What is OpenClaw, and how to use autonomous agents in your business

If you've been anywhere near tech news in the past month, you've seen the hype around OpenClaw, formerly known as Clawdbot and Moltbot.

OpenClaw’s creator, Peter Steinberger sparked interest in his project by tweeting about the AI agent (originally named Clawdbot) as if it’s alive:

"So @clawdbot can modify and update itself, but I gated this behind a config since it's advanced. Turns out the bots don't gaf."

After thousands of people spun up their own OpenClaw projects, another developer vibe coded a social network for these agents called Moltbook. Screenshots of supposed agent-to-agent conversations went viral, showing agents secretly plotting, or having an existential crisis.

The Moltbook screenshots make great content. But it also obscures what's actually happening.

The tech press initially covered OpenClaw and Moltbook with awe, but reporters have since become more skeptical about how autonomous the agents really are:

“As the bots discussed everything from private email protocols to cryptocurrency sales to the nature of consciousness, much of what they said was nonsense. And some of their chatter was probably fed to them by their creators. But the bots were remarkably convincing as they seemed to discuss their own technical skills, their view of the world and their plans for the future.” - Cade Metz, NY Times

In this blog post we’ll cut through the FUD surrounding OpenClaw and autonomous agents. We'll explain the real innovation behind the project, why it matters, and how you can apply these patterns to your own company.

What OpenClaw actually is

OpenClaw is a free, open-source AI agent that runs on your own computer. It gained over 151,000 GitHub stars and has become the fastest-growing AI project in recent memory.

The core innovation of OpenClaw is a departure from the prompt-and-wait sequence that ChatGPT introduced to users. Instead of lying dormant until you type a command, OpenClaw is built to complete long running and recurring tasks without human intervention. The agent achieves this through automation, persistent memory, and continuous improvement.

Automation

OpenClaw has a built-in function called a "heartbeat" that prompts the model at set intervals to do work.

This means that while you sleep, OpenClaw can check your inbox for urgent emails, review your calendar, or continue working on an ongoing project.

Beyond the heartbeat, OpenClaw accepts webhooks from external services. A new GitHub PR, an incoming Slack message, or a calendar event starting can trigger OpenClaw to act. The agent doesn't need a human to initiate its work.

Memory

The first generation of AI agents forgot everything after a session. When ChatGPT first launched, and you tried a new chat about a project you discussed with it yesterday, it had no idea what you're talking about.

But now many agents, including OpenClaw, solves this problem with a persistent memory system. OpenClaw writes project context, user preferences, and learned information to local Markdown files. When the heartbeat wakes the agent, it reads its memory and picks up where it left off.

This is why OpenClaw seems like it "knows" you. It remembers your timezone, your coworkers' names, and which emails are from your employer versus personal contacts.

But there's no magic here, it's simply state management. The agent saves important context to disk and loads it on the next run.

Improvement

When OpenClaw encounters a problem and solves it, or makes a mistake and learns from it, it can write that knowledge into a "skill" file. Skills are reusable instructions the agent can access on future runs.

Over time, this compounds. An OpenClaw instance that's been running for weeks has accumulated skills specific to your workflows, your tools, and your preferences. Skills can also be shared across the community via a repository called ClawHub, so users benefit from each other's discoveries.

This is the learning loop that makes OpenClaw feel like it's getting smarter. It writes down what works and reads it later.

The security risks of OpenClaw

OpenClaw’s security implications have drawn serious scrutiny from cybersecurity experts. Because the agent runs with broad access to email, messaging platforms, file systems, and third-party services, misconfigurations can expose sensitive credentials and system access in ways traditional security models were not designed to handle.

Researchers and threat analysts have documented hundreds of OpenClaw control interfaces and admin endpoints visible on the public internet that leak API keys, authentication tokens, and even private chat histories, allowing unauthorized parties to view or reuse those credentials.

Beyond leaked secrets, the very capabilities that make OpenClaw powerful, like automated execution of shell commands and integration with messaging platforms, also expand its attack surface. If an agent installation or its extensions are compromised, attackers could issue commands, send messages on behalf of users, or access connected accounts undetected.

Prompt injection and malicious third-party extensions have already been observed distributing harmful code under the guise of helpful “skills,” highlighting how unvetted code can escalate privilege and exfiltrate data.

OpenClaw vs. Adapt - enterprise ready autonomous AI agents

OpenClaw proves that AI models can complete long running and recurring tasks. The next question for business leaders becomes, “How do I safely apply this to my business?

Adapt was built to answer that question. It brings the same core capabilities that make OpenClaw powerful, but architected from the ground up for security and reliability.

Automation with boundaries

Adapt can run in the background and during off hours safely.

You can ask Adapt to summarize your inbox every morning at 9AM, generate a weekly pipeline report every Friday, or alert you when a key metric deviates from its normal range. These scheduled tasks run reliably without requiring you to remember to kick them off.

Beyond schedules, Adapt responds to webhooks from external services. When a new lead appears in HubSpot or a support ticket escalates in Zendesk, Adapt can immediately begin a workflow without waiting for someone to notice.

Like OpenClaw, Adapt writes and executes code to complete complex jobs. It can query databases, call APIs, transform data, and generate visualizations.

But here's the difference: every Adapt task runs inside an ephemeral, sandboxed container. These are isolated cloud environments that spin up for the task and disappear when it's done.

Your local machine, your production databases, and your sensitive credentials are never exposed. The agent operates with least-privilege access, meaning it can only touch the systems and data you've explicitly connected. This is automation with guardrails, not automation with crossed fingers.

