The Enterprise Strikes Back (Everything is Agent: Part 2)

Kevin Simback ๐Ÿท
@KSimback ยท Mar 17
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TLDR; this is part 2 of the series reviewing alternatives to OpenClaw. In Everything is Agent Part 1 , I covered the consumer/power user alternatives - the tiny claws and the security-first self-hosted options. In this article I explore the enterprise-grade solutions from the big hyperscalers to the startups and niche players that are trying to make agents safe enough for the Fortune 500.

Executive Summary

" Every company in the world needs to have an OpenClaw strategy " - Jensen Huang, CEO Nvidia

While I love Jensen's enthusiasm for OpenClaw, I would rephrase this a bit and just say that every company needs to have an agent strategy, because anyone who has played with OpenClaw knows that an enterprise is not going to run random GitHub skills with root access and call it a day.

So on March 16 at Nvidia's GTC conference, Jensen announced NemoClaw - an open source stack that adds privacy and security controls to OpenClaw making it more enterprise grade.

This is just the latest example in the agentic enterprise wave. In this article I explore the platforms that the "big boys" (OpenAI, Nvidia, Microsoft, Amazon, Google, and Salesforce) along with a handful of startups (CrewAI, Warp, Taskade, Adopt AI, OpenServ, Airtable Superagent, Superhuman Go, Perplexity, LangChain) are using to deploy secure, compliant and privacy-based agents at scale within large organizations.

The enterprise problem (and why OpenClaw scares them)

Enterprise doesn't have a tooling problem, it has a risk tolerance level that doesn't allow for tooling to be deployed willy-nilly despite the promise of productivity nirvana.

OpenClaw is powerful because it touches everything: shell, browser, files, messages, credentials. Enterprise sees that and thinks about permissions, identity, auditability, approvals, data security, legal, SOC 2, etc. and says "no way."

This is why the winners in this tier won't be the ones with the flashy "I built an agent to run my business" stories, they'll be the ones with the best governance and enough flexibility to allow agents to co-exist and integrate into large organizations, on their terms.

Unlike Part 1 where I grouped alternatives into "tiny claws" and "security-first" categories, the enterprise landscape doesn't break as cleanly. Instead, what you see is a spectrum from hyperscaler platform plays to startups carving out specific wedges. I've ordered them roughly by how much of the stack they're trying to own.

OpenAI Frontier: the "agents are employees" platform

Frontier, introduced on Feb 5, is OpenAI going straight for the agent management layer.

It's not an enterprise chatbot, it's a platform where agents have identity, onboarding, permissions, shared context, and governance. It's basically selling you the layer you'll run your digital workforce on.

"Frontier gives agents the same skills people need to succeed at work: shared context, onboarding, hands-on learning with feedback, and clear permissions and boundaries"

Why it matters:

  • Institutional knowledge is the moat in enterprise, not prompts
  • Governance is the gating item, not "can it write emails"
  • Managing agents built outside OpenAI is the correct posture if you want to own the layer

My take: Frontier is the most direct "we are the enterprise agent OS" claim from big tech.

The risk: enterprise platform gravity. Once you adopt the governance layer, you're not just buying a tool, you're buying a new dependency. And with OpenAI now owning OpenClaw itself, that dependency runs deep.

NVIDIA NemoClaw: the enterprise distribution of OpenClaw

NemoClaw is the one everybody's been talking about this week.

It's not an OpenClaw alternative. It's an enterprise-grade distribution of OpenClaw itself. Think of it like what Red Hat did for Linux, it took the open-source project that everybody loves and wrapped it in the security, governance, and support layer that enterprises need.

Jensen Huang just unveiled it at GTC 2026 and didn't hold back - he said every company now needs an OpenClaw strategy. That's NVIDIA going all-in on the "claw" ecosystem instead of competing with it.

What makes it worth exploring:

  • Single install with security built in: NemoClaw installs OpenClaw together with NVIDIA's OpenShell runtime (a new open-source security layer from their Agent Toolkit) and Nvidia's Nemotron models (although you can use other models). You don't need to figure out what security and governance wrappers to use and how to connect them to OpenClaw, NemoClaw already has it.
  • Hardware-agnostic: Despite being NVIDIA, it runs on AMD, Intel, and CPU-only setups. This is a deliberate play to become the default enterprise agent platform regardless of what silicon sits underneath, which tells you a lot about where NVIDIA sees the real value.
  • Local-first with a privacy router: Agents can run entirely locally on RTX PCs, DGX Spark, or DGX Station for maximum privacy. A privacy router handles the connection to cloud-based frontier models when needed, keeping everything within defined guardrails. This is the correct architecture for enterprise.
  • Apache 2.0 open source with enterprise tier: open core with paid enterprise extensions for managed infrastructure, compliance tooling, and support SLAs. Same playbook NVIDIA runs with NeMo for LLM training.

