FriendlyData VS Arclasp

Arclasp's answer:

Arclasp governs AI-agent workflows using accumulated workflow state rather than evaluating each action in isolation. It can apply cumulative controls, require human approval before consequential actions proceed, and preserve signed, hash-linked governance evidence of what happened.

Arclasp's answer:

Arclasp is designed for teams that need governance inside the execution path of agentic workflows. The backend makes authoritative allow, flag, approval, or deny decisions while the Python SDK integrates governance into the application workflow. It is especially useful when risk emerges across several individually acceptable actions, such as cumulative financial exposure, rather than from one API call alone.

Arclasp's answer:

AI engineering teams building agentic applications that can take consequential actions, especially teams that need runtime policy enforcement, human approval, workflow-level controls, and auditable governance evidence.

Arclasp's answer:

Python, FastAPI, PostgreSQL, SQLAlchemy, Next.js, and TypeScript. The public Python SDK also provides integrations for LangChain, LangGraph, CrewAI, and MCP.

Arclasp's answer:

Arclasp really started from one thing that kept bothering me: an individual action can look completely safe while the workflow around it becomes risky. A lot of controls are built to judge the action in front of them. Can this agent call this tool? Is this request over a threshold? Does this one operation look dangerous? That works up to a point. But agents don't always act once and stop. They work through sequences, and risk can build across those sequences.

A $4,000 commitment might be fine. Another $3,000 might be fine too. Then another $4,000 comes in. That last action is still only $4,000, but the workflow is now sitting at $11,000. That was the idea that eventually became Arclasp: govern the workflow, not just the API call. From there, it grew into a runtime governance layer that keeps track of workflow state, makes policy decisions outside the model, pauses actions for human approval when needed, and leaves behind evidence of those decisions that can be checked later.