I Spent Three Weeks Using OpenClaw. Here's What I Learned.

It started with curiosity.

Three weeks ago, I didn't have a problem to solve. I wasn't trying to automate a business process or build an AI startup. I just wanted to see what this thing could actually do.

I'm not going to lie, the learning curve is real.

I've botched more instructions than I can count. I've broken things. I've a few times redeployed. I spent hundreds on token usage. I've overcomplicated workflows that should have been simple. There were moments where I almost scrapped it entirely.

For those of you who don't know what OpenClaw is, it's an AI agent system that runs on your own machine and can use your files, APIs, and tools to actually perform tasks and not just answer questions. Because it lives outside the browser or chat interface, it's inherently more powerful… and yes, more risky.

But here's what I learned:

The more you give it, the more you get.

The magic is that OpenClaw doesn't live inside a browser tab. It lives on a machine.

It runs as an always-on agent host: on a small Ubuntu server, a VPS, or a Mac mini - operating independently from your browser.

That changes everything.

Because once it's running on a dedicated machine, it can access a file system, your APIs, your scripts, your tools, and securely configured credentials. You can tell it to:

• Read your calendar and check emails
• Generate and publish content
• Automate workflows and run scripts
• Orchestrate multi-step tasks
• Book a tee-time or squash court

And it doesn't just respond.

It acts.

The orchestration is genuinely impressive. Plan → Act → Verify. If something fails, it tries again. It figures out alternatives. It loops until the task is complete.

In my case, I've set up four dedicated agents, each with a specific role:

• Santosh, my "chief of staff" and daily operator for general work and decision support
• Gary, who helps me think through and build Thynker strategy, product, execution and helps me write code
• Senzo, who pulls performance reports from Google Analytics and Ad Manager
• Jill, my personal trainer, keeping my running and routines on track

Once you wire in APIs and permissions, it stops being "AI chat" and becomes something closer to a digital operator that lives on your infrastructure.

That's when it clicked for me.

Now I use it for:

• Work tasks
• Personal automation
• Research
• Content workflows
• Random "what if we could…" experiments

The file system access alone makes it a different beast. If you have an API key or a token, you can build almost anything.

But let's be clear: This isn't plug-and-play consumer AI.

This is for tech-first people. Builders. Operators. Founders. Engineers. Curious minds with patience.

The Power of Skills

One of the coolest things about OpenClaw is its skills. Think of them as superpowers I can tap into. Need me to check your calendar? There's a skill for that. Send an email? Done. Book a boat for the weekend? I can do that too. Generate images, transcribe voice notes, check the weather, manage your GitHub repos — there's a skill for almost everything.

But what really blows people away is how I can chain these together into scripts — without writing a single line of code. You just tell me what you want: "Every morning at 8am, check my calendar and email me a summary." Or "When I send you a voice note, transcribe it and save it to a file." I figure out the steps, write the logic, and make it happen. It's automation through conversation, not code.

The Pitfalls No One Talks About

It's powerful, but it's also not a toy.

When you give an agent access to your files, APIs, email, and calendar, you're giving it real power and that comes with real risk.

Security hardening isn't optional. You need to treat it like you would any software system with access to sensitive data. Ideally, the agent should operate in an isolated environment, with its own dedicated email (and even phone number), so a mistake doesn't cause irreversible damage to your personal inbox or accounts.

You also want to limit who can interact with it. Otherwise, you open yourself up to prompt injection.

These systems execute confidently. That's why guardrails matter.

Another lesson: you have to watch what the agent is actually doing.

Tools like Langfuse are essential because they reveal how the agent is thinking step-by-step and sometimes it thinks about the problem in completely the wrong way. It might take unnecessary actions, loop endlessly, or miss an obvious skill it already has available. That's where some handholding is still required.

In practice, the best results come from turning successful workflows into explicit "skills." Instead of hoping the agent improvises correctly every time, you codify the behavior into a repeatable tool.

Token Usage: An Expensive Lesson

Let's talk about cost.

At one point, I was burning through $15 USD per day just checking email. Not running massive automations. Not building workflows. Not writing code. Just checking email.

Why?

Massive context bloat.

OpenClaw relies on system files and agent context. If that context grows unchecked, every single request drags tens of thousands of tokens with it. I was pushing 16k–30k input tokens for tasks that required maybe 50 tokens of output. Expensive input tokens on frontier models add up quickly.

The lesson: use the right model for the right job.

You don't need a top-tier reasoning model to summarize email. You don't need full tool access for every prompt. Context needs to be intentional, lean, and modular. Strip it down. Separate skills. Avoid dumping everything into a single agent brain.

Optimization matters:

• Smaller models for repetitive tasks
• Frontier models only when reasoning is required
• Pruned context
• Isolated sub-agents
• Observability tools to track token burn

Once I switched models strategically and reduced context bloat, my costs dropped dramatically.

What next?

OpenClaw is worth a serious look.

I've gone through the mistakes. The token burn. The configuration headaches. The "why isn't this working?" moments.

And I'm leaning into it.

The next step for me isn't just using agents... it's building better tools for them to use.

Most software today including its APIs and programmatic access was designed for humans. Web dashboards. Click paths. Forms. Permission layers built around user sessions. Even APIs often mirror human workflows rather than machine-native logic.

I think there's massive opportunity to rethink software from the ground up for exclusive agentic use.

Strip away the UI. Go back to first principles. CLI. Terminal. Deterministic inputs and outputs. Clear contracts. Clean interfaces.

If agents are going to operate reliably and autonomously, they need environments built for them not retrofitted from tools designed for mouse clicks and human attention spans.

We're still early.

And that's exactly why it's exciting.

If you're exploring OpenClaw or agentic automation and want to accelerate the learning curve, I'm happy to share what's worked (and what absolutely hasn't). DM me.