Memory, both personal and collective

OpenClaw's memory is personal by design. It remembers what matters to a single user running it on their laptop. Adapt was built for teams, so its memory functions at both the individual and organizational level.

At the personal level, Adapt learns your preferences, your communication style, and the projects you're working on. But the real power comes from shared context.

Adapt maintains an organizational knowledge base that captures how your business works: which systems contain which data, how teams collaborate, what terminology means in your specific context, and what workflows have worked well in the past.

When a Product Manager asks Adapt about customer churn and an Engineer asks about deployment frequency, Adapt draws on both the shared understanding of business operations and the specific context each role needs.

The PM gets churn data connected to feature releases. The Engineer gets deployment metrics connected to incident response. Both benefit from Adapt knowing that your company uses Linear for issues, Stripe for billing, and that "enterprise customers" means accounts over $50K ARR.

This collective memory compounds as more people use Adapt, making the entire organization faster over time.

Improvement, without drift

OpenClaw improves through self-modification. When it solves a problem, it writes a skill file. When it encounters an error, it updates its own behavior. This is powerful for personal use, but in a business context it creates risk. An agent that rewrites itself can drift in unpredictable directions.

Adapt also supports skills, but it anchors improvement in a predictable, structured process rather than unsupervised self-modification. Every task in Adapt follows a six-step workflow: Understand, Enrich, Reason, Execute, Verify, and Polish.

The Verify step is especially critical. Before an agent presents a result, it cross-checks outputs against source data. If the numbers in a report don’t align with what’s in Salesforce or another system of record, Adapt flags the mismatch instead of confidently presenting inaccurate information.

When a workflow proves effective and a user wants to reuse it, that pattern can be saved as a skill. Unlike OpenClaw, these skills do not evolve autonomously; they are versioned, reviewed, and shared intentionally across teams. This means your Sales organization can benefit from a reliable RevOps-built workflow without fearing that an overnight run subtly altered its behavior.

Improvement in Adapt happens through deliberate iteration and governance, not unchecked drift.

Enterprise-grade security

OpenClaw is an open source project, which means every user owns the security risk. You're responsible for hardening the machine it runs on, securing the API keys it stores, and monitoring what it does with full system access.

Adapt operates on a zero-trust architecture designed for enterprise deployment. Every task runs in a sandboxed container, an isolation layer that prevents code from escaping its environment even if something goes wrong.

Agents operate with least-privilege access, meaning they can only reach the specific systems and data sources you've explicitly connected. All data is encrypted in transit and at rest. Every action the agent takes is logged in an audit trail, so you can see exactly what happened, when, and why. Adapt is pursuing SOC 2 Type II certification, and for regulated industries, private VPC deployment options are on the roadmap.

Critically, permissions follow your source systems. If an employee doesn't have access to a Salesforce record, they can't use Adapt to get around that restriction. The agent inherits the access controls you've already defined, so there's no new attack surface to manage.

What this means for you

OpenClaw proves that autonomous, code-executing, memory-enabled agents are real and useful. The pattern works.

For startup leaders, the question isn't whether to adopt this model. It's whether to build and manage it yourself, or use a platform designed for business.

If you want to experiment personally, OpenClaw is a great project. If you want your whole company to move faster, with security, collaboration, and verification built in, that's what Adapt is built for.

FAQ’s

What is OpenClaw?

OpenClaw is an open-source autonomous AI agent designed to operate continuously on a user’s own computer.

Instead of responding only when prompted, it can run in the background, wake up on a schedule, and react to external events such as incoming messages or system changes.

The project has attracted significant attention because it demonstrates that AI agents can perform ongoing, task-oriented work without constant human supervision.

What is Clawdbot? Clawdbot was the original name of the project, a playful riff on Claude, the model from Anthropic that powered early versions of the agent. After Anthropic requested a name change, the project briefly became Moltbot. During that transition, developers launched Moltbook as an experimental social network for agents. Days later the name was ultimately changed again to OpenClaw.

Is OpenClaw sentient?

No, OpenClaw is not sentient and does not possess consciousness, awareness, or independent intent.
While interactions involving OpenClaw can appear lifelike, this effect comes from large language models generating fluent text combined with persistent state stored in files.

The agent does not “understand” its actions in a human sense. What looks like agency is the result of automation, memory retrieval, and probabilistic text generation rather than genuine self-awareness.

What makes OpenClaw innovative?

OpenClaw illustrates a shift away from short, stateless AI interactions toward long-running agents. It shows how an AI system can combine recurring execution, retained context, and adaptive behavior to complete work over time.

By coupling automation with persistent memory and reusable skills, OpenClaw makes tangible what had previously existed mostly as theory: that AI agents can function more like background processes than chat windows.

Should you** use OpenClaw at work? **OpenClaw can be useful for individual experimentation but running it in a workplace environment carries real security and compliance risks because it operates with broad system access. For most companies, especially those with sensitive data, it’s better suited to personal use rather than sanctioned production work.

What is a business alternative to OpenClaw?

For organizations, a strong alternative to OpenClaw is a managed platform such as Adapt.

While OpenClaw is optimized for individual experimentation and research, Adapt is designed for team-wide and enterprise use.

It applies the same underlying agent patterns of automation, memory, and code execution, but embeds them within controlled environments that emphasize security, collaboration, and reliability.

This makes it possible for businesses to benefit from autonomous agents without assuming the operational and security risks that come with self-hosted experimentation.

For more on how AI agents are changing business operations, see our report "AI for Startup Leaders" or explore how we use our own product in "How Adapt Uses Adapt."