ABOUT THIS EPISODE
Agentic AI engineering lets teams orchestrate GPU resources, models and agents. Daemon Behr of Nutanix explains how he uses agent swarms to stay ahead.
Get tech leader insights to move faster and smarter.
Get more stories by subscribing to The Forecast.
Related:
Video transcript:
Daemon Behr: The fact that there's so many different large language models that are out there have different capabilities, whether it's multimodal or reasoning or so on, and the frontier models change on a weekly basis in terms of capability. But that impacts organizations as well because maybe they don't necessarily want to have those changes occur on a regular basis. They want to be able to lock certain models down to get deterministic outputs from their environment. So what we're able to do is we have our AI gateway that allows you to connect to those frontier models when needed, but you also have the capability to have open models that are running on-prem that you can lock to specific versions. So you have the capability of managing your tokens, managing your spend, have control over the environment, the performance, the availability, and also governance over your MCP servers as well. So having that greater control over where you're running your inference is a game changer and it's something that we're able to do really well.
Ken Kaplan: Teams have to build those capabilities. What do you call that activity or that area?
Daemon Behr: That's agentic engineering.
Ken Kaplan: So just describe that world a little bit. What is an agentic engineer doing?
Daemon Behr: So with agentic engineering, you're able to say these GPU resources are assigned over to this model. I want to scale with X number of users and then you're able to do that. You can increase the number of instances, you can load balance your endpoints, you can use some endpoints that are running in the cloud or on-prem, on bare metal. There's many different options on how you actually do the inferencing and the orchestration of all those different components. The vector databases, the data sources, the various agents that you create, the frameworks for that, the infrastructure that it's actually running on as well. That's all part of agentic engineering.
Ken Kaplan: How do you stay plugged in and moving ahead with so much rapid development around AI and how people are using it and what's next?
Daemon Behr: So I actually have a bunch of agents myself that I use. I use Nemo Cloth, it's a fork of open cloth that's a little bit more secure, in order to do research for me and curate my information on a daily basis. And then it actually sends me messages over Telegram and creates reports which I then study that as well as various YouTube videos from probably about 50 different people on a regular basis. And from that, I'm able to understand the changes that are happening in the industry. I also have a swarm of agents that I use to deploy new and innovative software that I come across on GitHub. So if something is rising up at the GitHub stars, I'll tell one of my agent teams to go download that, to deploy that in sandbox, tell me how it works, tell me the deficiencies, and then tell me if it's valuable to me. If it is, then I will actually adopt it. If not, then it'll tell me that I can just kill it.
Ken Kaplan: How did you get to that mindset and to actually do that?
Daemon Behr: I was doing that on my own and then I found out that I didn't have enough time to do that. So that's where I thought, well I could actually lean on the technology that I'm studying and learning to do that for me.
So the combination of traditional AI and generative AI is what we're calling modern AI. So it's not a bubble that's popped per se, it's an evolution to expand to generative AI. Now in terms of things that you could call bubbles and that are popping, it's not the same way that we'd have the like the dot-com bubble or busts like that where you have I guess a souring of expectations or the the trowel of disillusionment per se. That's happening on a much more shorter time span. So what may be at its peak point in terms of expectations, that could pop the next day and then you have a whole new thing which is propped up. So the rotation of innovation is a lot more frequent. So the bubbles are popping on a very regular basis but the bubbles are smaller.
Get tech leader insights to move faster and smarter.
Get more stories by subscribing to The Forecast.
Related:
- Can Containers Contain AI Agents?
- Dual Native Plugs Containers into VMs or Bare Metal
- AI Throws the Whole IT Stack Up in the Air
- Agent Gateway Enforces AI Token and Traffic Control
Video transcript:
Daemon Behr: The fact that there's so many different large language models that are out there have different capabilities, whether it's multimodal or reasoning or so on, and the frontier models change on a weekly basis in terms of capability. But that impacts organizations as well because maybe they don't necessarily want to have those changes occur on a regular basis. They want to be able to lock certain models down to get deterministic outputs from their environment. So what we're able to do is we have our AI gateway that allows you to connect to those frontier models when needed, but you also have the capability to have open models that are running on-prem that you can lock to specific versions. So you have the capability of managing your tokens, managing your spend, have control over the environment, the performance, the availability, and also governance over your MCP servers as well. So having that greater control over where you're running your inference is a game changer and it's something that we're able to do really well.
