AgentForge: Execution-Grounded Multi-Agent LLM Framework for Autonomous Software Engineering

arXiv:2604.13120v1 Announce Type: cross Abstract: Large language models generate plausible code but cannot verify correctness. Existing multi-agent systems simulate execution or leave verification optional. We introduce execution-grounded verification as a first-class principle: every code change must survive sandboxed execution before propagation. We instantiate this principle in AGENTFORGE, a multi-agent framework where Planner, Coder, Tester, Debugger, and Critic agents coordinate through shared memory and a mandatory Docker sandbox. We formalize software engineering with LLMs as an iterative decision process over repository states, where execution feedback provides a stronger supervision signal than next-token likelihood. AGENTFORGE achieves 40.0% resolution on SWE-BENCH Lite, outperforming single-agent baselines by 26--28 points. Ablations confirm that execution feedback and role decomposition each independently drive performance. The framework is open-source at https://github.com/raja21068/AutoCodeAI.

Related

• FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning
• WybeCoder: Verified Imperative Code Generation
• Verify Before You Fix: Agentic Execution Grounding for Trustworthy Cross-Language Code Analysis
• SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context

Source: arXiv cs.AI | 2026-04-17