Here are 7 underrated steps for building a scalable AI Agent It's not about the model but the structure, let me explain... The core concept that separates a monolithic system from an agentic system is its ability to reason and properly use tools. But building these systems can be very confusing without a proper roadmap. So, we prepared a brief roadmap that can help you move faster: 1. Pick your model Choose an LLM that: - Handles reasoning well - Supports step-by-step logic (Chain of Thought) - Gives stable, consistent answers → Tip: Start with Llama, Claude Opus, or Mistral (open weights = more control) 2. Build Agent's Logic Teach your agent how to think: - Should it reflect before responding? - Should it plan steps or act directly? - Should it use tools when it gets stuck? → Tip: Start simple with ReAct or Plan-then-Execute. 3. Write its Operating Instructions Define the operating rules: - How should it respond? - When should it use external tools? - What format should it return (JSON, markdown, etc.)? → Tip: This is your agent’s internal protocol. 4. Add memory LLMs forget. You fix that by: - Using sliding windows for short-term memory - Summarizing older chats - Storing key facts (user preferences, decisions) → Tip: Use MemGPT or ZepAI to make this easy. 5. Connect tools & APIs Let the agent take action: - Fetch data - Update CRMs - Run calculations → Tip: Utilize MCP to make your workflows even easier with its plug-and-play feature. 6. Give it a job Be specific. ✅ “Summarize user feedback and suggest improvements” ❌ “Be helpful” → Tip: Limit the scope of a Job, rather than what to do - focus your agent on what not to do 7. Scale to multi-agent teams Let agents specialize and collaborate: - One gathers data - One interprets it - One formats results → Tip: Limit the scope of a Job, rather than what to do - focus your agent on what not to do (Note: Details vary according to different use cases, hope it will give you a good start) If you are a business leader, we've developed frameworks that cut through the hype, including our five-level Agentic AI Progression Framework to evaluate any agent's capabilities in my latest book. 🔗 Book info: https://amzn.to/4irx6nI Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agent | 69 comments on LinkedIn