Bridging intent and execution in agentic systems

Amazon Science · 1 month ago

Researchers published a paper formalizing the intent-execution gap in AI agents, where mismatches occur between what language models intend and what the harness software actually executes, demonstrating that closing this gap without task-specific tuning achieves state-of-the-art results on benchmarks including SWE-Pro and Terminal-Bench2. The study introduces Simple Strands Agent, a lightweight harness implementation, and identifies concrete design principles such as requiring stronger text anchors for code edits, providing diff feedback after execution, and balancing reasoning with tool interactions through model-specific nudging. These findings suggest that optimal agent performance requires tight model-harness codesign rather than optimizing either component independently, with effective strategies varying across model families.