Alright, folks, Jake Morrison here, back at it from clawgo.net. Today, I want to talk about something that’s been buzzing in my brain for a while, something that’s gone from a curious side project to an absolute necessity in my workflow: getting AI agents to actually do things for me, not just generate text. Specifically, I’m digging into how I’ve started using OpenClaw to automate my content research and drafting – a task that used to eat up hours of my week.
You know the drill. You’ve got an article idea, maybe it’s about the latest AI model or a new approach to prompt engineering. First, you hit Google, then maybe Reddit, then a few academic papers. You’re bouncing between tabs, trying to synthesize information, pulling out key facts, quotes, and statistics. Then you open up a doc and start trying to string it all together. It’s a grind, and frankly, it’s not why I got into this gig.
For the longest time, my AI use was pretty basic: “Hey ChatGPT, write me an intro about X,” or “Summarize this article for me.” Useful, sure, but it felt like I was still doing all the heavy lifting. I wanted an AI that could actually go out, find the information, process it, and give me something closer to a first draft, something I could then refine and inject my own voice into. That’s where OpenClaw came in, and specifically, the idea of building a research agent.
My Frustration with “Smart” Search and the Agent Leap
Let’s be honest, smart search engines are getting better, but they’re still just search engines. They give you links, and you still have to click through, read, and interpret. I remember a particularly painful week trying to research the nuances of federated learning for an article. I spent an entire Tuesday just reading papers and trying to piece together a coherent narrative. My eyes were blurry, my coffee was cold, and my brain felt like scrambled eggs.
That’s when I had a lightbulb moment. What if I could give an AI agent a specific goal – “research the pros and cons of federated learning for enterprise deployment, including specific industry examples” – and let it go fetch? Not just spit out a Wikipedia summary, but actually browse, read, extract, and synthesize. That’s the leap from a static LLM prompt to an actual agent, one that can make decisions, execute actions, and iterate.
I’d been tinkering with OpenClaw for a bit, mostly just playing with its basic browsing capabilities. But the real power, I realized, was in chaining those actions together, giving it a memory, and letting it figure out the best path. It’s not magic, it’s just a more advanced form of scripting, but with an LLM as the orchestrator.
Building My First Research Assistant: “Claw-dette”
I decided to name my first serious agent “Claw-dette,” because, well, it sounds a bit fancy and a little like “Claude,” which is one of the models I often use with OpenClaw. My goal for Claw-dette was simple: given a topic, she should provide me with a structured outline and key bullet points for an article, including sources.
Here’s how I started to build her, conceptually. OpenClaw provides a nice environment for this, letting you define tools and then instruct the agent on how to use them.
Step 1: Defining the Core Tools
For a research agent, you primarily need two things:
- A way to search the internet: OpenClaw has built-in browsing capabilities, but I sometimes hook it up to a specific search API for better results or to avoid rate limits if I’m doing a lot of deep dives. For this example, we’ll assume OpenClaw’s default browser tool is sufficient.
- A way /extract information: This is where the LLM itself comes in. It reads the page content and pulls out what’s relevant.
- A way to store information temporarily: A scratchpad or memory to keep track of what it’s found so far.
In OpenClaw, you don’t explicitly “build” these tools like writing Python functions from scratch. Instead, you define their purpose and how the agent should think about using them. It’s more about prompt engineering the agent’s meta-cognition.
My initial agent prompt looked something like this (simplified for clarity):
You are Claw-dette, an expert research assistant for tech bloggers.
Your goal is to thoroughly research a given topic and provide a structured outline
with key facts, arguments, and supporting data points suitable for an article.
You MUST provide sources for all factual claims.
Available Tools:
1. `browse_web(query)`: Searches the internet for information.
- Example: `browse_web("latest AI agent frameworks")`
2. `read_page(url)`: Reads the content of a given URL.
- Example: `read_page("https://www.example.com/article")`
3. `summarize_text(text, goal)`: Summarizes provided text based on a specific goal.
- Example: `summarize_text(page_content, "extract key arguments for federated learning")`
4. `add_to_notes(note)`: Adds a piece of information to your internal research notes.
- Example: `add_to_notes("- Federated learning: privacy benefits (Source: Example Corp. Blog)")`
Workflow:
1. Understand the user's research request.
2. Break the request down into smaller search queries.
3. Use `browse_web` to find relevant articles, reports, and academic papers.
4. Use `read_page` to analyze promising links.
5. Use `summarize_text` to extract key information, arguments, and data points.
6. Use `add_to_notes` to compile all relevant findings, ALWAYS including the source URL.
7. Once sufficient information is gathered, synthesize it into a structured outline.
8. Present the outline and notes to the user.
Step 2: Giving Claw-dette a Task
My first real task for Claw-dette was: “Research the current state of AI in drug discovery, including major breakthroughs in the last 2 years and ethical considerations.”
I hit go, and then I watched. It was fascinating. Claw-dette didn’t just give me 10 links. She started with a broad search, identified a few promising articles, then drilled down. She’d read a page, identify a new keyword (e.g., “AlphaFold,” “insilico medicine”), and then search for that. She was essentially doing what I used to do, but at lightning speed and without the coffee breaks.
