Agent Steering

Agent Steering is a mechanistic interpretability technique that modifies an AI model's internal activations at runtime to control its behavior without retraining or prompting.

Agent Steering bypasses traditional prompt engineering by intervening directly in the model's residual stream. By injecting specific activation vectors (derived from contrastive pairs like 'helpful' vs. 'harmful') into the hidden layers during inference, operators can precisely dial in behavioral traits such as honesty, safety, or specific personas. This method is significantly more robust than prompting: it avoids the context window overhead and prevents the 'over-correction' issues common in text-based instructions. In production environments like the Kiro IDE or research frameworks using Sparse Autoencoders (SAEs), steering allows for granular, multi-vector control that maintains the model's core reasoning while locking it into a specific operational lane.

https://github.com/anthropics/anthropic-cookbook/blob/main/skills/activation_steering/guide.ipynb