Bridging intent and execution in agentic systems
Amazon Science
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.
Why it matters
The harnesses that mediate between models and tools in agentic systems are becoming their own performance bottleneck, but a few simple design principles can fix what ails them.
