At Cisco Reside in San Diego, D.J. Sampath, Senior Vice President of Cisco’s AI Software program and Platform group, wowed the gang 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 means to hurry troubleshooting, convey siloed groups collectively, and allow automation throughout the complete stack.
AI Canvas gained’t be out there 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 potential. So we’re making the mannequin out there to CCIEs and different specialists by means of an AI Studying Assistant out there in Cisco U.
We expect CCIEs (and shortly, different community engineers) will discover a wealth of ways in which the Deep Community Mannequin can assist them be taught 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 supply some pattern use instances that can assist you get began.
Tailor-made eventualities and coaching paths
As a CCIE, you’ve acquired years—typically a long time—of expertise in networking, and also you’re totally 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 be taught the talents wanted for the community you at the moment 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 out there in Cisco U. You would possibly strive a immediate comparable to this one:
- I’m the tech lead for a small workforce of community engineers. I must shortly get them up to the mark on the networking expertise we use in the environment, together with BGP, MPLS, and OSPF. Might you construct me a customized research plan?
Once I requested this query of the Deep Community Mannequin AI Assistant, I acquired a really good syllabus in define type, with hyperlinks to programs in Cisco U.
Right here’s a pattern:
Design validation and optimization
Cisco Validated Designs (CVDs) are basically blueprints, and IT professionals are accustomed to working by means of them. However typically you want extra steering. The Deep Community Mannequin AI Assistant can assist make CVDs extra navigable. It may entry different sources to assist flesh out CVDs and supply strategies for enhancing or optimizing designs.
It may additionally summarize the CVD, providing you with a high-level overview earlier than studying the entire thing. You’ll be able to ask it questions comparable 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. Might 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 can assist validate an present design with respect to a CVD and supply strategies for enhancing or optimizing designs.
- What sort of storage expertise ought to I think about for booting my blades in a UCS B chassis?
If you happen to’re having points with a CVD, you’ll be able to ask the Deep Community Mannequin AI Assistant the place you must begin wanting.
Automation assistant
The Deep Community Mannequin AI Assistant may assist with automation. You possibly can ask it questions comparable to:
- I’m an skilled in community structure and want some assist automating our department SD-WAN deployment. What can be a well-supported, easy-to-learn device that may assist me help this? My workforce doesn’t have quite a lot of coding expertise. Might you present examples and hyperlinks to related documentation and coaching?
Troubleshooting
The Deep Community Mannequin AI Assistant can assist analyze community diagnostics, comparable to syslog messages and debug output, and study downside signs to offer perception that is likely to be missed by human eyes. Though generative AI continues to be a younger expertise 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 possibly can merely enter the message into the assistant and say you want recommendation or a spot to begin. As a result of it’s educated on Cisco’s syslog codecs, it can provide steering and cross-reference different information.
If you happen to’re working with a number of information 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?” (In fact, you must at all times watch out about pasting uncooked output into AI.) On this manner, you should use the assistant to information you to the purpose the place you’re comfy taking up.
Quite a lot of debugging is basically taking a look at several types of diagnostic information and looking for the needle in a haystack that may assist what to do subsequent. The Deep Community Mannequin AI Assistant can assist with that course of. For instance, if you want to troubleshoot routing adjacencies, you’ll doubtless want to collect information from a number of gadgets and correlate the info to determine a root trigger.
You are a community troubleshooting assistant. Assist me diagnose why my OSPFv3 session will not be 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 deal with FE80::300:241 link-local ipv6 deal with 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 acquired:
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 (comparable to that from Wireshark or Tshark) to the Deep Community Mannequin AI Assistant, which might break down the body particulars for you. It may determine hard-to-spot points and dramatically improve the efficacy of deep networking troubleshooting.
The AI assistant can provide you extra that means and context than you would possibly get with different instruments. I attempted this with a problematic SNMPv3 packet. The AI assistant regarded on the worth of the fields and defined them to me. Whereas Wireshark confirmed me the sector names, the AI assistant defined that one area, 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 entice wasn’t being trusted. Bug discovered!
Whereas most of us are fairly acquainted with a variety of community applied sciences, we is probably not specialists in each one of many protocols we run on our community. Due to this fact, think about how helpful this may be for a protocol you’re not extremely educated about on the area 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 number 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 in your pondering. If you happen to strive 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 instances you’ve provide you with that I’d by no means consider. AI is an extremely highly effective device that may make us extra environment friendly and, frankly, much less pressured. However we should work out the most effective methods to make use of them, and we’re all on that journey collectively.
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