Everyone talks about AI agents. Few people actually build one that works. After designing multiple agent systems, here’s the simplest architecture that actually holds in production. Step 1: Define a single-purpose agent Your first agent should NOT be general-purpose. Bad: “AI assistant” Good: “Summarize customer feedback into actionable insights” The narrower the scope, the higher the reliability. Step 2: Implement the agent loop Every functional agent relies on a decision loop: while not done: observe_state() plan_next_action() execute() evaluate() This loop is the core of autonomy. Step 3: Tool-first design Agents become useful when they can act. Typical tools: APIs Databases Internal functions Best practices: Validate inputs Restrict permissions Add retry logic Log everything Step 4: Memory (don’t overdo it) You need two layers: Short-term memory → current task Long-term memory → optional (vector DB) Most early systems only need short-term context.…