What is an Agentic Workflow?

1 min read Updated

An agentic workflow is a multi-step process where AI agents autonomously plan, execute, and adapt their actions to complete a complex task — making decisions at each step rather than following a fixed script.

WHY IT MATTERS

Traditional automation follows predefined rules: if X then Y. Agentic workflows are adaptive — the agent decides what to do at each step based on context, intermediate results, and its reasoning capabilities.

Consider processing an invoice. Traditional automation extracts fields from a fixed template. An agentic workflow reads any invoice format, reasons about the content, validates against purchase orders, identifies discrepancies, and routes for approval — handling edge cases a rule-based system would choke on.

The power of agentic workflows comes with a tradeoff: unpredictability. Since the agent reasons about each step, the exact execution path may vary between runs. For financial workflows, this means you need guardrails that constrain outcomes regardless of the path taken.

HOW POLICYLAYER USES THIS

PolicyLayer constrains the financial actions within agentic workflows. Regardless of how an agent reasons about a payment workflow, PolicyLayer ensures every transaction step complies with spending limits, approved recipients, and budget constraints.

FREQUENTLY ASKED QUESTIONS

How are agentic workflows different from RPA?
RPA (Robotic Process Automation) follows fixed scripts on UI elements. Agentic workflows use AI reasoning to handle unstructured inputs, make decisions, and adapt to novel situations. RPA is brittle but predictable; agentic workflows are flexible but need guardrails.
What are common agentic workflow patterns?
Common patterns include prompt chaining (sequential steps), routing (branching based on classification), parallelization (fan-out/fan-in), orchestrator-workers, and evaluator-optimizer loops.
Are agentic workflows reliable enough for production?
Yes, with proper design. Use deterministic steps where possible, add validation checkpoints, implement retries and fallbacks, and always include human-in-the-loop for high-stakes decisions.

FURTHER READING

Enforce policies on every tool call

Intercept is the open-source MCP proxy that enforces YAML policies on AI agent tool calls. No code changes needed.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
// GET IN TOUCH

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