Understanding Agentic Workflows: A Practical Guide for Developers

Modern AI systems are moving away from simple prompt-response interactions toward structured agentic workflows, where models reason, plan, and act in iterative loops. By orchestrating workflows around reasoning cycles, developers can build far more capable AI systems.

What Are Agentic Workflows?

Agentic workflows define how an AI agent moves from perception → reasoning → decision → action. Instead of a single LLM call, the agent performs multiple deliberate steps.

At their core, agentic workflows revolve around:

• State Management
• Loop-based reasoning
• Tool orchestration
• Goal checking
• Memory updates

These patterns give AI systems the ability to complete complex, multi-step tasks autonomously.

The Workflow Loop

The most fundamental pattern is the agent loop:

async function workflowLoop(goal: string, tools: Tool[]) {
  let state = { goal, history: [] };

  for (let i = 0; i < 8; i++) {
    const perception = await observe(state);
    const decision = await deliberate(perception, tools);

    if (decision.type === "FINISH") return decision.output;

    const result = await execute(decision, tools);
    state.history.push({ decision, result });
  }

  throw new Error("Workflow exceeded limits");
}