Part 8/14:
The transition from rigid workflows to dynamic agent loops signifies a major paradigm shift in AI system design. Initially, systems chained deterministic prompts—one step after another—but now, agent loops enable self-correction and iterative refinement. For example, an agent can generate a SQL query, evaluate its correctness, reiterate if needed, and only then proceed.
Furthermore, the concept of "workflows of agents" introduces parallel processing with feedback loops, significantly improving robustness and speed. By delegating subtasks to sub-agents that can communicate and refine their work, systems can better handle complex, multi-faceted problems.