The approval and accountability layer for agentic AI. Identity → Policy → Approval → Trace. Try: npx sidclaw-mcp-guard demo

System of record for AI tool risk: inventory, policy enforcement, approvals, and audit-ready evidence.

ClawFlow: A lightweight Agent Runtime / AgentOS Kernel for next-generation personal AI agents

ForceField Python SDK -- AI security in 3 lines of code. Prompt injection detection, PII redaction, security evals, tool governance. GitHub Action, pre-commit hook, Homebrew, VS Code extension.

Run a Neura Relay Action Card and receive a governed Decision Receipt before execution.

Deterministic pre-execution gate for one credentialed tool request: explicit policy, allow/deny decision, and inspectable decision artifact.

Paid remote MCP for schema drift checks, tool-schema approvals, compatibility receipts, breaking-change explanations, and release audit logs.

Harness engine for AI Agents. From demo to production.

Deterministic security architecture model for AI agent systems with deterministic permission and tool control.

Fleet-analyze a directory of MCP Tool Cards. Counts by side-effect class, PII/secrets exposure, reversibility, human-approval-required; surfaces approval gaps (destructive tools missing human_approval_required: true). Library + CLI.

Python FastAPI service for evaluating MCP server and tool policies, trust posture, destructive-action controls, and operator-facing review workflows.

Deterministic security control layer for agent tool calls.