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.

Structural security, governance and execution for AI agents: validate actions before they touch files, credentials, network, APIs, or money.

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

Execution boundary for autonomous systems. Deterministic runtime enforcement of executable constraints before state-changing actions occur.

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

Open protocol for deciding when AI-generated scientific outputs are admissible to shape what science does next

Scoped authorization for AI-shaped scientific work

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

A certifying, model-free, minimal-intervention runtime shield for LLM agents: it repairs an unsafe tool-call trajectory with the least-cost enactable edit instead of blocking, and certifies every intervention for re-verification without the model.

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

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

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

Runtime governance for AI agents — PERMIT / ABSTAIN / FORBID enforced in-path, sealed with tamper-evident receipts that verify offline.

Reference implementation and executable evidence corpus for execution-prevention under the Unified Agency Architecture (UAA).