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

TL;DR

This paper introduces TACT, a method to detect and correct agent drift in language model agents by steering their internal activations, improving performance on complex software engineering tasks.

Contribution

The paper presents a novel activation steering technique to identify and mitigate overthinking and overacting in language model agents, enhancing their reliability.

Findings

Activation states can linearly separate overthinking and overacting behaviors.

Steering activations reduces agent errors and improves resolve rates.

TACT outperforms baselines on multiple benchmarks, reducing steps-to-resolve.

Abstract

When language model agents tackle complex software engineering tasks, they often degrade over long trajectories, which we define as *agent drift*. We focus on two recurring failure modes *overthinking* and *overacting*, i.e., where the agent repeatedly reasons over information it already has, and where it issues tool calls without integrating recent observations or acquiring new evidence. In this paper, we introduce TACT (Think-Act Calibration via activation Steering), to detect and mitigate agent drift in the residual stream before it surfaces as a behavioral failure. In specific, we label trajectory steps as overthinking, overacting, or calibrated, and find that their hidden states can separate linearly along two *drift axes*, pointing from calibrated behavior toward each failure mode (AUC $\approx$ 0.9). To mitigate agent drift, we project each step's activation onto these axes at test time and pull drifted ones back toward the calibrated region. Experiments show that TACT outperforms unsteered baselines across SWE-bench Verified, Terminal-Bench 2.0, and CLAW-Eval, lifting average resolve rate by $+5.8$ pp on Qwen3.5-27B and $+4.8$ pp on Gemma-4-26B-A4B-it while cutting steps-to-resolve by up to $26\%$. These gains frame agent drift as a steerable direction in the residual stream, and position TACT as a viable handle for reliable long-horizon agents.