TACT: Mitigating Overthinking and Overacting in Coding Agents via Activation Steering

Researchers introduce TACT, a technique using activation steering to detect and correct 'agent drift' in language model coding agents, where models either repeatedly reason over known information or issue tool calls without proper reasoning. The method improves task resolution rates by 4.8-5.8 percentage points across multiple benchmarks while reducing steps needed to complete tasks by up to 26%.

Agent drift represents a critical failure mode in AI systems designed to handle complex, multi-step software engineering tasks. As language models tackle longer problem-solving trajectories, they increasingly fall into predictable failure patterns: overthinking (circular reasoning on existing information) and overacting (executing actions without sufficient evidence or integration of recent observations). TACT addresses this by treating drift as a steerable phenomenon within the model's internal representation, specifically within residual streams where activation patterns can be linearly separated along distinct axes. The research demonstrates that these failure modes have discernible geometric signatures in model hidden states, enabling detection and correction before behavioral failures manifest.

This work builds on the broader movement toward interpretability-driven AI safety, where understanding and steering internal model dynamics offers more direct control than post-hoc filtering or reinforcement learning approaches. As AI agents increasingly handle real-world software engineering tasks, the ability to maintain consistent reasoning quality across long horizons becomes economically important. The significant performance gains on established benchmarks—SWE-bench Verified, Terminal-Bench 2.0, and CLAW-Eval—suggest practical utility for production systems.

For developers and companies deploying AI coding assistants, this represents a path toward more reliable agents without requiring extensive retraining. The technique's applicability across different model architectures (Qwen and Gemma) indicates generalizability. The reduction in steps-to-resolve carries direct efficiency benefits, lowering computational costs per task. However, the approach remains within research-to-engineering transition territory, requiring integration into production pipelines and validation against edge cases in real deployment scenarios.

• →TACT detects agent drift by identifying linear patterns in hidden states that distinguish overthinking and overacting from calibrated behavior with 0.9 AUC.
• →Activation steering at test time improves task resolution rates by 4.8-5.8 percentage points without model retraining.
• →The technique reduces computational steps required to resolve tasks by up to 26%, lowering execution costs.
• →Agent drift represents a steerable phenomenon in residual streams, enabling direct control over long-horizon reasoning stability.
• →Results generalize across multiple model architectures and software engineering benchmarks, suggesting broad applicability.