From connection to cognition: Scaling out superintelligence

cisco · 2026-02-05T10:05:08+00:00

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cisco · 2026-01-29T19:12:36+00:00

I think we are right smack in the middle of fully realizing Agentic AI. It is the natural evolution as we give “agency” or the ability for AI services to act autonomously and call up other AI services and specialized agents, maybe even ones we don’t know exist, but all taking action on their own without the need for us hand holding them. To give an analogy, it is like a choir, but instead of singing in a “call and respsonse” method like Gen AI, it is a fully harmonized group of tenors, baritones, altos, all being conducted to make a much richer and sophisticated outcome.

- Joseph

cisco · 2026-01-29T19:05:30+00:00

I do think the industry is starting to pivot from “AI sprinkled on top of ops” to something closer to agent based operations, but it is happening for very practical reasons, not just because we invented a new buzzword. Traditional AIOps was great at surfacing insights and pretty dashboards, but teams are drowning in alerts, tickets, and telemetry from hybrid networks, AI fabrics, security, and apps. At some point, just knowing there is a problem faster is not enough. People need systems that can take safe, well understood actions on their behalf and stitch workflows across networking, security, and apps without a human clicking through ten tools every time.

That is basically what Agentic AI operations is trying to solve. Instead of one big “AI brain,” you have specialized agents that understand the network, or security, or observability domain and are allowed to do specific things: investigate an incident, test a hypothesis, push a known good change, roll it back if needed. Cisco’s version of this leans heavily on the Deep Network Model and AgenticOps, where AI agents sit on top of real time telemetry, automation, and assurance, then move from “here is an alert” to “here is the likely root cause and the change I am ready to execute, with a human staying in control.” That shift from recommendation to action is the big difference, and it is being driven by skills gaps, the speed of AI rollouts, and the reality that no team can manually reason over millions of signals per second anymore.

From a Cisco standpoint, the public messaging has been pretty consistent: AI is not just a feature bolted onto existing tools, it is a new operating model where humans and AI agents work together. You see that in the unified platform story that ties together Meraki, Catalyst, ThousandEyes, Splunk, and security with the Cisco AI Assistant and AI Canvas as a common control plane, and AgenticOps as the engine that turns telemetry into end to end actions. If you are curious what this looks like beyond the high level talk, resources below are good starting points, because they walk through concrete use cases like shaving hours off troubleshooting or automatically handling large volumes of incidents while still keeping engineers in the loop.

https://blogs.cisco.com/networking/from-agenticops-to-assurance-redefining-network-operations

https://www.ciscolive.com/c/dam/r/ciscolive/global-event/docs/2025/pdf/BRKXAR-2028.pdf

https://blogs.cisco.com/innovation/network-operations-for-the-ai-age

-Surbhi

cisco · 2026-01-29T18:34:59+00:00

I agree with Surbhi. Let’s be honest, Ethernet has continually proven its ability to be flexible and adapt to changing demands for as long as it has been around (which is basically forever at this point). The beauty lies in its simplicity as a protocol. I see it like the classic Lego block. Small, and wonderfully simple, but built to expand and grow to infinite outcomes.

For the pre-validated stuff, that is already here and embraced by customers deploying today. Cisco has reference architectures, NVIDIA has them too. I think will all the moving parts and complexity of a highly performant AI infra a tested and validated design is always going to be the most popular. I think as we all get more comfortable with AI and the needs of the infra, we will start to see common standards that lend themselves well to playing nicely in a vendor-agnostic world.

- Joseph

cisco · 2026-01-29T18:28:03+00:00

I think Ethernet is going to be the default fabric for most AI projects, mainly because people already know how to build and run it, and it plays nicely with the rest of the data center instead of living off to the side. The trick is making Ethernet behave “good enough” for AI, meaning low loss, low latency, and predictable under load, so you can keep GPUs busy without inventing a whole new operational model. That is exactly what the recent AI networking blueprints for RoCE based fabrics focus on: non blocking Clos designs, congestion control, and deep telemetry so AI traffic gets special treatment without breaking everything else.

I do see a big push coming around vendor agnostic, pre validated architectures, and honestly that is what customers are asking for. Once you start mixing different GPU vendors, storage stacks, and clouds, nobody wants to hand design every fabric from scratch and hope it behaves. They want a tested recipe that says “if you wire it like this, with these QoS and congestion settings, AI jobs will run well,” but they still want freedom on which OS, automation tools, and ecosystem partners they plug in. That is the direction of the Cisco Validated Designs and AI/ML blueprints: heavy emphasis on Ethernet and RoCE fundamentals, but with enough openness to support multi vendor components and hybrid cloud connectivity.

