Building AI Agents from Scratch (Part 1): Core Architecture and Underlying Principles Explained
Dev.to / 6/12/2026
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Key Points
- The article defines an AI agent as a system that perceives its environment, reasons and plans autonomously, and uses external tools to take actions toward a specific goal.
- It contrasts autonomous agents with traditional chatbots by highlighting that chatbots typically rely on single-pass, reactive dialogue, while agents iteratively run an “agent loop” and can recover from failures.
- The piece explains a commonly used architecture formula: Agent = LLM + Memory + Planning + Tool Use, where the LLM provides reasoning, memory stores context, planning breaks down goals, and tool use performs real-world actions.
- It illustrates why agent-style execution matters for multi-step, dependent tasks (e.g., searching flights, checking loyalty points, and booking), which are difficult for standard chatbots to handle reliably.
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