AI Agent Governance & Observability

What is AI agent governance?

AI agent governance is the practice of controlling, monitoring, and enforcing policy over AI agents running in production — covering what they're allowed to do, how much they're allowed to spend, what data they can access, and who can override or halt them. Waxell implements AI agent governance through a runtime policy engine that evaluates agent behavior before each execution step and returns structured enforcement: retry, escalate, or halt.

What's the difference between AI agent observability and AI agent governance?

AI agent observability is the ability to see what an agent did — capturing traces, LLM calls, tool invocations, token usage, and decision points. AI agent governance is the ability to control what an agent can do — enforcing policies, blocking actions, routing decisions to humans, and maintaining an audit trail. Waxell provides both: Waxell Observe captures full execution telemetry, and the governance engine enforces policy in real time before the next step runs.

How do you govern Claude Code or Cursor without changing any code?

Waxell Connect lets teams bring third-party agents — including Claude Code, Cursor, and custom GPT workflows — into a governed workspace with no code changes and no SDK required. Connect works at the coordination layer: registering agents, surfacing their activity, routing decisions to an inbox, and applying MCP governance policies to tool calls. There is no instrumentation step and no engineering work needed to start.

What is MCP governance?

MCP (Model Context Protocol) governance is the practice of applying policy, audit, and access controls to the tool calls made by AI agents through the MCP layer. Waxell Connect's MCP governance layer monitors every MCP tool call, checks it against active policies, scans for PII, and logs it to the audit trail.

How does Waxell compare to LangSmith for AI agent monitoring?

LangSmith is an observability tool for LangChain applications. Waxell instruments 200+ libraries across every major LLM provider, vector database, and agent framework, not just LangChain. Waxell also adds a governance layer that LangSmith does not have: runtime policy enforcement, human-in-the-loop approvals, cost budgets, PII detection, and kill switches — enforced during execution, not reviewed after.