Guillaume de Saint-Marc, VP of Engineering at Cisco Outshift, explains the architectural transition from rigid, deterministic enterprise workflows to dynamic multi-agent collaboration.
Prediction Not checkable as stated
Saint-Marc: Superintelligence will emerge from multi-agent scaling, not single models
“We had this strong thesis and vision that the intelligence will come not just from building ever smarter and ever bigger and more powerful single agents But more through what we call horizontal scaling. So we distributed systems, a lot of agents coming togethe…”
Assertion Not checkable as stated
Saint-Marc: Agentic root cause analysis cut troubleshooting from days to minutes
“Another agent will have, will be like the agent capable of finding troubleshooting, like finding what we call the root cause analysis, you know, doing the root cause analysis of the problem. We had some amazing results on this, literally going from putting exp…”
Insight
Saint-Marc: AI agents without agency are useless, but unconstrained agency is dangerous
“An agent with no agency is useless. An agent with you know, too much agency can be dangerous and especially if it goes out of control.”
Insight
Saint-Marc: Agentic AI requires adding Layers 8 and 9 to OSI model
“We've also published a paper extending the Aussie model. So, like this, you know, most engineers will know the seven layers of the Aussie model, the IP stack and the different communication layers, and level seven is really where the world has been living for …”
Prediction Not checkable as stated
Saint-Marc: Future Enterprise Agent Swarms Will Be Deeply Heterogeneous
“As complexity is growing, and as we, again, as I said before, we asymptotically start to progress towards something we can call superintelligence, It's going to be very heterogeneous, very different types of agents, different vendors, different clouds, differe…”
Disclosure
Saint-Marc: Cisco Outshift is developing an SLM for Semantic TBAC
“This is stuff that we are going to share soon, but like for the T-back functionality, the semantic teabag. For now, we are using you know, pick your model. So, connect the model you want to actually power the solution. But we are also working on a small langua…”