This article introduces AI Agents by contrasting them with chatbots and workflows, presenting the core formula Agent = LLM + Memory + Tools + Loop, explaining each component with a flight-booking example, and outlining the engineer's role in designing reliable agent systems.
This article deconstructs AI agent architecture as a stateful runtime chain—Goal→Plan→Context→Decide→Act→Observe→Verify→State→Next—distinguishing Planner, Tool, MCP, Memory, and Reflection as interconnected layers rather than parallel modules, emphasizing that reliability comes from the harness runtime handling verification, recovery, and context engineering outside the model.
In a deep‑dive roundtable, three data‑engineering veterans discuss the rise of AI agents, the importance of data context, memory mechanisms, workflow versus agent trade‑offs, and the future of database intelligence, offering practical strategies and architectural philosophies for building smarter data pipelines.
