Repository Level Development

Summary: Autonomous software engineering that operates at the scale of entire codebases, transforming static repositories into interactive, A2A-compliant agents capable of collaborative problem-solving. This approach automates the conversion of digital assets into executable agents that can participate in multi-agent systems.

Overview

Repository Level Development represents a paradigm shift from traditional code manipulation to autonomous codebase management. Rather than working with isolated functions or modules, this approach treats entire repositories as coherent units that can be systematically transformed into intelligent agents. The process involves four critical stages: environment setup, skill extraction, inner agent instantiation, and final agentization with proper interface specifications.

The core innovation lies in bridging the semantic gap between static code repositories and dynamic agent capabilities. This transformation enables repositories to become active participants in the Agentic Web, where they can autonomously execute tasks, collaborate with other agents, and provide specialized domain knowledge through standardized interfaces.

Key Details

Technical Architecture:

  • Four-stage agentization pipeline: Environment Setup → Skill Extraction → Agent Instantiation → A2A Compliance
  • Compliance with Agent-to-Agent Protocol for seamless interoperability
  • Generation of Agent Cards for self-description and capability discovery
  • Integration with Model Context Protocol for standardized tool communication

Critical Technical Challenges:

  • Environment Inconsistency: Repositories often lack proper dependency management and reproducible execution environments
  • Unstructured Skills: Code functionality exists in disparate formats that resist systematic extraction
  • Semantic Gaps: Bridging the divide between code implementation and discoverable agent interfaces

Performance Metrics:

  • Current state-of-the-art achieves 36.9% success rate (Claude Code on A2A-Agentization Bench)
  • Evaluation across 35 repositories spanning 9 domains
  • 522 evaluation instances testing both fidelity and interoperability

Failure Patterns:

  • Environment pre-configuration issues (dependency conflicts, missing prerequisites)
  • Skill construction problems (inability to extract coherent tools from code)
  • Capability specification defects (misalignment between actual and advertised functionality)

Relationships

Sources