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technologyreview.com·2h ago
AI Research
arXiv CS.AI·May 28·6 min read
Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents
TL;DR
Large language model (LLM) agents often fail when their outputs don't match expected formats, requiring expensive model retraining. Runtime harness adaptation modifies the interface layer—the code that interprets and constrains LLM outputs—without retraining the model itself.
✦ Why It Matters
Engineers can fix LLM agent failures and adapt behavior without retraining, reducing deployment time and computational costs.
Key Takeaways
- Modify agent output interfaces at runtime instead of retraining models to save computation.
- Use harness-level constraints to enforce deterministic behavior and format compliance.
- Adapt agent schemas and error handling without touching model weights.
- Deploy rapid iterations on agent behavior by changing interface logic, not model parameters.
Full Summary
LLM agents are systems where language models generate decisions and actions within a software framework. Traditional approaches require retraining models when output formats change or fail, which is computationally expensive.
Runtime harness adaptation instead modifies the harness—the interface layer that translates raw LLM outputs into structured actions—at execution time without touching the underlying model weights. The technique maintains deterministic behavior by constraining outputs through the harness rather than model modification.
This approach preserves agent reliability while enabling rapid iteration on output schemas and error handling. Engineers can adapt agent behavior to new requirements or fix format mismatches without expensive retraining cycles.
The method demonstrates that interface-level adaptation is sufficient for many real-world agent deployment scenarios.