At Cisco Dwell in San Diego, D.J. Sampath, Senior Vice President of Cisco’s AI Software program and Platform group, wowed the group with a demo of AI Canvas. That’s a multi-data, multi-agent system, built-in with Cisco’s AI Assistant and powered by Cisco’s Deep Community Mannequin. In that demo, we might all see AI Canvas’s capacity to hurry troubleshooting, convey siloed groups collectively, and allow automation throughout your complete stack.
AI Canvas gained’t be accessible till October. Nevertheless, we needed to supply our CCIEs, CCDEs, and Cisco Licensed DevNet Consultants the chance to work with the Deep Community Mannequin as quickly as doable. So we’re making the mannequin accessible to CCIEs and different consultants via an AI Studying Assistant accessible in Cisco U.
We expect CCIEs (and shortly, different community engineers) will discover a wealth of ways in which the Deep Community Mannequin will help them study extra and change into extra environment friendly. However we notice that agentic ops is model new, and that you just is likely to be questioning how one can instantly begin experimenting with the Deep Community Mannequin. So I assumed I’d provide some pattern use circumstances that can assist you get began.
Tailor-made eventualities and coaching paths
As a CCIE, you’ve bought years—generally many years—of expertise in networking, and also you’re absolutely up to the mark in your group’s IT infrastructure. However what about your workforce members, particularly extra junior community engineers? The Deep Community Mannequin AI Assistant can be utilized to construct tailor-made eventualities and coaching concepts so that everybody in your workforce can study the abilities wanted for the community you presently have, in addition to any new applied sciences your group plans to roll out.
The Deep Community Mannequin understands a variety of networking applied sciences, but it surely’s educated explicitly on a depth and breadth of Cisco-specific materials. It’s additionally educated on the supplies and coursework accessible in Cisco U. You would possibly attempt a immediate equivalent to this one:
- I’m the tech lead for a small workforce of community engineers. I have to shortly get them up to the mark on the networking know-how we use in the environment, together with BGP, MPLS, and OSPF. May you construct me a customized research plan?
After I requested this query of the Deep Community Mannequin AI Assistant, I bought a really good syllabus in define kind, with hyperlinks to programs in Cisco U.
Right here’s a pattern:
Design validation and optimization
Cisco Validated Designs (CVDs) are primarily blueprints, and IT professionals are accustomed to working via them. However generally you want extra steerage. The Deep Community Mannequin AI Assistant will help make CVDs extra navigable. It may well entry different sources to assist flesh out CVDs and provide solutions for bettering or optimizing designs.
It may well additionally summarize the CVD, providing you with a high-level overview earlier than studying the entire thing. You may ask it questions equivalent to:
- Contemplating the CVD for FlexPod, present a getting-started doc that I can use to configure my preliminary UCS supervisor.
- I’m starting to implement the CVD for FlexPod. May you give me a high-level overview of what I’ll be doing and the items I’ll be working with?
The Deep Community Mannequin AI Assistant will help validate an present design with respect to a CVD and provide solutions for bettering or optimizing designs.
- What sort of storage know-how ought to I take into account for booting my blades in a UCS B chassis?
In the event you’re having points with a CVD, you’ll be able to ask the Deep Community Mannequin AI Assistant the place it is best to begin wanting.
Automation assistant
The Deep Community Mannequin AI Assistant may assist with automation. You can ask it questions equivalent to:
- I’m an skilled in community structure and wish some assist automating our department SD-WAN deployment. What can be a well-supported, easy-to-learn instrument that might assist me help this? My workforce doesn’t have an excessive amount of coding expertise. May you present examples and hyperlinks to related documentation and coaching?
Troubleshooting
The Deep Community Mannequin AI Assistant will help analyze community diagnostics, equivalent to syslog messages and debug output, and look at drawback signs to supply perception that is likely to be missed by human eyes. Though generative AI remains to be a younger know-how that may make errors, expert-level IT professionals are well-equipped to judge the output for accuracy and detect hallucinations.
