What Are Agentic Workflows? Patterns, Examples, and How to Make Them Reliable

Agentic workflows represent a shift in automation, where AI agents autonomously decide steps at runtime, utilizing reasoning, tools, and memory to achieve goals, rather than following predetermined scripts. These workflows are characterized by adaptability, allowing agents to handle complex, unscripted tasks by planning, acting with tools, observing outcomes, and iterating until objectives are met. The guide explores the components and patterns of agentic workflows, such as planning, tool use, reflection, and multi-agent orchestration, and highlights their benefits, including handling unscriptable tasks, adapting to input changes, and compressing multi-step work. However, the autonomy of these workflows introduces risks, such as hallucination, context blindness, and silent failures, necessitating rigorous testing and reliability practices. Techniques for ensuring reliability include mapping failure modes to tests, employing statistical measures for consistency, integrating testing into CI/CD pipelines, red-teaming beyond security, and transforming production failures into regression tests. Ultimately, agentic workflows are best suited for tasks requiring runtime decision-making and adaptability, with a focus on engineering discipline to manage the inherent complexities and potential failures.

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