An agentic workflow is a system design where a language model runs in a loop: plan, act via tools, observe results, revise, repeat — until the task is done or the system escalates to a human. It's the difference between a model that answers questions and one that gets things done.

Loops compound both capability and risk. The same property that lets a model work through a multi-step process also means errors accumulate, costs multiply with each iteration, and failure modes multiply too. A single-shot prompt that hallucinates produces one bad output. An agent that hallucinates can take a chain of irreversible actions before anyone notices.

This is why evals and guardrails stop being nice-to-haves the moment you go agentic. You need to define success criteria before you build, not after you've discovered something went wrong in production.

In practice, agentic workflows show up wherever a task can't be completed in one shot: multi-step document processing, anything requiring conditional branching based on intermediate results, and processes that need tool calls plus retry logic. Technically, they're usually built on function calling or MCP, wrapped in an explicit loop with logging and escalation paths.

The hype around agents often skips past the engineering discipline they require. An agent with broad tool access and weak guardrails isn't powerful — it's a liability. Scope the tools tightly, log every action, and build in human checkpoints for anything consequential.

Further reading:

• The Shift from Models to Compound AI Systems (Berkeley AI Research) — Makes the case that the future of AI is systems, not models, with agentic workflows as a core pattern.
• Agentic Design Patterns Part 1 (Andrew Ng / The Batch) — Introduces reflection, tool use, planning, and multi-agent collaboration as composable agentic patterns.