How to Build an AI Agent That Actually Works in Production When building AI agents, most people jump straight to tools and models. That is usually why their agents end up fragile, unpredictable, and messy. This is the mental model required to build robust AI workflows designed for real work, not just demos: 1. Purpose: Define the specific problem and the exact value the agent delivers. 2. Scope: Establish strict boundaries to prevent scope creep and unexpected hallucinations. 3. Memory: Structure short-term context and long-term data retention. 4. Orchestration: Plan how the agent coordinates tasks, calls tools, and handles edge cases. 5. Testing: Rigorously evaluate outputs to ensure reliability before deployment. Do this once, do it clean, and everything else becomes easier. Not faster. Easier. Save this post—you will need it for your next AI project. #aiagents #aibuilding #aiworkflow #automation #vibecoding - @the.ai.solopreneur