# Enterprise AI needs fixed workflows alongside agents

Markdown copy of https://realalpha.app/s/enterprise-ai-needs-fixed-workflows-alongside-agents/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Arthur Mensch](https://realalpha.app/builders/arthur-mensch/) · Arthur Mensch is CEO and co-founder of Mistral. · Co-founded and leads Paris-based Mistral AI as CEO; named to Time's 100 Most Influential People in AI.
- Host: [Alex Kantrowitz](https://realalpha.app/people/alex-kantrowitz/) · Founder, Big Technology · Former BuzzFeed News senior tech reporter and author of Always Day One.
- Show: Big Technology Podcast
- Episode: [Who Wins if AI Models Commoditize? — With Mistral CEO Arthur Mensch](https://realalpha.app/v/xxUTdyEDpbU/), Jan 16, 2026
- Moment: 6:48 to 11:15 of the episode (4 min), playing on https://realalpha.app/s/enterprise-ai-needs-fixed-workflows-alongside-agents/; the episode from there: https://realalpha.app/v/xxUTdyEDpbU/?t=408

## Quote

> “It's never going to exist. I mean, you have a wealth of problems, just like you don't have any human that is able to **solve every task in the world**.”
> — [Arthur Mensch](https://realalpha.app/builders/arthur-mensch/) at [7:03](https://realalpha.app/v/xxUTdyEDpbU/?t=423) · Big Technology Podcast

## Takeaways

1. Humans still define important parts of how a system should behave.
2. Agents add a dynamic layer by choosing tools and actions.
3. Static workflows and dynamic agents can work together on harder problems.

## About this segment

Arthur Mensch argues that no general system can solve every task; enterprise AI pairs human-defined workflows with agents that choose actions dynamically. That balance keeps systems useful as capabilities grow.

From “Who Wins if AI Models Commoditize? — With Mistral CEO Arthur Mensch”, Jan 16, 2026. [Watch the full episode](https://realalpha.app/v/xxUTdyEDpbU/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[6:52](https://realalpha.app/v/xxUTdyEDpbU/?t=412) Issue? Well, I think the issue is, well, first of all, AGI is a very simple concept. So probably too simple for enterprises. There's no such thing as like one system that is going to be solving all of the problems of the world. And so at the end of the day- Not yet, or you just don't believe in that concept at all? It's never going to exist. I mean, you have a wealth of problems, just like you don't have any human that is able to solve every task in the world. You, of course, need to have some amount of specialization to actually solve problems. And so we are back from magical thinking to system thinking.

[7:22](https://realalpha.app/v/xxUTdyEDpbU/?t=442) We need to figure out what is the data that is going to be used to make the model better at a specific task. What is the flywheel that we need to set so that we accrue more signal from humans interacting with the system so that eventually the application becomes better and better. And so in real life, enterprises are just complex systems. And you can't solve that with a single abstraction, which is AGI.

[7:45](https://realalpha.app/v/xxUTdyEDpbU/?t=465) And so AGI to a large extent is what we were not able to achieve and which is basically the North Star of, I'm just going to make the system better over time. But because, as you've said, it's hard to explain now to investors that the technology you're building is never going to be matched by your competitors. Then there's, of course, a shift in the narrative that companies are not building like a North Star single system that is going to be solving all problems, but that will need to go into the weeds of enterprises and solving their actual problems.

[8:17](https://realalpha.app/v/xxUTdyEDpbU/?t=497) And I think at Mistral, we've been ahead of time in thinking about this. that set us our story. Our story has been to assume that eventually AI will be more decentralized, that more customization would be needed because we were running into the limits of the amount of data we could accrue and the limits of scaling those. And because of that, we created the company on that premise, on the fact that we'll bring more customization ability to enterprises.

[8:41](https://realalpha.app/v/xxUTdyEDpbU/?t=521) Yeah, and we'll get to the Mistral story in a little bit, but one more question about this. It seemed to me, and I wonder if you think this has been a shift, You were ahead of this for sure, but it seems to me like there's been a shift in the AI industry where the idea was effectively make the models smarter and they'll try to figure out these. They'll be able to figure out these problems on their own.

[9:05](https://realalpha.app/v/xxUTdyEDpbU/?t=545) Like, for instance, just make it concrete, make the model smarter, and it will be able to do, you know, a lower level associates job or maybe do data entry for multiple systems and be able to file reports. and now it seems like there's been a shift from do that to actually build out the infrastructure that the models are just one component that the infrastructure is super important and things like orchestration and you know working through the applications that are built on top of the models is going to be where the value is found it's interesting yes i think if you look at it from a system perspective you have two components and we'll always have these two components the first components are like static definitions of what the workflow should be and how a system should behave. And those static definitions are set by humans that are defining how the system should

[9:56](https://realalpha.app/v/xxUTdyEDpbU/?t=596) behave. And so this corresponds to the manual information that you're using to define the system. And then there's a dynamic component where you're connecting a model to tools and you're giving instruction to the model and the model can go and call the tools itself. And so it can decide on the graph of execution that it's going to follow. And so that part is dynamic and there's a static part where you're setting up guardrails or you're deciding you have a tree of decision sometimes. And I think it's a bit utopist and irrealistic to think that you can solve everything with a dynamic system without guidance from humans. And what has happened in the industry in the last three years is that effectively the dynamic part has grown because models can think for longer because they can call multiple tools because they can code.

[10:44](https://realalpha.app/v/xxUTdyEDpbU/?t=644) But the static part remains extremely important. And even if the dynamic part grows, then the static part allows you to create systems that are even better and more interesting. solve problems that you were not able to solve before. So the combination of these static systems, which you can call orchestration if you want, and the dynamic systems that you can call agents is going to stay super important because the two things are moving up together so that we can tackle problems that are more and more complex. Okay. And so now like with that established,