Tracking Capabilities for Safer Agents

• URL: http://arxiv.org/abs/2603.00991v1
• Date: Sun, 01 Mar 2026 08:39:37 GMT
• Title: Tracking Capabilities for Safer Agents
• Authors: Martin Odersky, Yaoyu Zhao, Yichen Xu, Oliver Bračevac, Cao Nguyen Pham,
• Abstract summary: Instead of calling tools directly, agents express their intentions as code in a capability-safe language: Scala 3 with capture checking.<n> Scala's type system tracks capabilities statically, providing fine-grained control over what an agent can do.<n>Our experiments show that agents can generate capability-safe code with no significant loss in task performance.
• Score: 2.9897366166831265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI agents that interact with the real world through tool calls pose fundamental safety challenges: agents might leak private information, cause unintended side effects, or be manipulated through prompt injection. To address these challenges, we propose to put the agent in a programming-language-based "safety harness": instead of calling tools directly, agents express their intentions as code in a capability-safe language: Scala 3 with capture checking. Capabilities are program variables that regulate access to effects and resources of interest. Scala's type system tracks capabilities statically, providing fine-grained control over what an agent can do. In particular, it enables local purity, the ability to enforce that sub-computations are side-effect-free, preventing information leakage when agents process classified data. We demonstrate that extensible agent safety harnesses can be built by leveraging a strong type system with tracked capabilities. Our experiments show that agents can generate capability-safe code with no significant loss in task performance, while the type system reliably prevents unsafe behaviors such as information leakage and malicious side effects.

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