Comparisons
Waxell compared with LangSmith, Langfuse, Helicone, Arize, and the rest of the observability and governance field.
12 min readPortkey vs Waxell: Two MCP Gateways, Two Trust Models
Portkey, now Prisma AIRS AI Gateway, enforces MCP tool-call policy. So does Waxell — the difference is when each gateway re-checks the tool itself.
12 min readDatadog vs Waxell: Where Agent Policy Runs [2026]
Datadog AI Guard evaluates agent actions inside your app process. Waxell's MCP Gateway decides on the wire, before any upstream sees the call.
14 min readNutanix vs Waxell: MCP Gateway Governance Compared [2026]
Nutanix's MCP gateway governs agent access on infrastructure you run. Waxell's MCP Gateway secures every tool call in flight, hosted and self-serve.
9 min readAI Agent Reproducibility: 61% vs 90% Correct [2026]
Agent runs whose tool sequence differs from the baseline: 61% correct. Reproducibility is a recording problem, not a re-run problem.
14 min readLangChain vs Waxell: Two Perimeters Compared [2026]
LangChain governs the model call and the agents on its own stack. Waxell governs tool calls from assistants nobody built — and the laptop.
16 min readRunlayer vs Waxell: Where Does the Perimeter Sit? [2026]
Runlayer governs the tool layer - MCP calls, catalog, shadow MCP. Waxell governs the model call and workflow step too. A sourced 2026 comparison.
16 min readCrewAI vs Waxell: Who Writes the Policy? [2026]
CrewAI gives you hooks to write enforcement yourself. Waxell ships policy as a control plane across any framework, plus tools and employee devices.
13 min readTrueFoundry vs Waxell: Where Enforcement Lives [2026]
TrueFoundry enforces AI policy at the gateway boundary. Waxell enforces inside the agent, at the gateway, at each step, and on employee devices.
13 min readMaxim AI vs Waxell: Evaluation vs Enforcement [2026]
Maxim AI splits evaluation and gateway governance across two products; Waxell enforces policy across the whole agent lifecycle in one control plane.
15 min readFiddler AI vs Waxell: Two Control Planes Compared [2026]
Fiddler AI enforces inline at the gateway you already run; Waxell owns the gateway, the agent process, and the device.
15 min readSpeakeasy vs Waxell: MCP Governance Compared [2026]
Speakeasy gates MCP tool calls at the gateway before they run; Waxell extends the same enforcement past the gateway to the agents you build, self-serve.
15 min readLangfuse Alternative for AI Agent Governance [2026]
Looking for a Langfuse alternative with runtime enforcement, not just tracing? Waxell enforces policy at runtime while Langfuse observes.
9 min readArthur AI Alternative for Agent Governance [2026]
Arthur AI finds and evaluates your agents — but enforcement lands after the fact. Waxell enforces policy before each step runs.
13 min readBest AI Agent Governance Platforms in 2026
Enterprise AI governance platforms compared on discovery, enforcement, and GA status. See where pre-execution enforcement actually ships.
9 min readAI Agent Cost Enforcement: Before vs. After [2026]
Uber hit its 2026 AI budget by April. One company got a $500M Claude bill. Here's what changes when teams move from cost alerts to enforcement.
10 min readAgentic Architecture: Developer vs. Operator Authority [2026]
Gartner: 40% of enterprise apps run AI agents by end of 2026. The teams failing ask one question too late: who controls them in production?
8 min read3,800 GitHub AI Repos Breached: Agent Pipeline Risk [2026]
TeamPCP stole 3,800 GitHub internal repos via a poisoned VS Code extension—including Copilot and agentic workflow code. What teams running vendor AI agents need to do now.
7 min readWhat Is an AI Playbook? Prompts vs. Playbooks [2026]
Prompts you retype every session cost more time than you think. Here's what separates a prompt from an AI playbook that agents find and read automatically.
8 min readAgentOps vs. MLOps: What the Old Playbook Missed [2026]
88% of AI agent pilots never reach production. The tools aren't the problem—the operational playbook is. Here's what MLOps misses for agentic systems.
9 min readHuman-in-the-Loop vs Human-on-the-Loop for AI Agents
Teams block every agent action for approval — or monitor nothing. Neither works. Here's the risk-tiered oversight model that scales, and why the governance layer must enforce it.
21 min readMicrosoft Agent Governance Toolkit vs Waxell: Toolkit vs Platform
Microsoft released the Agent Governance Toolkit on April 2, 2026 — an open-source library that enforces policy on AI agent actions before they execute. This post walks through what AGT is, where it wins outright, where the gaps are, and how enterprise teams can run both together.
17 min readAgentic Governance vs AI Governance: A Runtime Guide [2026]
Agentic governance is the runtime control layer traditional AI governance misses — see how it differs, what a framework includes, and what the EU AI Act requires.
14 min readWaxell vs. Helicone: Cost Visibility vs. Runtime Control
Helicone tracks what your LLM calls cost. Waxell controls what your agents are allowed to do — including how much they're allowed to spend. Here's when you need each.
13 min readWaxell vs. Arize Phoenix: The Iteration Tool vs. the Production Control Plane
Arize Phoenix helps you iterate and optimize AI agents before shipping. Waxell governs what they can do after you ship. Here's when you need each.
15 min readWaxell vs. Braintrust: When Evaluation Isn't Enough
Braintrust optimizes AI agent quality in development. Waxell governs what agents can do in production. Here's when you need each — and when you need both.
14 min readWaxell vs. LangSmith: When You Need a Control Plane, Not a Dashboard
LangSmith gives you unmatched visibility into LangChain agents. Waxell governs what agents can do at runtime. Here's how to choose — and when you need both.
12 min readMCP Governance vs Observability: Why You Need Both [2026]
Most teams with MCP deployments have observability. Almost none have governance. Here's why that gap matters and what it costs when something goes wrong.