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Loop Engineering: Building AI Agents That Learn and Adjust

IBM explains how to engineer AI agents that act, observe, adjust and repeat toward measurable goals using loops, tools and human oversight.

Summarized from @IBMTechnology, . 25-second read.

IBM Technology released a video explainer on loop engineering, a design approach for that goes beyond traditional engineering. The video covers how to structure agentic loops around defined goals, give agents tools and skills, use subagents to verify work, and maintain human oversight throughout the loop.

Key concepts include setting goals agents can actually verify, managing context and persistent state, integrating tools via APIs and Model Context Protocol (MCP), and avoiding "comprehension debt" by keeping humans meaningfully involved in agent workflows. The approach emphasizes iteration and feedback rather than relying on a single perfect prompt.

Read the full story at @IBMTechnology

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