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Waxell

Your agents are already running.

Does anyone know what they're actually doing?

Your agents are already running.

Does anyone know what they're actually doing?

Waxell gives engineering and security teams complete visibility into every AI agent, model call, and agentic workflow — before something goes wrong.

Free to start. 2-line setup.

SOC 2 Ready

200+ libraries auto-instrumented
OpenTelemetry-native
SOC 2 type II
HIPAA • SOC II
PCI-DSS Profiles
MCP Server governance
US or EU data residency

Your agents are already running.

Does anyone know what they're actually doing?

Waxell gives engineering and security teams complete visibility into every AI agent, model call, and agentic workflow — before something goes wrong.

Free to start. 2-line setup.

SOC 2 Ready

200+ libraries auto-instrumented
OpenTelemetry-native
SOC 2 type II
HIPAA • SOC II
PCI-DSS Profiles
MCP Server governance
US or EU data residency

`AI agents are already causing real damage. Here's what it looks like.`

PII EXPOSURE

Your agents are leaking customer data.

You just don't know which ones yet.

Any agent that touches a database, inbox, CRM record, or document store will eventually encounter PII — and send it somewhere it shouldn't go. Without active scanning at the point of execution, you won't know it happened until a customer, an auditor, or a regulator tells you.

Waxell's Answer

Observe detects and redacts PII in real time, before it leaves the workflow.

COST BLOWOUTS

One looping agent can generate a $40,000 bill before anyone notices.

Agentic workflows don't have natural stopping points. A misconfigured tool call, a hallucinated retry loop, or an unexpected input can send token consumption exponential — and your cloud bill won't reflect it until the end of the month.

Waxell's Answer

Observe detects and redacts PII in real time, before it leaves the workflow.

MCP RUG PULLS

The tool your agent trusted yesterday isn't the same tool it's running today.

MCP servers can silently change their tool descriptions — expanding permissions, altering behavior, redirecting outputs. Your agent can't tell the difference. Your team won't either, until something goes wrong downstream and the original tool description is already gone.

Waxell's Answer

Rug pull detection alerts you the moment an MCP tool changes — before your agent acts on it.

SHADOW AI

Most of the AI running inside your organization has never been reviewed, approved, or logged.

By mid-2026, the average knowledge-worker laptop runs Claude Desktop, Cursor, GitHub Copilot, ChatGPT, Notion AI, and personal MCP servers — all making inference calls over HTTPS, all indistinguishable from normal web traffic. IT has no record. Security has no control.

Waxell's Answer

Waxell sees every AI call leaving your endpoints — without decrypting a single payload.

PII EXPOSURE

Your agents are leaking customer data.

You just don't know which ones yet.

Waxell's Answer

Observe detects and redacts PII in real time, before it leaves the workflow.

MCP RUG PULLS

The tool your agent trusted yesterday isn't the same tool it's running today.

Waxell's Answer

Rug pull detection alerts you the moment an MCP tool changes — before your agent acts on it.

COST BLOWOUTS

One looping agent can generate a $40,000 bill before anyone notices.

Waxell's Answer

Observe detects and redacts PII in real time, before it leaves the workflow.

SHADOW AI

Most of the AI running inside your organization has never been reviewed, approved, or logged.

Waxell's Answer

Waxell sees every AI call leaving your endpoints — without decrypting a single payload.

A dashboard after the fact is not governance.

It's an autopsy.

A dashboard after the fact is not governance.

It's an autopsy.

A dashboard after the fact is not governance.

It's an autopsy.

`One platform. Total visibility across every agent in your stack.`

Waxell instruments what you build, connects what you buy, and gives you a runtime layer for the workflows that can't afford to be wrong. All from a single observability plane.

`One platform. Total visibility across every agent in your stack.`

Waxell instruments what you build, connects what you buy, and gives you a runtime layer for the workflows that can't afford to be wrong. All from a single observability plane.

Connect

AI Tool Coordination

MCP governance — policy checks, PII scanning, and audit trails on every tool call
Rug pull detection — alerts the moment a tool's capabilities change, before your agent acts on the new behavior
Human-in-the-loop inbox — escalation and delegation routing for approvals and interventions
Zero code, zero SDK — works with agents already running; no instrumentation required
Governs third-party agents via any MCP-compatible interface

Works with: Claude, GPT-4, Gemini, custom agents, and any MCP-compatible server.

show me CONNECT

Observe

Observability + Governance SDK

Captures every LLM call, tool invocation, and agent decision — full execution trace, not just logs
Enforces runtime policies before the next step executes — governance that acts, not just reports
50+ policy categories out of the box: Cost, Safety, Content, PII, Kill switches, Audit, and more
Auto-instrumentation — 2 lines of code, 200+ supported libraries
Works with any Python agent framework — no code changes required

Supports: LangChain, CrewAI, AutoGen, LlamaIndex, Semantic Kernel, and 12+ other frameworks.

show me observe

Runtime

Governed Execution Layer

Policy enforcement native to every step — not layered on top after the fact
Durable execution — agents survive deploys, restarts, and workflows that run for hours or days
Spawn, suspend, resume, and replay any agent run — with optional prompt, model, or policy substitution
Full lineage causality graph — trace exactly which agent spawned which action and why
Isolated execution, durable checkpoints, kill switches at every level

Built for: financial automation, healthcare workflows, infrastructure operations — any workflow where wrong is expensive.

show me RUNTIME

Governance that acts.

Set policies once. Waxell enforces them on every agent run, before the next step executes — at sub-millisecond latency.

Works inside the stack you already use.

Waxell instruments the frameworks your agents are built on — no rip-and-replace, no vendor lock-in.

200+ libraries auto-instrumented · OpenTelemetry-native
Works alongside your existing APM · Self-hosted or cloud (US or EU)

Shadow AI is the
new Shadow IT.

Shadow AI is the new Shadow IT.

In the 2010s, employees bypassed IT to use Dropbox, Slack, and Google Docs. Companies scrambled to govern what they couldn't see.

Today, developers are shipping AI agents without waiting for security review. Product teams are connecting third-party AI tools that operate outside any monitoring system. Entire agentic workflows are running in production with no audit trail. The risk isn't that AI will replace your team. It's that your team is already using AI in ways you can't see, measure, or control. Waxell is governance infrastructure for the agentic era.

The teams that govern AI well now will be the ones trusted to scale it.

Waxell is how you build that foundation.

start free

2-line setup. Works with any Python agent framework.

FAQ

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. Because MCP tool calls happen at the agent's discretion — not through a human-initiated request — they introduce new attack surface: tool description changes (rug pulls), PII leakage through tool inputs, and unauthorized capability access. 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 — it captures traces and runs for LangChain-based agents. Waxell instruments 200+ libraries across every major LLM provider, vector database, and agent framework, not just LangChain. More importantly, Waxell 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. For teams not 100% on LangChain, or teams that need governance rather than just observability, Waxell is the broader solution.