My take: NemoClaw is arguably the most significant enterprise agent announcement so far because it doesn't try to replace OpenClaw, it embraces it. NVIDIA is betting that the enterprise agent layer is about being the trusted infrastructure that makes the agents safe to deploy at scale.

The risk: it's brand new and based on my early attempt to install it, still has some bugs to iron out. That said, if Nvidia is backing it, I'm not fading it.

If you're already running OpenClaw and want to bring it into a corporate environment, NemoClaw is the most natural upgrade path available.

Google Agent Development Kit + Vertex Agent Builder: a full stack play

Google's approach is the most "platform thinking" of the hyperscalers.

"Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows."
"Vertex AI Agent Builder is our open and comprehensive platform that empowers enterprises to rapidly build, scale, and govern enterprise-grade agents grounded in your enterprise data. It provides the full-stack foundation and extensive developer choice you need to transform your applications and workflows into powerful, reliable agentic systems at global scale."

We shouldn't be surprised here that Google has a robust enterprise-grade agent platform given they have everything else that enterprises need, including their own suite of Gemini models. If you want scalability and reliability with real enterprise fit, this is your play.

Microsoft Agent Framework + Foundry Agent Service: distribution is the product

Microsoft has the most underappreciated advantage in enterprise agents: distribution.

So if you're a Microsoft/Azure shop, there's going to be a lot of inertia to work with their agent stack. Especially if you want agents to integrate into Teams, Outlook, SharePoint, the whole M365 universe.

"Agent Framework combines AutoGen's simple agent abstractions with Semantic Kernel's enterprise features โ€” session-based state management, type safety, middleware, telemetry โ€” and adds graph-based workflows for explicit multi-agent orchestration."

Why Agent Framework matters:

  • Enterprise workers live in M365
  • Adoption happens where the work already is
  • Governance and identity can plug into existing enterprise controls

My take: Microsoft will win a lot of agent deployments simply by being the default, not because they have the best agent UX. That's just how enterprise works.

Amazon Bedrock AgentCore: the cloud layer nobody's talking about

Amazon doesn't often come up in most agent conversations, but this is Amazon, the pioneer of cloud computing, of course they're going to have an agent product.

AWS doesn't get much hype because they don't ship flashy demos, but Bedrock AgentCore, their full-stack agentic platform, is definitely a serious contender.

It has runtime, gateway, memory, identity, browser, code interpreter, observability, and policy controls, all serverless and all framework-agnostic.

What makes it worth exploring:

  • Framework and model agnostic: you can bring any agent framework and any model.
  • AgentCore is a suite, not a single product: Runtime for secure serverless deployment, gateway for unified tool access with MCP support, memory for persistent context across sessions, identity for auth across AWS and third-party services, plus browser and code interpreter for enhanced capabilities. Each service works independently or together.
  • Enterprise security by default: complete session isolation, VPC connectivity, PrivateLink support, and real-time policy enforcement that intercepts tool calls before they execute. This is the level of controls that regulated industries need.
  • OpenAI Stateful Runtime partnership: AWS and OpenAI recently announced a co-developed Stateful Runtime Environment coming to Bedrock. This gives agents persistent working context including memory, tool state, workflow history, and permission boundaries across multi-step tasks. AWS is also the exclusive third-party cloud distribution provider for OpenAI Frontier, which is a significant strategic move.

My take: Amazon's agent play is the most "enterprise-boring" of the hyperscalers, and I mean that in a good way. Don't sleep on Amazon here.

The risk: AWS has a reputation for shipping powerful but complex services that require real engineering investment to use well. In today's age of vibe coding and productivity-maxxing, a complex agent platform can hurt adoption.

Salesforce Agentforce: the first mover with the installed base

Salesforce's angle is obvious: it has your customer workflows across service, sales, marketing and commerce, so if you already live inside Salesforce, Agentforce is a very low-friction place to put agents.