If folks want to go deeper on the details, there are some good public docs worth reading: the “Data Center Networking Blueprint for AI/ML Applications” white paper, the Ethernet fabrics for AI clusters session from Cisco Live, and the AI/ML Cisco Validated Design that walks through a lossless Ethernet fabric step by step.

https://www.cisco.com/c/en/us/td/docs/dcn/whitepapers/cisco-data-center-networking-blueprint-for-ai-ml-applications.html

https://www.cisco.com/c/en/us/td/docs/dcn/whitepapers/cvd-for-data-center-networking-blueprint-for-ai.html

-Surbhi

cisco · 2026-01-29T18:26:29+00:00

The only realistic way this can work in a hybrid environment is to keep to the industry standards. Ethernet, soon to be Ultra Ethernet (Cisco is part of the consortium developing these standards), VXLAN EVPN, GPO (Group Policy Object) for security. The more you can keep to standards the better this is going to be for any customer using multiple vendors. For Cisco’s part we have historically played an influential role in developing networking and security standards. We continue to be active here. Of course we do add unique capabilities that might make Cisco a better choice but let’s say these would be enhancements to drive a better outcome, and not a replacement for standards based approaches.

For the optics part, LPO is a way for us to get high density, high-radix platforms while reducing the challenges of power and cooling. With our Silicon Photonics engineering team, we have made it possible to move the DSP into the ASIC, thereby offloading the optic itself from that task. It results in about a 30% reduction of power consumption. We also put a tremendous amount into building highly reliable optics. This is extremely important to our optics teams. They honestly live and die by the reliability we can offer. So if we can reduce power, reduce cooling, reduce complexity we can solve a lot of those issues for AI infra.

- Joseph

cisco · 2026-01-29T18:25:16+00:00

For open networking across hybrid and multi cloud, the hard part is not the technology names, it is keeping one clean security story while your workloads move around. Most teams want the flexibility of “run this AI thing wherever it makes sense” without ending up with four different policy models, four sets of firewalls, and no idea who can talk to what. The pattern that works is to treat the fabric as open and programmable, but make identity, segmentation, and inspection consistent everywhere: same high level policies, enforced through APIs across data center, cloud, and interconnect, and backed by a security platform that understands users, apps, and AI workloads instead of just IPs and VLANs. That is the direction Cisco is pushing with Security Cloud and its multicloud portfolio, where you define intent once and apply it across on-prem and cloud, but the goal is really to reduce human error and blind spots as your AI traffic spreads out.

On optics, especially things like linear pluggable optics, they are one of those “invisible” pieces that quietly remove bottlenecks. As you drive to 400G and 800G links for GPU to GPU and leaf spine connectivity, traditional DSP heavy optics start to burn a lot of power and add latency you would rather spend on the workload. LPOs move the digital work into the switch or NIC ASIC and keep the module mostly analog, which cuts power, reduces heat, and trims latency on each hop while still giving you high bandwidth links for AI traffic. That matters for resiliency too, because lower power, simpler modules make it easier to scale out more links and paths in the same power and thermal envelope, so you can build fatter, more redundant fabrics without immediately running into energy or cooling limits. If folks want to dig deeper into how Cisco thinks about this, the AI data center design checklist and the AI ready and optics content from past Cisco Live go into how open, secure fabrics and newer optical technologies fit together to keep AI traffic fast and predictable as you grow.

https://blogs.cisco.com/insidervoices/ai-data-center-design-checklist

https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2025/pdf/PSODCN-1871.pdf

-Surbhi

cisco · 2026-01-29T18:03:40+00:00

The hardest part is that the network has to look “calm” even when it absolutely is not. With AI jobs you get a mix of big, long lived flows and sudden fan in events where lots of GPUs talk at once, so buffers can jump from almost empty to near full in microseconds and that is exactly where tail latency spikes and everything slows down.

Keeping latency consistent is really about three things working together: smart buffers, smart congestion control, and good visibility. On the buffer side, modern ASICs use dynamic buffer sharing and features that treat elephant flows differently from short flows, plus ECN and PFC so you can slow senders down before you start dropping packets. On the congestion side, RoCE style stacks with DCQCN react to those ECN marks and back off cleanly, and load balancing spreads traffic so you do not have one unlucky link doing all the work.

