The Problem: Dynamic Scope Expansion

Autonomous agents are inherently unpredictable because their requirements evolve at runtime. A task that begins as a simple log analysis can quickly escalate into a request for GitHub history, Slack conversations, and eventually write access to sensitive repositories. Traditional software relies on static, pre-defined permissions, but agents require a system that can handle expanding goals while maintaining a limited blast radius. The core challenge is not increasing agent intelligence, but building a safety layer that allows agents to operate autonomously without granting them excessive, persistent access.

A Three-Pillar Runtime Architecture

To safely unlock agent autonomy, a runtime layer must sit beneath any model or harness, governed by three core principles:

• Containment: The agent must run within an untrusted boundary (a sandbox) where all security controls are enforced from the outside. This ensures that even if an agent is compromised or makes a mistake, its impact is physically restricted to its specific container.
• Scoped Capabilities: Instead of a monolithic sandbox with broad permissions, the runtime should grant access on a per-task basis. If an agent needs to search Slack for incident-related chatter, the runtime should provide a just-in-time tool scoped only to that specific incident channel, rather than full Slack access.
• Intent-Based Access: The runtime must evaluate the user's original intent to determine if a requested capability is appropriate. If an agent investigating a latency spike suddenly requests access to email, the runtime should recognize this as a deviation from the established intent and either deny the request or escalate it for human approval.

Portability and Orchestration

Safety mechanisms must be as portable as the agents themselves. A secure runtime should function identically whether the agent is running on a local developer machine, in a private VPC, or across distributed cloud environments. By decoupling the security policy from the model and the harness, developers can orchestrate complex workflows where multiple scoped agents collaborate, each operating within its own hardened, policy-compliant sandbox. This approach allows for "fanning out" tasks—such as reviewing multiple pull requests in parallel—while maintaining a consistent governance plane.