One specific example: she found an article mentioning a new AI model for protein folding. Instead of just noting that, she then executed another `browse_web` query for “AlphaFold impact drug discovery” and `read_page` on a few more specific articles, pulling out details about its accuracy and implications. She then added these to her `notes`, linking back to the original source.
This iterative process, where the agent uses information it just found to inform its next action, is the core of what makes these agents so powerful compared to a single-shot prompt.
Step 3: The Output and My Reaction
After about 15 minutes (which would have been at least an hour for me), Claw-dette presented me with:
- A detailed outline:
- I. Introduction to AI in Drug Discovery
- II. Major Breakthroughs (2024-2026)
- A. Protein Folding (AlphaFold and successors)
- B. Compound Synthesis and Optimization
- C. Clinical Trial Optimization
- III. Ethical Considerations
- A. Data Privacy
- B. Bias in Datasets
- C. Access and Equity
- IV. Future Outlook
- Under each point, she had bulleted facts, statistics, and direct quotes, each with a source URL.
I’m not going to lie, I was blown away. It wasn’t perfect, of course. Some of the phrasing was a bit dry, and a few sources were paywalled or less authoritative than I’d like. But it was a fully functional, well-researched first draft of an outline. I had a foundation to build on, not a blank page.
Beyond Research: My Content Draft Agent
Encouraged by Claw-dette’s success, I decided to take it a step further. What if an agent could not only research but also generate a first pass at the actual article content? This is where I started building “Article-Bot” (I’m still working on the name, okay?).
Article-Bot takes Claw-dette’s outline and notes as input and then uses a similar iterative process to write sections. The trick here is to provide very clear instructions on tone, style, and length for each section.
Key Instructions for Article-Bot:
- Tone: Conversational, informative, slightly opinionated (like me!).
- Audience: Tech-savvy readers, interested in AI agents and automation.
- Length: Aim for 200-300 words per major section.
- Cite Sources: Integrate sources naturally into the text (e.g., “According to a recent report from X, Y happened…”).
- Focus: Maintain focus on the main topic, avoid tangents.
Here’s a simplified piece of the prompt I use for Article-Bot, specifically for generating a section based on an outline point:
You are Article-Bot, an AI content drafter.
Your task is to write a section of a blog post based on the provided outline point
and supporting research notes.
Maintain a conversational, informative, and slightly opinionated tone.
Target audience: tech bloggers and AI enthusiasts.
Outline Point: {{outline_point}}
Research Notes: {{relevant_notes_from_Clawdette}}
Instructions:
1. Draft a paragraph or two covering the main idea of the outline point.
2. Integrate at least one specific piece of data or finding from the research notes.
3. Ensure the tone is engaging and appropriate for a blog.
4. DO NOT just list facts; weave them into a narrative.
What I found was that Article-Bot, given a good outline and solid research from Claw-dette, could produce surprisingly readable content. It wasn’t perfect, and I still had to go in and add my personal anecdotes, my unique takes, and generally polish it up. But it was a *draft*, a starting point that cut my writing time by at least 30-40% on some articles.
I remember one article about the future of multimodal AI. I gave Article-Bot the outline and notes, and it generated a section discussing the challenges of data fusion in multimodal models. It even managed to reference a specific paper I had flagged in the notes. I still had to rewrite a few sentences for flow and add a personal observation about the difficulty of training these models, but the core information and structure were there.
Practical Takeaways for Your Own Agent Journey
So, you’re probably thinking, “Okay, Jake, this sounds cool, but how do I actually get started?” Here’s my advice, based on my own trial and error:
- Start Small, Think Big: Don’t try to build an “everything” agent right away. Pick one specific, repetitive task that eats up your time. For me, it was initial research. For you, it might be summarizing meeting notes, drafting email responses, or even just organizing your project files.
- Define Your Tools Clearly: In OpenClaw (or any agent framework), the LLM acts as the brain, but the tools are its hands and feet. Be explicit about what each tool does and how the agent should use it. The more specific, the better.
- Iterate on Your Prompts: Your first agent prompt won’t be perfect. Mine certainly wasn’t. Pay attention to where the agent gets stuck, or where its output isn’t what you expect. Tweak the instructions, add more constraints, or provide better examples. It’s an ongoing conversation.
- Embrace the “Thought Process”: OpenClaw often lets you see the agent’s internal monologue – its “thoughts.” This is invaluable for debugging. If an agent is making bad decisions, look at its thoughts to understand why. Is it misinterpreting the goal? Is it struggling to choose the right tool?
- Don’t Expect Perfection: AI agents are assistants, not replacements. Their job is to get you to 70-80% of the way there, saving you the grunt work. The final 20-30% – the human touch, the creativity, the nuanced judgment – that’s still on you. And honestly, that’s where the fun is for me.
- Keep an Eye on Costs: Running agents, especially those that browse the web or use powerful LLMs frequently, can add up. Monitor your API usage and optimize your agent’s workflow to be efficient. Sometimes a broader search followed by targeted reading is better than a hundred micro-searches.
Using OpenClaw to build these agents has fundamentally changed how I approach content creation. It’s not about making me obsolete; it’s about making me more efficient, more focused on the creative aspects, and frankly, less bored. If you’re a content creator, a researcher, or anyone drowning in repetitive digital tasks, I highly recommend diving into the world of AI agents. It’s not the future; it’s right now, and it’s making a real difference in my workday.
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