A year ago, "AI observability" meant tracing LLM calls and looking at dashboards. The category has since fractured into four distinct segments: developer observability tools, governance platforms, agent security vendors, and infrastructure incumbents bolting on AI monitoring.
Microsoft open-sourced the Agent Governance Toolkit. ServiceNow acquired Traceloop and launched AI Control Tower. Cisco announced its acquisition of Galileo. ClickHouse acquired Langfuse. Palo Alto Networks acquired Portkey. Arthur AI launched Agent Discovery & Governance. Datadog shipped native LLM observability. The EU AI Act's next major compliance milestones arrive in 2026, with high-risk system obligations expected by late 2027 following the May 2026 Omnibus deferral.
Observability is table stakes. Every tool on this page captures traces. The question that matters now: what happens before the next step executes? Who enforces cost budgets mid-run? Who scans for PII in real time? Who governs MCP tool calls across your organization? Who stops a runaway agent at 3am — not alerts you about it on Monday?
Waxell covers the full arc. Here's how that stacks up.
One Platform. Five Products. The Full Arc.
Where does Waxell fit in the market?
Deep Comparisons
Each comparison below goes beyond the feature matrix — covering positioning, use-case fit, and when the other tool might be the right choice.
FAQ
What's the difference between AI agent observability and AI agent governance?
Observability captures what agents did — traces, LLM calls, tool invocations, costs. Governance controls what agents are allowed to do next. Every tool on this page provides some form of observability. Only a few — Waxell, Microsoft AGT, and Arthur AI — enforce policies during execution, before the next step runs. The distinction matters: a dashboard that shows you a $4,000 spend spike on Monday morning is observability. A budget control that halts the agent at $500 on Friday night is governance.
How does Waxell compare to Microsoft's Agent Governance Toolkit (AGT)?
AGT is an open-source library (MIT license) that evaluates policies before a tool call fires. If the call is allowed, AGT steps aside — that's its boundary. Waxell covers the full execution arc: pre-execution enforcement, mid-execution cost tracking via BudgetLedger, human-in-the-loop approval gates, PII detection, data-layer governance, and durable execution with checkpoint and resume. AGT requires YAML authoring and code deployments for policy changes; Waxell policies update through the platform UI with no deployments.
Is Waxell a replacement for LangSmith?
It depends on your stack. If you're all-in on LangChain and need deep LangGraph-native tracing, LangSmith's integration is purpose-built for that. If you use multiple frameworks — or if you need runtime governance, enforced cost controls, PII detection, or MCP governance — Waxell covers capabilities that LangSmith doesn't. Some teams run both: Waxell for governance and enforcement, LangSmith for LangChain-specific evaluation workflows.
How does Waxell compare to Datadog for AI agent monitoring?
Datadog connects AI agent traces to infrastructure metrics, APM data, and user sessions — correlation depth that standalone AI tools can't match. If your primary need is correlating agent behavior with backend infrastructure performance, Datadog's integration is strong. Waxell is purpose-built for agent governance: 50+ enforced policy categories, runtime cost controls, PII detection, content guardrails, kill switches, durable execution, and MCP governance. Datadog monitors; Waxell monitors and enforces.
What does Waxell do that Arthur AI doesn't?
Arthur AI leads on agent discovery — automatically scanning compute environments to find and catalog every AI application. Waxell leads on agent governance and execution — runtime policy enforcement with 50+ structured categories, durable execution with checkpoint and resume, MCP gateway governance, and a coordination layer for third-party agents. Arthur discovers the agents; Waxell governs them.
Can I use Waxell alongside another observability tool?
Yes. Waxell instruments at the SDK level and doesn't conflict with proxy-level tools (Helicone, Portkey) or framework-native tools (LangSmith). Some teams use Waxell for governance and enforcement while keeping a secondary tool for evaluation or experiment tracking.
Does Waxell support MCP governance?
Yes — at two levels. The Waxell MCP Gateway fronts 160+ upstream connectors with preventive controls: deny, redact, require approval, rate-limit, or cost-cap any tool call before it reaches the upstream server. Waxell Connect provides MCP governance for third-party agents, including rug-pull detection when upstream tools silently change their capabilities.
What compliance frameworks does Waxell support?
Waxell includes HIPAA, SOC 2, and PCI-DSS compliance profiles out of the box, with data residency options in US East and EU West. The platform maps to NIST AI RMF, the EU AI Act, and ISO/IEC 42001:2023, with industry overlays for financial services (Treasury FS AI RMF, SEC, FINRA), healthcare (HIPAA, 42 CFR Part 2), and other regulated sectors.
How is Waxell different from a gateway tool like Helicone or Portkey?
Gateway tools see HTTP requests at the proxy level — which model was called, how many tokens, what it cost. Waxell instruments at the SDK and runtime level — it sees agent reasoning, policy evaluation, cost attribution at every node, parent-child relationships between agents, and the full causal graph of execution. A gateway can rate-limit API calls; Waxell can enforce that a specific agent cannot exceed $50 across its entire spawn tree, halt an agent mid-run when it attempts to call an unauthorized tool, or pause execution for human approval before a financial transaction completes.
How long does it take to set up Waxell?
Observe: two lines of Python. Runtime: two decorators. Connect: zero code — add your existing tools and start. MCP Gateway: one endpoint. Most teams are running within a single session.
Can Waxell govern agents I didn't build — like Claude Code, Cursor, or custom GPT workflows?
Yes. That's what Waxell Connect does. It provides governance and coordination for third-party agents with no SDK and no code changes — including shared context, automatic handoffs, human-in-the-loop routing, and rug-pull detection.