"Agentforce is the only enterprise agentic AI solution that elevates every experience by bringing together humans, applications, AI agents, and data. Now any company can safely deploy agents that work for their customers, suppliers, and employees 24/7. Teams can manage the complete agent development lifecycle with a robust set of tools to build, test, deploy, manage, and orchestrate AI agents at scale. Let humans do what they do best, and let Agentforce do the rest."

The value prop is not "look how smart the agent is" but rather about activating your CRM.

What makes it worth exploring:

  • Pre-built agent templates for core CRM workflows: service case routing, lead qualification, order management, campaign optimization. These are opinionated templates that map directly to how Salesforce workflows already operate.
  • Data Cloud as the context layer: Salesforce's advantage is that it already has your customer data, interaction history, and business rules. Agents that can reason over a unified customer profile are fundamentally more useful than agents that need to be wired up to five different data sources.
  • Atlas reasoning engine: Salesforce built a custom reasoning layer specifically for structured business data and CRM actions and designed to handle the multi-step decision-making that CRM workflows require.

My take: the risk is the classic Salesforce risk, you end up building a whole company inside the platform. Sometimes that's fine but the lock-in risk is real. But for teams that are already deep in the Salesforce ecosystem, the activation energy to get agents working here is lower than many other option in this article.

We just covered the "big boys" of the enterprise agent map, including 4 of the Mag7 which all have very robust offerings. Make no mistake, this is a huge market with an incredible TAM potential, so of course it will be crowded.

The question is - with all that firepower, is there any opportunity for startups to take share?

Let's take a look at a few that all have a potential place within enterprise -maybe not running the full stack like the big boys, but can certainly carve out a niche.

CrewAI: the framework that became an enterprise platform

CrewAI started as an open-source multi-agent orchestration framework as a way for Python devs to spin up "crews" of agents with defined roles, goals, and delegation chains.

Recently CrewAI has quietly evolved into something much bigger. They now claim 60% of the U.S. Fortune 500 are using it, and they've shipped CrewAI AMP (Agent Management Platform) which is a full enterprise control plane for building, deploying, monitoring, and scaling agent-driven workflows across an entire org.

What makes it worth exploring:

  • Open-source foundation with enterprise ceiling: the open-source framework (MIT licensed) gives you the multi-agent orchestration, role-based agent design, memory systems, delegation, and 100+ pre-built tool integrations. AMP layers on the managed infrastructure, observability, and governance that enterprise needs. You can start building for free and scale into the paid tier without rearchitecting.
  • No-code + pro-code in one platform: CrewAI Studio lets non-technical users build crews via drag-and-drop with an AI copilot. Engineers can go deeper with object and event-based Python APIs. This dual-track approach is smart because in most enterprises, the people who understand the workflow best are not the people who write the code.
  • Real-time tracing and observability: every LLM call, tool invocation, delegation decision, and output is traced with full visibility into reasoning, duration, token usage, and cost. This is table stakes for enterprise and CrewAI does it.
  • Agent training with human feedback: you can train agents to improve accuracy over time, either automatically or with human-in-the-loop input during live executions. For critical workflows where you need precision, not just vibes, this matters.

My take: CrewAI occupies a unique position in the landscape. It's not a hyperscaler platform trying to own the whole stack, and it's not a tiny startup with little traction. It's the open-source framework that actually made the leap to enterprise platform and it did it by solving the right problem: most teams don't want to build agent infrastructure from scratch, they want to define what agents need to do and let the platform handle the how.

The risk: CrewAI's high-level abstractions are a double-edged sword. When things work, it feels like magic, but when things break, the "black box" nature of the orchestration can make debugging painful. And as they move upmarket into enterprise, they'll increasingly compete with the hyperscaler platforms that have deeper pockets and existing procurement relationships.

Warp Oz: dev teams need orchestration, not more copilots

Warp Oz is not trying to be your personal assistant, it's trying to be the orchestration layer for dev teams who are already running multiple coding agents - more like a new "agent ops" category.

What makes it worth exploring:

  • CLI and web launch: spin up agents from your terminal or a web dashboard, schedule them like cron jobs, and parallelize work across repos. This maps to how dev teams actually work.
  • Unified model access: one interface across multiple LLM providers, so your agents aren't locked to a single model's strengths and weaknesses.
  • Session management and replay: as soon as you have more than one agent, you need logs, traces, and the ability to replay what happened. Warp builds this in.
  • Built for the multi-agent reality: most dev teams in 2026 are already running Claude Code, Cursor, Copilot, and other coding agents simultaneously. Warp isn't trying to replace any of them, it's the coordination layer above them.