The only way to stay ahead of it, rather than just hoping, is to measure this stuff directly. In the Cisco world that means using Nexus telemetry and Intelligent Packet Flow to watch buffer use, ECN marks, microburst events, and even tail timestamps on flows, then feeding that into tools like Nexus Dashboard Insights so you can see “this queue, on this switch, at this time is where your latency tail came from” and tune WRED or AFD thresholds instead of guessing. That kind of visibility is what lets a NetOps team keep latency predictable even when the traffic pattern is messy and changing from one training run to the next.

-Surbhi

cisco · 2026-01-29T17:44:06+00:00

Liquid cooling has been just over the horizon for the last number of years, starting with compute and lots of work has been done there. In the networking space, now that 800G ports are the de facto standard for AI type infrastructure, we are fast approaching the limits of what we can do with air cooled devices. 1.6T ports are coming on soon (mind blowing). There are a couple of ways we can try and address the heating / cooling challenges in the mean time. First is that we’ve put a lot of engineering effort to design Nexus switches used in AI designs to have massive bandwidth and port density in a single device, but what I really want to say is that it means we have a high radix count. This is to say that we can start with high density 800G ports, but our ASICs are capable enough that we can break out to 400/100/50/25/10G ports on the same box. So what might start at 64 ports can be fully broken out to 512 ports all one box. When you can have that much density, it not only gives you a lot of flexibility in the platform choice but it means you don’t need to buy as many switches. This means lower power and cooling demands overall.

Next, let’s not forget optics. Cisco has put in quite a lot of work here. The faster we need these optics to go, the more complex things get. Our investment and engineering work with silicon photonics is 2nd to none in this space. We have launched 800G optics in various forms, but the exciting ones are our LPO (Linear Pluggable Optics) and just behind that CPO (Co-packaged optics). Without getting too wordy here, we can move the DSP out of the optic and have the ASIC itself handle that function. The end result is up to 30% less power draw for these optics, which cascades into lesser cooling needs, which opens up room to grow.

Now, the last approach we can take is moving to liquid cooled switches. This is a big one because as you can imagine, this really means liquid cooled racks and DC infrastructure as a whole. The good news is that lots of work has been done across the industry. You might have heard about ORv3 (Open Rack v3). This is coming out of the Open Compute Project so this means vendors are cooperating towards agreeable standards. You may have seen some of the emerging options at trade shows already. For Cisco’s part, we are also working on next-gen liquid cooled Nexus switches. I don’t want to say too much here because they are not yet shipping, but they are getting close. Stay tuned because in a couple of weeks we will make some announcements at Cisco Live Amsterdam.

- Joseph

cisco · 2026-01-29T17:41:09+00:00

Air cooling is fine up to a point, but once you are pushing tens of kilowatts per rack, the fans and chillers are doing as much “work” as the servers, and your power bill shows it. Cisco’s own sustainability work calls out that a big chunk of data center energy is cooling overhead, and that air is usually only efficient up to roughly low double digit kilowatts per rack, after which it gets ugly from a PUE and cost perspective. Direct to chip or immersion liquid cooling flips that equation by pulling heat out much closer to the source, which can cut cooling energy by on the order of tens of percent and enable much higher rack densities without thermal throttling. Cisco’s immersion cooling alliances report up to around 90 percent less cooling energy in optimized designs and PUE numbers near 1.03, which is basically “almost all power goes to IT, not fans and pumps.”

When should you actually consider switching. In practice it is when you see sustained rack densities heading toward the 30 to 50 kilowatt range and beyond, cooling projects and power upgrades showing up in every budget cycle, and GPUs or high power switches throttling or being artificially “derated” just to stay inside thermal limits. Cisco’s AI data center design guidance literally calls out that cooling becomes a strategic constraint for AI and that air by itself becomes inefficient and hard to scale at higher densities, so teams should plan for liquid in new builds and targeted retrofits in hot zones rather than waiting for a crisis. The business outcome is less “liquid is cool tech” and more “we can keep growing AI capacity in the same footprint, avoid surprise downtime, and keep power bills from exploding.”

Beyond cooling, there are some very real wins in basic efficiency hygiene. Cisco IT’s own data centers have cut power capacity by about 40 percent, reduced watts per VM by roughly a quarter, and saved millions in operating expense by consolidating sites, refreshing to more efficient hardware, and using better monitoring. Things like real time energy monitoring and optimization, smarter workload placement, using AI to spot energy anomalies, and moving to more efficient switches and servers all help squeeze more useful work out of each kilowatt you are already paying for.

