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Build Log. I'm Nick.
Here's what I shipped this week and what it taught me.
What if I told you the most tedious part of my job—digging through dozens of articles for a single data point—is now completely automated? Running on a system that costs less than a coffee per week to operate?
Everyone's talking about AI agents. But most are just glorified chatbots wearing a fancy hat. Three months ago, I built a researcher that actually does the work. It’s been shipping real research for my content teams, every single day. No hand-holding.
The problem is obvious. Information overload is real. Manual research is slow. It’s inconsistent. You find a great source one week, forget it the next. It doesn’t scale.
But here’s the shift. The current crop of LLMs—Claude 3, GPT-4—they finally have the reasoning. And the context length. They can synthesize information from multiple sources accurately. Not perfectly. But reliably enough for production.
This isn't about asking better questions to a chatbot.
[BED: SWELL]
This is about building a digital employee. One that executes a full research process from a single prompt. Start to finish.
The Assembly Line
You’ve probably heard that AI agents are the future.
Here’s what actually happens when you run one.
They’re not a magic box. They’re a factory assembly line for information.
Let me walk you through my production system. It has four core parts. Plain English.
Part one: The Trigger. This is how it starts. In my case, it’s a simple webhook from an Airtable base. My editor adds a new topic to a row. That webhook fires. The pipeline catches it.
Part two: The Fetcher. It gets the data. The agent receives the topic. It generates specific search queries. Then it pulls text from a curated list of sources. I don’t let it roam the whole web. We’ll get to why.
Part three: The Analyst. This is the brains. The heavy reasoning. And this is where most people waste money.
And this is where it gets interesting from an operations standpoint.
I route tasks. Claude Opus handles the synthesis and critical analysis. Its accuracy is worth the higher cost for that step. But for simple jobs? Like classifying an article’s main theme? I use Haiku. It costs pennies. This routing cuts my average task cost by about sixty percent.
Part four: The Formatter. How it delivers. It doesn’t just dump text. It structures the findings. Bullet points. Key quotes with citations. It posts it directly to a Google Doc. Or into a Slack channel for the team.
This system isn’t running in a demo. It’s been tested in production. It handles three to five research requests daily. I haven’t touched it in six weeks. It just ships.
Your First Agent – Start Painfully Small
So you want to build one. Here is the single biggest mistake I see. Starting too broad.
Do not build an agent to “research market trends.” You will fail. The scope is infinite. The output will be uselessly vague.
Your first agent should be so specific you can describe its job in a single sentence.
[BED: DUCK]
Let me give you a real spec from my second week of testing. It was awful. I asked it to “research remote work.” I got a mess of generic platitudes from Forbes and Wikipedia.
I scrapped it. Started over with this: “Find three recent product launches in the developer tools space from the last two months. Summarize the key feature of each. Identify the primary target customer. List the pricing model if available.”
Night and day. The agent had guardrails. It knew what “recent” meant. It knew what a “product launch” looked like. It had a clear output format.
The basic workflow is this. One: receive that specific task. Two: generate three to five targeted search queries. Three: scrape or fetch the top three results for each. Four: summarize and synthesize those findings. Five: output to a structured format.
Let’s talk cost. Because if it’s not cost-effective, it’s a toy.
A task like that one? Using my model routing strategy? It runs me between seven and fifteen cents per execution. Seven cents. You can run a dozen a day for a dollar. That’s what I mean by cheaper than coffee.
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Now, building this involves trial and error. You have to choose tools. Set up the routing logic. Handle errors.
If you want to skip that grind, I’ve put together a free architecture diagram. It’s the exact blueprint for my system. Plus a list of the tools I use and why. It’s the cheat sheet for getting your first agent deployed. Get it at buildlogpodcast.com/research. That’s buildlogpodcast.com/research.
The Contrarian Take – Constrain Its World
Which brings me to my contrarian take. The thing most people get dangerously wrong.
Everyone says to just “give the AI more context.” Throw more data at it. The real bottleneck isn’t quantity. It’s quality.
You’ve probably heard that more data is the answer.
Here’s what actually happens when you run it.
Letting your agent roam the entire open web is a recipe for garbage. It will get lost in SEO spam farms. It will cite anonymous forum posts. It will drown in low-quality news aggregators.
I learned this the expensive way. My first version used a general web search. I asked it for best practices for WordPress maintenance. It came back with a beautiful summary. Citing four sources. Looked professional.
One citation was from a legitimate developer blog. The other three were from content farms. Pages stuffed with affiliate links. The advice wasn’t just bad. It was actively harmful to site performance. I almost shipped it.
The fix? Constrain your agent’s world.
[BED: SWELL]
Don’t teach it to be a librarian of the entire internet. Teach it to be a specialist in your personal library.
For my tech research agent, I gave it a curated list. Fifteen high-authority sources in the software and SaaS niche. No more. No less. For my history content sites, I pointed it first at a private database of verified academic journal summaries.
The result? Output reliability shot up. The cost per task went down—fewer tokens wasted on junk. And the noise disappeared. It’s the single most important optimization I made.
Your Move
So here’s your action for today. Right now. It’s not to code anything.
Open a document. Define one single, repetitive research task you did this week. I’m serious. Think of the most tedious lookup you do.
Write it out in extreme detail. Like you’re instructing a meticulous, slightly literal-minded intern.
“Go to these three industry blogs. Find articles from the last month. Compare their takes on the new Google algorithm update. List each key argument. Format the output as a simple comparison table.”
That document? That’s your spec. That’s your blueprint. That is the entire first step.
Then, head to buildlogpodcast.com/research. Grab the architecture diagram and the tool list. See exactly how to turn that spec into a working system. A system that runs while you sleep.
The smart home automation side of this connects directly to what I cover on SmartHome Wizardry. If your home isn't automated yet, start there.
For a deeper dive on automating real business processes with AI, check out our sister show, ‘Production Ready AI.’ We tear down systems that are actually running in the wild.
That's the build log for this week.
Ship something. Measure it. Tell me what happened.