My take: this is less sexy, more useful. The "agent ops" category barely existed six months ago but it's going to be a real market. If you're running a dev team with multiple coding agents and no orchestration layer, you already know the pain.

Taskade: the managed OpenClaw vibe for teams

Taskade is what a lot of teams actually want - just give me a workspace where I can spin up a handful of agents with different roles, attach tools and knowledge, collaborate, and not think about servers.

If you're a small team, Taskade's pitch is strong:

  • Multi-agent teams
  • Multi-model support
  • No-code setup
  • Enterprise posture (SOC 2)

My take: managed platforms like this will eat a ton of the long tail, because most people don't actually want to self-host, they just want outcomes.

Adopt AI: enterprise glue is a business

Adopt AI helps turn existing apps into agentic systems, connecting to legacy and modern stacks.

This may be a stepping stone for organizations with a lot of tech debt, which is the ugly reality of enterprise environments.

Adopt AI makes it easier for organizations dealing with ancient internal APIs, permissions spaghetti or just face internal blockers like "we can't move fast because legal."

My take: it's all about getting the customer moving toward agentic AI, and Adopt AI has an opportunity to build a wedge into the enterprise operating model by tackling legacy issues head-on.

OpenServ: teams of agents, marketplace energy

OpenServ is interesting because it's more than just an agent builder - it allows you to build teams of agents, ship them, orchestrate them, monetize them, and optionally bridge into web3 execution.

  • SDK for building capabilities
  • Platform for orchestration
  • Configurable autonomy levels
  • Marketplace and distribution vibes

My take: this category is going to be real - people will build agents like they build Shopify apps. The only true web3-oriented company on this list, so worth paying attention to if you follow that space.

The hard part is trust. If you want marketplaces for agent capabilities, you need security posture, reviews, sandboxes, and real permission models. Otherwise it becomes ClawHub, but with higher stakes.

Airtable Superagent: research workflows, productized

Superagent is basically multi-agent research and analysis as a product. It's not trying to be the agent for everything, it's trying to be the agent for:

  • Research
  • Synthesis
  • Analysis
  • Interactive outputs
"Superagent, our first standalone product built on multi-agent coordination. When you ask Superagent a question, you're not getting one AI assistant doing sequential tasks. You're getting a coordinating agent that plans the work, deploys specialists who work in parallel, and synthesizes their output into a finished deliverable you can immediately use."

Why it matters:

  • Multi-agent parallelism is perfect for research tasks
  • Data source integration is the moat
  • Interactive matrices and cards are what agent output should look like in enterprise, not a wall of text

My take: Superagent is one of the cleanest examples of agents becoming a UI paradigm, not just a chatbox.

Superhuman Go: the agent layer, not the platform

Superhuman Go is a different angle - it sits on top of the million apps you already use.

What makes it worth exploring:

  • Cross-app overlay: rather than forcing you to move into yet another workspace, Go sits on top of whatever you already use, Gmail, Notion, Slack, Salesforce, the works. The agent lives where the work happens, not in a separate window.
  • Agent store model: if you can become the layer where agents live across apps, you get distribution and network effects. Think browser extensions, but for agentic capabilities.
  • Low adoption friction: for teams that don't want to rethink their entire stack, the overlay model is the fastest path to "agents are doing things for me."

My caution: overlays can feel magical until they hit the edge cases. The difference between a demo and a daily driver is whether it survives the messy reality of modern work. Cross-app integration also means cross-app failure modes, and debugging gets hard when the agent touches five apps in one workflow.

Perplexity Computer: multi-model is not a gimmick

Perplexity Computer is the first big platform that's basically saying, "no single model wins, orchestration is the product."

"Perplexity Computer unifies every current AI capability into a single system. Computer is a general-purpose digital worker that operates the same interfaces you do. It is the next evolution of AI. Chat interfaces have answers, while agents can do tasks. Perplexity Computer is a system that creates and executes entire workflows, capable of running for hours or even months."

I debated on whether this should be considered an enterprise-grade product or just a more refined consumer-based product, but ultimately I landed on this as an enterprise solution that's most likely to be rolled out at an individual level. Existing employees using Perplexity Computer to manage digital workers, but with multi-model routing as the core.

