Bridging intent and execution in agentic systems

Summary

AI agent performance increasingly depends on the harness mediating interactions between language models and tools rather than raw model reasoning. To address mismatches between model intent and harness execution, the authors developed Simple Strands Agent, an open-source single-agent harness. The architecture implements robust tool-interface practices, such as condensing middle log outputs from bash commands, enforcing line-aware string replacements with ambiguity checks, and returning diff files after edits for verification. Evaluated across benchmarks like SWE-Bench-Verified, SWE-Bench-Pro, and Terminal-Bench 2, these model-harness alignments yielded consistent performance gains across frontier models without task-specific tuning. The findings show that model families require tailored interface codesign alongside stable evaluation infrastructure to achieve optimal benchmark reliability.

Context

AI agent performance bottlenecks are shifting from model reasoning capacity to the harness software that mediates tool interaction and reasoning loops. Problem-specific optimizations like tuned prompts and specialized execution graphs frequently fail to transfer across different models or new model versions because they implicitly overfit specific model behaviors rather than addressing the core model-harness interface.

Approach / What changed

The authors formalize the intent-execution gap and introduce the Simple Strands Agent (SSA), an open-source single-agent harness. SSA handles bash output by condensing middle lines while keeping start and end logs, improves file editing via disambiguation prompts and line-aware string replacement anchors, and supplies post-edit diff files to verify state. The team evaluated SSA across SWE-Bench-Verified, SWE-Bench-Pro, and Terminal-Bench 2 using standardized environments and cloud infrastructure.

Takeaways

• Naively truncating bash logs drops critical status codes; condensing middle output while retaining start and end lines preserves vital command execution context.
• File-editing harnesses avoid unintended codebase modifications by requesting clarification upon finding ambiguous matches and enforcing line-aware replacement anchors instead of partial-line guessing.
• Providing immediate diff feedback after successful file edits closes the verification loop, allowing models to inspect exact additions and deletions before proceeding.

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