Vision¶

AI agents should feel safe and predictable¶

SafeAI exists because AI agents are becoming autonomous participants in production systems — reading data, calling APIs, writing code, managing infrastructure — and there is no standard way to enforce security boundaries around what they can see, do, and say.

SafeAI is the runtime boundary layer that makes AI agent systems safe by default.

What We Do¶

• Runtime security enforcement at input, action, and output boundaries
• Secret and PII detection before data reaches any LLM
• Policy-driven tool control with contracts, identity, and approval workflows
• Framework-agnostic integration with LangChain, CrewAI, AutoGen, Claude ADK, Google ADK, and any HTTP-based stack
• Audit trail and compliance with immutable logging and cost governance
• AI-powered configuration through the intelligence layer

What We Don't Do¶

• Replace model safety training or alignment research
• Provide hosted LLM inference or model fine-tuning
• Build application-layer business logic or workflow orchestration
• Offer proprietary, closed-source security — our enforcement engine must be inspectable

Decision Framework¶

We say no to features that:

1. Expand scope beyond runtime boundary enforcement without clear security benefit
2. Add framework coupling that breaks the framework-agnostic principle
3. Introduce non-determinism in the enforcement path (security decisions must be predictable)
4. Increase maintenance burden beyond what the maintainer team can sustain
5. Compromise auditability by hiding enforcement logic or decision rationale

Core Philosophy¶

Security at the boundaries¶

Every piece of data in an agent system crosses a boundary: input (user to agent), action (agent to tool), or output (agent to user). SafeAI enforces policies at these three boundaries, covering the complete data flow.

Least privilege by default¶

Agents start with no permissions and gain access only through explicit policy. Tool contracts, clearance tags, and capability tokens ensure agents can only do what they are authorized to do.

Deterministic enforcement¶

Given the same input and policy, SafeAI always produces the same decision. There are no probabilistic filters, no ML-based classifiers in the enforcement path, and no non-deterministic behavior. Security decisions must be predictable.

Invisible when working¶

SafeAI adds negligible latency and requires zero changes to agent application code when used through framework adapters. Developers should not feel the safety layer.

Framework-agnostic¶

SafeAI works with LangChain, CrewAI, AutoGen, Claude ADK, Google ADK, and any future framework. The core engine has zero framework dependencies. Adding support for a new framework means writing a thin adapter, not rebuilding the system.

Open source¶

Security infrastructure must be inspectable. SafeAI is open source so that security teams can audit the enforcement engine, researchers can verify the boundary model, and the community can extend it with new detectors, adapters, and policy templates.

The North Star¶

We want to reach a point where, when someone asks a team "How do you secure your AI agents?", the answer is:

"We use SafeAI."

Not because it is the only option, but because it is the obvious one — the way teams reach for well-established tools in other domains. A single runtime layer that handles detection, enforcement, auditing, and access control across every agent framework, every deployment model, and every compliance requirement.

That is the future we are building toward.