Knowledge workers are adopting a more sophisticated approach to AI than traditional one-shot prompting: agentic loops that enable autonomous reasoning and iterative refinement. In a discussion on The AI Daily Brief, host NLW and Nufar Gaspar explored how organizations can deploy iterative AI agent systems to produce higher-quality, more reliable outputs. Rather than accepting a model's first response, agentic loops treat AI systems as autonomous partners that can research, review, and refine their own work across multiple iterations before delivering final results. The approach hinges on three critical design decisions: establishing verifiable finish lines that signal when agents have completed their task, strategically selecting which work types benefit from iteration (not all tasks warrant looping), and implementing cost controls to prevent expensive runaway iterations. Gaspar and NLW detailed how organizations can compose multiple specialized agents into work graphs that divide labor—one agent researches a topic, a second reviews findings, and a third refines output based on feedback—creating a pipeline that mirrors the iterative thinking process knowledge workers perform manually. This framework represents a shift toward treating AI as an autonomous reasoning partner rather than merely a first-draft generator. By designing systems with explicit stopping conditions, task-specific looping strategies, and multi-agent collaboration, organizations can automate the iterative refinement work that previously consumed significant knowledge worker time, while maintaining quality and controlling computational costs.