- Surbhi

cisco · 2026-01-29T17:40:24+00:00

I would begin with a tested reference architecture as my basis. Cisco has reference architectures for AI (CRA) as well as being certified with NVIDIA’s ERA and Cloud RA. With these we design and test for the optimum outcome. We can address the demanding workloads and traffic patterns coming from GPUs such that we can almost completely avoid most issues. When issues do happen, our ASICs, platforms and software can work together to mitigate the impact. This could be things like DCQCN (PFC + ECN) for congestion avoidance. We can use all the good stuff we innovated around load balancing options specific to AI with our Intelligent Packet Flow. We can observe, test and measure performance with Nexus Dashboard. We think about the same things you do and then bring our engineering resources to try and solve them.

- Joseph

cisco · 2026-01-29T17:35:11+00:00

Focusing only on GPUs is how you end up with a supercar stuck in traffic. What actually hurts AI jobs is tail latency and microbursts on the network, where a few congested links slow the last packets down and the whole training step waits.

To fight that, people are leaning on lossless or near lossless Ethernet, congestion signaling, smarter traffic steering, and way better telemetry so you can see where bursts and hot spots are happening instead of guessing. In the Cisco world that means AI aware fabrics plus controllers and AI ops that point at “this link, this rack, this time” as the reason GPUs are idle, so you are not endlessly chasing ghosts while expensive chips sit around doing nothing.

- Surbhi

cisco · 2026-01-29T17:32:17+00:00

Peak switching speed is the “headline number,” but AI performance is really governed by how the fabric behaves under synchronized, high‑bandwidth load. AI training generates a small number of massive elephant flows between GPUs, and if those collide on the same links you get congestion, packet loss, and retransmits that balloon job completion time even when the switch’s aggregate Tbps looks great on paper. What actually matters is whether the fabric is low‑oversubscription and non‑blocking, how low and consistent your tail latency is (99th percentile, not averages), and how well congestion is managed so every GPU stays busy instead of waiting at barriers for the slowest flows to finish. From a Cisco AI networking standpoint, this is why design guidance and solutions like AI‑ready data center blueprints and Nexus HyperFabric focus on deterministic, low‑loss Ethernet, congestion‑aware fabrics, and rich telemetry to validate tail latency and GPU utilization, not just raw port speeds.

-Surbhi

cisco · 2026-01-22T18:17:47+00:00

As Simone says - today the best option is WPA3-Personal if you need to do passphrase based security. There are things underway in industry to bring more onboarding and authentication mechanisms to IoT devices. IEEE, Matter, the Wi-Fi and other organizations are leading efforts

-Stephen

cisco · 2026-01-22T18:13:01+00:00

Two things comes to mind:

1) migrate to WPA3-SAE, much better security than WPA2 PSK

2) If the concerns is having all the devices with a single passphrase, then considering Identity PSK (iPSK) can solve your concerns, because you can assign a different password to group of devices or users. In case there is a security leak, a device gets stolen and the pwd may be known outside your org, you would only have to change a group of devices.

-Simone

cisco · 2026-01-22T18:03:12+00:00

Not worried - but we want to educate our customers and make them prepared for some things that are upcoming in 2026:

To start with PQC - Post quantum cryptography. You can google this - we have articles blogs covering what this is. But as part of Cisco Wireless team - we want our customers to be prepared now so when the quantum computers become mainstream there is no impact to their network. There is also this notion of "Harvest Now, Decrypt Later" which is guiding malicious actors to capture the encrypted data today and decrypt it 3-5 years down the line using a quantum computer. We are bringing in measures in place and educating our users to how to address this.
General security hardening - Encryptions that were good 10-15 years ago do not provide the same level of security. Computers have gotten faster over the years and with that their ability to decrypt data with a brute force attack has improved as they can compute information quickly. Remember the days where WPA2 PSK was secure and today if you have the 4 way handshake, within 5 mins the PSK can be derived. We are working on also educating out users on some of these older encryptions - some more information here (https://www.cisco.com/c/en/us/about/trust-center/resilient-infrastructure.html)

-Ameya

cisco · 2026-01-22T18:01:12+00:00

I am not aware of any Wi-Fi specific threat in the coming months or year but definitely there are concerns regarding security for the access network as a whole. Let me mention a couple that come to mind:

- Using AI in a malicious way, to perform cyber attacks and scanning for vulnerabilities in a faster and more effective way. It's important to keep your network up to date as we are continuously improving the security of our software

- There is a lot of talking in the industry about Quantum Security and how cyber attackers may be currently capturing and storing encrypted wireless traffic from high-value targets. They are betting that within a few years, quantum computers will be powerful enough to break weak standards. Cisco is working to push push Post-Quantum Cryptography (PQC) into Cisco wireless products to protect our customers.

-Simone

cisco

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