THIS is THE massive problem holding back AI agents today. --- IndyDevDan here, super excited to say: Agentic Horizon Lesson 5 (TAC 13) is officially live on (link below). Huge thanks to every engineer that's a member and has taken Tactical Agentic Coding and Agentic Horizon - your feedback and votes were key to making this lesson happen (and the FINAL LESSON as well). For all non-members, this video is a sneak peak of the full lesson available to Agentic Horizon members. If you're interesting you can unlock this lesson and others by purchasing Tactical Agentic Coding AND Agentic Horizon. Let me be clear, TAC and AH is not for beginners. See the landing page for more details. --- 🎥 VIDEO REFERENCES • Tactical Agentic Cooking: 🤖 Traditional software gets smarter with every use - storing analytics, patterns, and data that power better algorithms. But AI agents today? They execute and forget. Every. Single. Time. The real problem isn't bad context engineering or weak prompt engineering. It's that your agents don't learn. Memory files force global context but require manual updates. Prime prompts, sub-agents, and skills are powerful but YOU have to manually steer them every time you want to add new information. 🔥 What if your agents could turn actions into expertise automatically? What if they could learn WITHOUT human intervention? That's the difference between generic agents and agent experts. Generic agents execute and forget. Agent experts execute and LEARN. 💡 In this game-changing video, we reveal how to build agents that act, learn, and reuse expertise at runtime with NO human in the loop. Real experts don't relearn their craft every time - they have a mental model that evolves with each useful action. That's exactly what we're building here. 🛠️ We dive deep into meta agentics - the atoms that make up agent experts. You'll learn how to build meta prompts (prompts that write prompts), meta agents (agents that build agents), and meta skills (skills that create skills). These are the foundational tools every agentic engineer needs in their arsenal. 🚀 Watch as we demonstrate our orchestrator agent operating a multi-agent system where WebSocket experts and database experts maintain their own expertise files - concrete mental models of their problem space. These agents validate their understanding against the actual code, update automatically, and become more proficient with zero manual intervention. ⚡ We'll show you how to scale compute by deploying three, five, or ten agent experts against a single question to co-locate on the right answer. See how tactical agentic coding transforms from one-shot prompts to self-improving systems that handle planning, building, AND learning. 🌟 This is the future of agentic coding - where agents don't just execute your commands, they accumulate expertise around the specific problems that matter most to you, your business, and your customers. This is how you build agents that improve as they're used, just like traditional software, but powered by AI. Key insights: - Build self-improving template metaprompts that learn at runtime - Create expertise files that serve as mental models for your agents - Use multi-agent orchestration to validate answers with high confidence - Implement the three-step workflow: plan, build, self-improve - Scale up compute by deploying multiple experts against critical problems - Understand why the code is ALWAYS the source of truth (not docs, not comments) 💼 The game never ends except when your agents stop learning. I don't know about you, but I want experts operating my codebase and products - not generic agents I have to manually manage every single time. Stay focused and keep building. #aiagents #agenticcoding #aicoding