Most people think building an AI agent means giving it: • a task • a tool • a prompt That’s not an agent — that’s a macro with better marketing. A real AI agent has three layers 👇 1️⃣ Input Pipeline (Signal Intake)This is where the agent listens: – user queries – system changes – external events – live data feeds If the agent isn’t consuming real signals, it can’t act intelligently. 2️⃣ Reasoning Layer (Decision Engine) This is the agent’s “brain”. It evaluates options, simulates outcomes, and chooses the next step. Without structured reasoning → it becomes unpredictable. 3️⃣ Feedback Loop (Self-Improvement)This is where 95% of agents fail. If the agent can’t learn from: – user corrections – outcomes – failed attempts – time-based patterns …it cannot evolve. The future belongs to agents that improve with usage, not ones that repeat the same workflow forever. Automation gives speed. Agents give adaptation. And adaptation is where exponential value hides.

I had paused LinkedIn earlier to focus on building and thinking deeply about a few things around AI, data, and how founders actually use them to grow. Over the last few months, I’ve been: • studying how AI agencies are evolving • understanding where automation actually adds leverage (and where it doesn’t) • thinking about AI less as a tool and more as a thinking partner I’m coming back here now to share insights, frameworks, and practical ideas — especially around : Data intelligence Ai agencies Systems that scale Decision-making with AI Nothing overly polished. Just real observations and useful thinking. If you’re building, experimenting, or just curious about how AI is really being applied today —happy to connect, exchange notes, or chat. Looking forward to reconnecting 👋

There’s a subtle change happening in how the best AI agencies operate. They’re spending less time building automations and more time designing decision systems. Instead of: • “What can we automate next? ”They’re asking: • “What decisions should never be manual again?” That shift changes everything. When AI handles decisions like: – lead qualification – pricing recommendations – prioritization of work – risk detection Your agency stops scaling on effort and starts scaling on judgment. This is where AI actually becomes leverage —not as a tool layer, but as a thinking layer. Worth paying attention to this transition.

Most agencies stall at the same place. Not because they can’t get clients. But because every new client adds more work. That’s not scale — that’s disguised freelancing. Real scale happens when one action creates value multiple times. Leverage in an AI agency looks like this: • One discovery call → becomes a reusable ICP insight • One successful workflow → becomes a template • One client result → becomes sales proof + training data If your agency resets to zero after every project, you don’t own a business. You own a job with better tools. The agencies that break past this phase design systems where: results improve automatically, delivery gets faster, and knowledge compounds. AI doesn’t replace hard work. It turns structure into a force multiplier. Build leverage first. Clients will follow.

Huge shoutout to the Top 5 Members of Brendan's AI Community for November! These are the people leading the way, sharing knowledge, and helping spread the AI culture: 🥇 @Tom Galeski 🥈 @Safock Ai 🥉 @Jody Murfit 🏅 @Diptamoy Barman 🏅 @Pavan Sai Massive congrats to all of you for your consistency and energy this month. You help set the tone for the whole community. I also want to congratulate every single member of Brendan's AI Community! Because of your support, wins, questions, posts, and engagement, we're building something special here. Your energy fuels the entire group, and that's what makes this place different. Keep crushing it, keep showing up, and keep sharing your wins. The momentum you're building is exactly what turns learning into real results 🔥 Who's going to be on next month's list? 👀