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Most AI support bots aren't actually solving problems. They're assigning homework. Customer: "I need an invoice." AI: "Go to Billing → Settings → Invoices." Customer: 😑 That's not resolution. That's directions. The new shift happening in AI support isn't better answers. It's agents that can actually finish the job....

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Aaron Edwards

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396,250 Aufrufe • vor 2 Monaten •via X (Twitter)

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🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

MIKE

39,041 Aufrufe • vor 2 Monaten

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.
19:17

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

Dami-Defi

13,354 Aufrufe • vor 2 Monaten

“There is no competitor to ServiceTitan until the demo I saw from you guys today.” I got that DM this week from an owner who runs three home services brands. He sent it a few hours after seeing what we’ve been building. We started building AI agents three years ago with a text agent that captured leads and booked jobs. As we kept building, we learned that the system underneath the agent determined how much work the agent could do. Traditional systems of record were built for people clicking through workflows and entering data. An agent sitting on top of one faces major constraints on how much work it can complete. We wanted agents to run workflows from beginning to end, so we built the FSM around them. Larry was our first AI Employee. Because he operates inside our system, he can answer the call, book the job and put it directly on the dispatch board. An AI sitting on top of a traditional system might answer the same call, but often has to hand the rest back to the office staff. In a busy business with thousands of customer interactions, the difference between completing half the work and all of it is massive. Then we started thinking about how AI could impact the entire operation. That led us to build Operator, an orchestrator agent that can work across the schedule, jobs, invoices, payments, customer records and years of conversations. Business owners can give it work in plain English and let it carry that work across the system. As customers have started using Operator, they’ve realized they can ask it to do almost anything. One customer had 2,010 rows of equipment sitting in a spreadsheet. Operator went through the entire file, identified 78 rows that were air filter sizes instead of equipment, and attached the remaining 1,931 units to the correct customer properties. Now when a tech shows up, they already know what equipment is at the house. You can tell Operator: “Here’s our equipment list. Put every unit on the right customer record.” Another owner wanted a price book built from his own history. Operator read 455 past invoices and turned 306 free-text line items into a catalog organized around the work his company performs. He asked: “Read our past invoices and turn them into a starter price list.” One HVAC owner got access before our demo call. He asked Operator who owed him money that day and had payment reminders going out before we got on the phone. This is where we think AI is headed. You tell it what you need done, and it works across your business until the job is finished. If you want to see it, DM me.
1:16

“There is no competitor to ServiceTitan until the demo I saw from you guys today.” I got that DM this week from an owner who runs three home services brands. He sent it a few hours after seeing what we’ve been building. We started building AI agents three years ago with a text agent that captured leads and booked jobs. As we kept building, we learned that the system underneath the agent determined how much work the agent could do. Traditional systems of record were built for people clicking through workflows and entering data. An agent sitting on top of one faces major constraints on how much work it can complete. We wanted agents to run workflows from beginning to end, so we built the FSM around them. Larry was our first AI Employee. Because he operates inside our system, he can answer the call, book the job and put it directly on the dispatch board. An AI sitting on top of a traditional system might answer the same call, but often has to hand the rest back to the office staff. In a busy business with thousands of customer interactions, the difference between completing half the work and all of it is massive. Then we started thinking about how AI could impact the entire operation. That led us to build Operator, an orchestrator agent that can work across the schedule, jobs, invoices, payments, customer records and years of conversations. Business owners can give it work in plain English and let it carry that work across the system. As customers have started using Operator, they’ve realized they can ask it to do almost anything. One customer had 2,010 rows of equipment sitting in a spreadsheet. Operator went through the entire file, identified 78 rows that were air filter sizes instead of equipment, and attached the remaining 1,931 units to the correct customer properties. Now when a tech shows up, they already know what equipment is at the house. You can tell Operator: “Here’s our equipment list. Put every unit on the right customer record.” Another owner wanted a price book built from his own history. Operator read 455 past invoices and turned 306 free-text line items into a catalog organized around the work his company performs. He asked: “Read our past invoices and turn them into a starter price list.” One HVAC owner got access before our demo call. He asked Operator who owed him money that day and had payment reminders going out before we got on the phone. This is where we think AI is headed. You tell it what you need done, and it works across your business until the job is finished. If you want to see it, DM me.

Eric Rea

16,801 Aufrufe • vor 18 Tagen