents;
• Skill (ski-): Reusable capability packages (tools, APIs, etc.);
• Rule (rul-): Constraints to ensure quality consistency;
• Knowledge Base (kno-): Static context reference. This design makes the system highly modular and composable.

The framework adopts a dual-system architecture, where each entity exists simultaneously in the .agents/ (Google Antigravity format) and .claude/ (Claude Code format) directories, and is automatically kept in sync via git pre-commit hooks. Developers can switch seamlessly between the two platforms without worrying about format compatibility issues, reflecting ecological openness.

Each session automatically runs a three-role quality loop:

• Auditor: Verifies rule compliance, only reads rules without modifying content;
• Evaluator: Scores from four dimensions (completeness, quality, compliance, efficiency) and generates a weighted scorecard;
• Optimizer: Analyzes audit and scoring history to generate priority improvement suggestions (decision-making power rests with developers). Quality outputs are appended to qa-report.md, and the loop is non-blocking, only accumulating evidence for reference.

The framework implements a comprehensive cross-session memory mechanism:

• Context Ledger: Stores complete session records (step outputs, reasoning traces), with one file per session (YYYY-MM-DD-HH-MM-{project}.md) for precise state reconstruction;
• Memory: Lightweight snapshot (1-2KB) loaded at quick startup, containing project name, last checkpoint, and active status. Both are located in the project root directory (not platform directories), their content is ignored by git, but the directories themselves are tracked.

Application Case: The assistant-documentation-generator example system, fully generated by the framework, includes 5 specialized agents, 4 skills, 4 rules, 3 knowledge base files, 1 resource, a built-in quality assurance layer, and supports dual-platform export. Summary: AiAgentArchitect is not just a code generator but a methodological framework that attempts to turn agent development from an art into an engineering discipline. Outlook: As agent technology evolves, such tools will help developers build reliable systems more efficiently and find direction in complex ecosystems.