Ken Kaplan: Teams have to build those capabilities. What do you call that activity or that area?
Daemon Behr: That's agentic engineering.
Ken Kaplan: So just describe that world a little bit. What is an agentic engineer doing?
Daemon Behr: So with agentic engineering, you're able to say these GPU resources are assigned over to this model. I want to scale with X number of users and then you're able to do that. You can increase the number of instances, you can load balance your endpoints, you can use some endpoints that are running in the cloud or on-prem, on bare metal. There's many different options on how you actually do the inferencing and the orchestration of all those different components. The vector databases, the data sources, the various agents that you create, the frameworks for that, the infrastructure that it's actually running on as well. That's all part of agentic engineering.
Ken Kaplan: How do you stay plugged in and moving ahead with so much rapid development around AI and how people are using it and what's next?
Daemon Behr: So I actually have a bunch of agents myself that I use. I use Nemo Cloth, it's a fork of open cloth that's a little bit more secure, in order to do research for me and curate my information on a daily basis. And then it actually sends me messages over Telegram and creates reports which I then study that as well as various YouTube videos from probably about 50 different people on a regular basis. And from that, I'm able to understand the changes that are happening in the industry. I also have a swarm of agents that I use to deploy new and innovative software that I come across on GitHub. So if something is rising up at the GitHub stars, I'll tell one of my agent teams to go download that, to deploy that in sandbox, tell me how it works, tell me the deficiencies, and then tell me if it's valuable to me. If it is, then I will actually adopt it. If not, then it'll tell me that I can just kill it.
Ken Kaplan: How did you get to that mindset and to actually do that?
Daemon Behr: I was doing that on my own and then I found out that I didn't have enough time to do that. So that's where I thought, well I could actually lean on the technology that I'm studying and learning to do that for me.
So the combination of traditional AI and generative AI is what we're calling modern AI. So it's not a bubble that's popped per se, it's an evolution to expand to generative AI. Now in terms of things that you could call bubbles and that are popping, it's not the same way that we'd have the like the dot-com bubble or busts like that where you have I guess a souring of expectations or the the trowel of disillusionment per se. That's happening on a much more shorter time span. So what may be at its peak point in terms of expectations, that could pop the next day and then you have a whole new thing which is propped up. So the rotation of innovation is a lot more frequent. So the bubbles are popping on a very regular basis but the bubbles are smaller.
English
United States
TRANSCRIPT 🔗
Are you the producer of this podcast?
Add a podcast transcript
Need Audio-to-Text?
Transcribe with Listen411 in Just 60 Seconds
SEARCH PAST EPISODES
Search past episodes of Connected Social Media.
OTHER EPISODES IN THIS PODCAST
The promise of cell and gene therapies is big. They have the ability to address the underlying genetic causes of disease, offering new possibilities for people with serious and previously difficult-to-treat conditions. But as cell and gene therapies move beyond just treating rare diseases to also a…
Nearly half of all adults turn to social media, at least in part, for health information. This represents a growing concern when considering how the spread of health misinformation has ballooned in recent years. The consequences are significant for individuals who may make uninformed decisions abou…
In this video interview, Liqid Founder and CTO Sumit Puri argues the path to affordable inference runs through composable AI infrastructure that dynamically taps into pools of GPUs, CPUs and DRAM while optimizing usage to manage costs. He says Liqid allows a single server to access a pool of 30 AMD…
Intel IT’s IaaS agentic AI platform delivers comprehensive, cross-domain infrastructure management and monitoring across the enterprise IT landscape. A coordinated mesh of specialized AI agents fuses live enterprise telemetry with configuration, incident, and change data, then reasons across live i…
In a video interview, Five Horizons CTO Benjamin Urquhart explains how hyperconverged infrastructure let a nonprofit healthcare provider scale from zero servers to over 50 virtual machines.
Get tech leader insights to move faster and smarter.
Get more stories by subscribing to The Forecast.
Vide…
Disclaimer: The podcast and artwork embedded on this page are from Connected Social Media, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
EDIT
Thank you for helping to keep the podcast database up to date.
