devchilll/scope

What it solves

SCOPE is a governance framework designed for enterprise AI agents operating in high-stakes, mission-critical environments (such as banking) where safety, regulatory compliance, and auditability are non-negotiable. It prevents unsafe inputs from reaching the LLM and ensures agent actions are authorized and logged.

How it works

Built on Google's Agent Development Kit (ADK), SCOPE employs a "Defense in Depth" architecture across five core pillars:

1. Safety: Uses a fast ML-based pre-model gate (e.g., toxic-bert) to block unsafe requests in ~50ms before they ever reach the LLM, supplemented by LLM-based contextual checks.
2. Compliance: Enforces human-readable business and regulatory rules (e.g., PCI-DSS, HIPAA) that the agent must consult for every decision.
3. Permissions (IAM): Implements role-based access control (USER, STAFF, ADMIN) to ensure users and agents can only access authorized data and tools.
4. Escalation: Routes low-confidence decisions or high-risk actions (like large fund transfers) to a human-in-the-loop review queue via SQLite.
5. Observability: Maintains detailed audit trails and compliance-specific logs (SOC2, PCI-DSS) for every interaction and tool call.

Who it’s for

Enterprise developers and organizations deploying AI agents in regulated industries (finance, healthcare, legal) who need a production-ready layer of safety and oversight.

Highlights

• Pre-LLM Guardrails: Blocks toxic or unsafe content before the model is called, reducing costs and increasing safety.
• Policy-as-Code: Allows compliance rules to be defined in YAML or Python and cited by the agent in its reasoning.
• Granular IAM: Integrates identity management directly into tool execution to prevent unauthorized data access.
• Human-in-the-Loop: Built-in escalation protocols for complex or high-value cases.
• Regulatory Logging: Specialized logging modules for PCI-DSS and SOC2 compliance.