Peeking Inside AI Agents: Mechanistic Interpretability for Tool Use

Summary

AI agents are increasingly deployed in high-stakes enterprise workflows, but their reliability is undermined by tool-use failures that are difficult to diagnose. Agents may skip required tool calls, invoke tools unnecessarily, or take actions whose consequences only become visible after execution. Existing observability methods—prompt analysis, output scoring, and logging—are all external and reactive, surfacing problems only after the model has already acted. In long-horizon settings, an early tool mistake can alter the entire trajectory, increase token consumption, and create downstream safety and security risks.

This paper introduces a mechanistic interpretability framework that reads model states before each action to infer both whether a tool is needed and how consequential the next tool action is likely to be. Using Sparse Autoencoders (SAEs) and linear probes, the framework decomposes activations into sparse features, identifies the internal layers and features most associated with tool decisions, and tests their functional importance through feature ablation. The probes were trained on multi-step trajectories from the NVIDIA Nemotron function-calling dataset and validated on GPT-OSS 20B and Gemma 3 27B models.

Key Contributions

• Internal observability layer: First systematic application of mechanistic interpretability to agent tool-use monitoring, providing visibility into model states before action execution
• SAE-based feature decomposition: Identifies sparse features in model activations that correlate with tool necessity and consequence prediction
• Functional validation via ablation: Confirms causal importance of identified features by removing them and observing behavioral changes
• Cross-model validation: Demonstrates framework works across different model architectures (GPT-OSS 20B and Gemma 3 27B)
• Practical failure diagnosis: Surfaces deeper causes of agent failure in long-horizon runs where early mistakes cascade

Implications

For Researchers

This work bridges mechanistic interpretability and agentic systems, opening a new research direction for understanding how models make tool-use decisions internally. The SAE-based approach provides a template for studying other agent behaviors beyond tool calling, such as planning, memory retrieval, and multi-step reasoning.

For Developers

The framework offers a practical debugging tool for agent systems in production. By monitoring internal states before tool calls, developers can catch potential failures—like unnecessary tool invocations or missed required calls—before they execute. This is particularly valuable for long-horizon enterprise workflows where early mistakes compound.

For Users

End users benefit from increased reliability and safety in AI agent systems. The ability to detect risky tool calls before execution reduces the chance of costly errors in high-stakes domains like finance, healthcare, and legal workflows.

References

• https://arxiv.org/abs/2605.06890v2