runtime-enforcement

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.

Reference implementation, 600-scenario benchmark, and reproducibility artifact for fail-closed pre-response routing.

Deterministic execution authorization for AI agents and automation systems

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

Runtime behavioral contracts for AI agents: define YAML contracts, check adapter outputs, and raise explicit contract violations. Agent Behavioral Contracts (ABC).

Runtime security and policy enforcement for tool-using autonomous AI agents

The open source runtime enforcement for AI agents.

👟 SUP: Sycophancy Under Pressure

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

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

VELLA = Verifiable Evidence Ledger & Logic Authority; deterministic pre-execution authorization and signed proof for AI agents and automated workflows.

AgentInterdict — runtime enforcement for autonomous AI agents. Trust context. Verify authority. Interdict unsafe action.

Runtime guardrails for AI agent MCP tools. Blocks dangerous tool calls at runtime with a default-deny policy proxy.

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

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

Evidence-coded map of constrained decoding, structured generation, guarantees, systems costs, and evaluation for language models.

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

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.