For instance, the Deep Community Mannequin AI Assistant might assist interpret a syslog message. You can merely enter the message into the assistant and say you want recommendation or a spot to start out. As a result of it’s educated on Cisco’s syslog codecs, it can provide steerage and cross-reference different knowledge.
In the event you’re working with a number of knowledge sources, the evaluation turns into extra advanced. With the Deep Community Mannequin AI Assistant, you’ll be able to describe the symptom you see after which ask, “What ought to I search for?” (After all, it is best to at all times watch out about pasting uncooked output into AI.) On this method, you need to use the assistant to information you to the purpose the place you’re comfy taking up.
Numerous debugging is actually taking a look at various kinds of diagnostic knowledge and trying to find the needle in a haystack that can assist you recognize what to do subsequent. The Deep Community Mannequin AI Assistant will help with that course of. For instance, if that you must troubleshoot routing adjacencies, you’ll seemingly want to assemble knowledge from a number of gadgets and correlate the information to determine a root trigger.
You are a community troubleshooting assistant. Assist me diagnose why my OSPFv3 session isn’t establishing with one neighbor. That is the output from ‘present ospfv3 neighbor’:
OSPFv3 1 address-family ipv6 (router-id 192.0.2.1)
Neighbor ID Pri State Lifeless Time Interface ID Interface 192.0.2.2 128 EXCHANGE/BDR 00:00:38 13 Vlan300 192.0.2.6 128 FULL/DR 00:00:37 5 Vlan300 And that is the related config from Vlan300: ipv6 handle FE80::300:241 link-local ipv6 handle 2001:DB8::241/64 ipv6 allow ipv6 mtu 1500 ipv6 nd dad makes an attempt 0 ipv6 nd ra suppress all no ipv6 redirects ipv6 ospf 1 space 0 bfd interval 1000 min_rx 1000 multiplier 5
Right here’s the response I bought:
In some unspecified time in the future, many people find yourself troubleshooting on the protocol stage (packet seize or it didn’t occur, proper?), the place issues get advanced in a short time. On this case, you’ll be able to paste the decoded output of a packet seize (equivalent to that from Wireshark or Tshark) to the Deep Community Mannequin AI Assistant, which might break down the body particulars for you. It may well determine hard-to-spot points and dramatically enhance the efficacy of deep networking troubleshooting.
The AI assistant can provide you extra which means and context than you would possibly get with different instruments. I attempted this with a problematic SNMPv3 packet. The AI assistant appeared on the worth of the fields and defined them to me. Whereas Wireshark confirmed me the sphere names, the AI assistant defined that one discipline, the msgAuthoritativeEngineTime, represented the variety of seconds a tool had been on-line, which was 61411 (roughly seven weeks). The factor is, I simply booted that machine. So my SNMP supervisor was confused, and the SNMPv3 lure wasn’t being trusted. Bug discovered!
Whereas most of us are fairly aware of a variety of community applied sciences, we might not be consultants in each one of many protocols we run on our community. Subsequently, take into account how helpful this may be for a protocol you’re not extremely educated about on the discipline stage. The AI assistant is great at analyzing these fields and explaining their network-relevant context. Whereas the assistant gained’t remedy the issue for you, when used correctly, it can provide you some good hints. When you perceive extra about these fields, making use of some reasoning and fixing the bug is way simpler.
These are simply a few of the ways in which the Deep Community Mannequin AI Assistant might be useful to skilled community engineers. I hope they’re a helpful springboard to your considering. In the event you attempt them out, I’d be excited to listen to in regards to the outcomes you’re getting.
However I’d be much more excited to listen to about use circumstances you’ve give you that I’d by no means consider. AI is an extremely highly effective instrument that may make us extra environment friendly and, frankly, much less careworn. However we should work out one of the best methods to make use of them, and we’re all on that journey collectively.
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