Agentic workflows represent a fundamental shift in how AI systems are architected, moving beyond simple request-response patterns to autonomous, goal-driven execution models. Traditional software pipelines operate synchronously along predetermined execution paths—input flows into processing, which produces output—but this rigid structure breaks down in complex domains such as research synthesis, customer support automation, or multi-step reasoning tasks, where the desired outcome requires adaptive decision-making, error recovery, and dynamic resource allocation.
The core problem that agentic workflows solve is the inability of rigid pipelines to handle open-ended problems where the exact sequence of steps, required tools, or intermediate decisions cannot be predetermined. Without agentic workflows, teams must either hardcode every possible execution path—an exponentially complex undertaking—or accept reduced autonomy and rely on human intervention whenever unexpected scenarios arise.
This lesson explores the architectural patterns, state management strategies, and planning mechanisms that enable agents to reason about goals, select appropriate tools, execute actions, and iterate based on outcomes. The aim is to understand how to build systems that genuinely adapt to new situations rather than merely executing predefined scripts.
Analogy🏏Cricket
🏏 Think of it like cricket: Consider India's Test match strategy in the 2023 World Cup against New Zealand. The Indian captain (the agent) doesn't simply decide the opening batting order at the start of the innings and lock it in. Instead, the captain continuously observes wickets falling, the opposition's bowling changes, pitch degradation, weather shifts, and match situations—monitoring scorecard updates every few overs. After each 6-ball delivery, the captain reassesses: should I adjust the field placements? Should I promote or demote a batter in the order? Should I shift from aggressive to defensive batting based on required run rate? The captain maintains persistent context (current match situation, opposition strengths, batter form) across multiple decision cycles, integrates real-time tools (DRS for disputed decisions, field adjustments, pace vs. spin bowling changes), observes outcomes (did that field placement prevent sixes?), and adapts the strategy. The agentic workflow is precisely this loop: perceive state (current innings score, wickets lost, overs remaining), reason (should we accelerate or consolidate?), act (call for aggressive batting or defensive blocking), receive observation (result of the last delivery), and iterate. Without this persistent, iterative loop, the captain would be making random decisions in isolation rather than coherent, contextual strategy—the team would collapse. Understanding this reveals why agentic workflows must be stateful, observable at each step, and capable of learning from outcomes rather than just executing pre-written scripts.
🏏 Showing the Cricket analogy — a Cricket version isn’t available for this concept yet.