The risk is that it doesn't have some of the enterprise orchestration and security elements that the other solutions have, so it things go haywire, debugging could be problematic, thus why I view it more as an enterprise-grade agent-based tool rather than a platform for deploying an agentic workforce.

That said, if Perplexity nails observability, they could be a serious contender at the organization level.

LangChain + LangGraph: the orchestration layer beneath the platforms

LangChain is a different animal from everything else in this article, but deserves a mention. It's not really an alternative to OpenClaw, but it is an agent orchestration infrastructure that is being used by enterprise.

Here's why it matters for this article: if you're evaluating enterprise agent solutions, you're going to encounter LangChain. It's under the hood of a lot of what's out there and understanding where it sits in the stack helps you make better decisions about everything else.

What makes it worth knowing about:

  • LangGraph is the agent runtime: stateful multi-agent orchestration with complex control flows, human-in-the-loop patterns, persistence, and memory. If you need deterministic, multi-step agent workflows with full control over the graph of execution, this is what serious teams are building on. It's MIT licensed and framework-agnostic on models.
  • LangSmith is the enterprise layer: observability, tracing, evals, and deployment. 300+ enterprise customers, 15 billion+ traces processed, 100 trillion+ tokens tracked.
  • Deep Agents for long-running work: LangChain's newer agent harness goes beyond LangGraph with built-in task planning, sub-agent spawning, long-term memory, and context management for agents that run for minutes or hours across dozens of steps.
  • Framework-agnostic and cloud-agnostic: works with every major model provider, runs on AWS, Azure, GCP, or self-hosted.
  • NVIDIA integration: as of GTC 2026, LangChain announced a comprehensive integration with NVIDIA combining LangSmith + LangGraph + Deep Agents with NVIDIA's Agent Toolkit, Nemotron models, NIM microservices, and OpenShell. They also joined the Nemotron Coalition. This positions the LangChain stack as the builder's on-ramp to NemoClaw and the broader NVIDIA enterprise agent ecosystem.

My take: LangChain/LangGraph is not an OpenClaw alternative. Nobody is choosing between OpenClaw and LangGraph as they solve different problems at different layers. But LangChain is increasingly the connective tissue of the enterprise agent stack.

The question is whether LangChain can continue to move up the stack without getting squeezed. From below, simpler frameworks like Pydantic AI are gaining traction. From above, the hyperscaler platforms are building their own orchestration layers and may eventually commoditize what LangGraph does today.

A quick summary of the enterprise alternatives

The "Big Boys":

  • OpenAI Frontier - best for teams that want the agent management layer and are willing to go all-in on OpenAI's ecosystem
  • NVIDIA NemoClaw - best for teams who want OpenClaw but need an enterprise-grade upgrade path with security and local-first deployment
  • Google ADK + Vertex - best for teams that want optionality, open standards, and integration with Google's data plane
  • Microsoft Agent Framework - best for teams that live in M365 and want agents deployed where workers already are
  • Amazon Bedrock AgentCore - best for teams whose infrastructure already runs on AWS and want agents that fit into existing security and compliance posture
  • Salesforce Agentforce - best for teams deep in Salesforce who want CRM workflows to become agentic with minimal lift

The Startups:

  • CrewAI - best for teams that want to go from idea to production multi-agent workflows fast, without being locked into a single cloud provider
  • Warp Oz - best for dev teams running multiple coding agents who need orchestration and ops
  • Taskade - best for small teams that want managed multi-agent workspaces without self-hosting
  • Adopt AI - best for enterprises with legacy integration nightmares who need a no-code agent glue layer
  • OpenServ - best for builders who want to create, orchestrate, and monetize teams of agents or have a desire to plug-in to web3
  • Airtable Superagent - best for research-heavy workflows that need multi-agent parallelism and interactive outputs
  • Superhuman Go - best for teams that want agents as a cross-app overlay without changing their existing stack
  • Perplexity Computer - best for teams that want multi-model orchestration without building the routing layer themselves
  • LangChain / LangSmith - best for teams that want framework and model agnostic orchestration infrastructure

My Summary Take

You won't win the enterprise with the smartest model, and most likely we see intelligence commoditize as models get better over time.

The winners in the enterprise space will be the platforms that solve:

  • Integration
  • Identity
  • Permissions
  • Observability
  • Auditability
  • And the human desire for approvals and accountability

And they'll win because they can do it all safely, repeatedly, with logs, and with sane boundaries.

Everything is agent. Enterprise is just going to force us to grow up.