BEYOND CHATBOTS || Why Multi-Agent AI Workflows Are the Real Business Opportunity
Most businesses are using AI the way they once used Google: typing in a question and waiting for an answer.
That is not a criticism. It is simply where the technology meets most people in their day-to-day: a prompt, a response, a copy-paste into a document. Useful, certainly. But it barely scratches the surface of what AI can actually do when it is set up to work for you rather than with you.
At Web Summit Vancouver 2026, I attended a session called "Beyond Chatbots: Multi-Agent Workflows at Work," led by the team at MAKE, an AI and automation platform. They walked us through a live example of how their own product marketing team rebuilt their launch process using a system of AI agents, as well as the four design principles they identified along the way.
Before getting into the principles, it is worth establishing what an AI agent actually is, because the term gets used loosely. When you type a query into ChatGPT or Claude, you are using AI as a tool: you provide input, it generates output, and the loop ends there. An agent is different. An agent is given a goal, a set of tools, and the ability to reason through how to accomplish that goal, step by step, without you directing each move. It acts. It makes decisions. It does not wait to be asked.
A multi-agent system takes that further. Rather than a single agent handling everything, the work is distributed among specialized agents that collaborate, check in with humans at the right moments, and maintain a shared record of progress.
Principle 1: The Decomposition Decision
The first question to ask is how many agents you actually need. Agents perform best when their task is narrow and well-defined. But splitting a workflow into too many agents creates its own complexity. The MAKE team found a useful pattern: rather than building a separate agent for each of the 15 content types, they used a single first-draft agent with a dynamic system prompt, assigning it different roles depending on the task at hand. Fewer agents, more flexibility. In other words, you need to determine how narrow a task you want your agent to handle. Give it too wide a spectrum, and it won’t function well juggling all the different tasks; give it too narrow a scope, and you have too many agents.
Principle 2: The Determinism Dial
Not every step in a workflow needs an AI decision. Some steps should always happen the same way, every time. Letting an agent decide something that should be hardcoded wastes resources and introduces unpredictability. The discipline is in identifying which parts of a process require genuine reasoning and which parts should simply execute. This distinction is what separates a well-designed workflow from an expensive one. For example, if you always need to provide your first-draft agent with product images and a press release before they can start drafting social captions and other marketing materials, do not make the agent decide whether they need to ask you for them; just give them the materials!
Principle 3: State as the Source of Truth
AI agents do not have reliable long-term memory. For a multi-agent system to function at scale, there must be a persistent, external record of what has been done, what is in progress, and what comes next. In the MAKE example, that record lived in their project management tool. This could be your team’s Asana, Monday, or any other project management tool. Any agent, or any human, could pick up where the work left off. Transparency and continuity are built into the architecture.
Principle 4: Human-in-the-Loop as Architecture
This is the principle most often treated as an afterthought, and it is the most important. The goal is not to remove humans from the process; it is to engage them at the right moments. Agents handle the repetitive, low-judgment work. Humans review, redirect, and approve where their input genuinely matters. When this is designed intentionally rather than bolted on, the result is a workflow that is both faster and more trustworthy.
The tools to build these systems are more accessible than most people realize. Platforms like MAKE allow teams to design and manage multi-agent workflows without a development background. The barrier is not technical. It is conceptual. Understanding what agents are, where human judgment still belongs, and how to structure a workflow that holds together in production; that is the real work.
The competitive advantage will not go to the businesses that adopted AI first. It will go to the ones who learned to deploy it well.
