How a structured workflow can rein in AI’s rogue agents
How a structured workflow can rein in AI’s rogue agents
Publish Date: 2026-06-11 03:30:00
Source Domain: www.imd.org
Think workflows, not wizards
How do you tap into LLMs’ power without losing control over costs, transparency, or data? The real design challenge is not whether agents can do more, but how to give them enough autonomy to be useful without giving up control. That starts with being clear about the difference between an agent and a workflow.
An AI agent is a system that takes a high-level goal (“prepare a client briefing”) and decides on its own which tools to use, in what order, and when to stop. A workflow, by contrast, is a predefined sequence of steps that humans design up front: which systems to call on, what inputs and outputs to expect, and where approvals are needed. Agents optimize for flexibility; workflows optimize for clarity, control, and repeatability.
The goal should not be to replace workflows with agents, but to let agents and LLMs operate inside well-defined flows. That starts with designing the process as if no AI existed, and only then inserting LLM calls where language understanding truly creates value.
This means distinguishing between two layers:
- The workflow layer
- A sequence of explicit, human-designed steps: retrieve and transform data, apply rules, and ask for approvals.
- Implemented using familiar building blocks: microservices, ETL jobs, workflow engines, or orchestrators like Airflow or Temporal.
- Governed by existing controls, logs, and permissions.
- The intelligence layer
- LLMs and other models are used within specific steps: to classify, summarize, extract fields, or propose options.
- Each model call has a clear purpose, constrained input, and expected output format.
- Fallbacks are defined: what happens if the model is uncertain, inconsistent, or fails?
Rather than asking an agent to “handle, contract, review”, define a structured flow — an order of actions with predefined input-output format or schema — and let the model handle only the genuinely ambiguous parts.
From ‘magic agent’ to structured workflow
So, how does it work in…
