AI agents operating in agentic workflows represent a fundamental shift from traditional request-response software architectures. Agentic workflows enable autonomous systems to plan sequences of actions, adapt strategies based on intermediate results, and achieve goals without explicit step-by-step human instruction.
The core challenge that agentic workflows address is the planning problem: given a large language model with reasoning capability, how do we structurally constrain its output so it reliably executes a series of tool calls, evaluates results, handles failures, and determines when a goal has been achieved? Without agentic workflows, LLMs produce single-shot outputs with no ability to self-correct, access real-time data, or decompose complex problems.
Agentic systems introduce feedback loops where agents observe outcomes, reason about next steps, and maintain internal state — enabling the construction of reliable autonomous systems. This capability becomes critical for enterprise applications such as financial advisors querying live market data, customer support agents routing tickets with context, research agents synthesizing information across multiple sources, and code generation systems that test output and refactor iteratively.
Part 32 focuses on three advanced dimensions of agentic systems: agent memory architectures that persist and retrieve context across episodes, tool composition patterns that enable safe chaining of powerful external functions, and agentic evaluation frameworks that measure whether workflows reliably achieve intended outcomes at scale.
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.