Governance-as-Code for AI agents. Declarative constraints with deterministic enforcement. Provisioning Identity, Tool-based rules, Brokering Credentials & Ensuring safe deployment

DCEE is a lightweight Python framework for validating data against contracts and enforcing SLA rules. Built on pandas and boto3, it provides simple, fast data validation without heavy dependencies.

Secure runtime for multi-agent AI. Kernel sandboxing (seccomp-bpf), real-time PII redaction, Merkle audit trails.

Lightweight runtime enforcement for agentic AI. PII masking, policy checks, and Merkle audit trails as a decorator.

👟 SUP: Sycophancy Under Pressure

Deterministic pre-execution gate for AI agents (fail-closed, YAML policy)

Stop AI agents before they do damage — behavioral sequence detection blocks credential exfiltration, config harvesting, and secret leakage before the network call fires

The open source runtime enforcement for AI agents.

In this project, we design and develop a framework leveraging formal runtime enforcement approaches to enforce the lifecycle constraints of a document at runtime, preserving its integrity and privacy using cryptographic approaches alongside.

AIP security plugin for OpenClaw: skill signing, capability manifests, runtime enforcement

Enforce tool-usage contracts on agent tool calls — block before side effects.

External kill switch for autonomous runtimes. Validate at enforcement boundaries. Revoke to halt execution.

Workload-scoped runtime enforcement for AI workloads. Deterministic governance at the workload boundary.

Compiler-Kernel Co-Designed execution integrity enforcement using Policy-Carrying Code (PCC) and eBPF-LSM.

Official Python SDK for MachineID - simple device registration and validation for AI agents.

Runtime governance infrastructure for autonomous AI agents. VGS-001 to VGS-011. DOI: 10.5281/zenodo.20264923