At Cisco Stay 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 may all see AI Canvas’s skill to hurry troubleshooting, carry siloed groups collectively, and allow automation throughout your complete stack.
AI Canvas received’t be obtainable till October. Nevertheless, we wished to supply our CCIEs, CCDEs, and Cisco Licensed DevNet Consultants the chance to work with the Deep Community Mannequin as quickly as attainable. So we’re making the mannequin obtainable to CCIEs and different consultants by means of an AI Studying Assistant obtainable in Cisco U.
We predict 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 turn out to be extra environment friendly. However we understand that agentic ops is model new, and that you just may be questioning how one can instantly begin experimenting with the Deep Community Mannequin. So I believed I’d provide some pattern use circumstances that will help you get began.
Tailor-made eventualities and coaching paths
As a CCIE, you’ve obtained years—typically many years—of expertise in networking, and also you’re totally up to the mark in your group’s IT infrastructure. However what about your group 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 group can be taught the abilities 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 skilled explicitly on a depth and breadth of Cisco-specific materials. It’s additionally skilled on the supplies and coursework obtainable in Cisco U. You may attempt a immediate equivalent to this one:
- I’m the tech lead for a small group of community engineers. I have to shortly get them up to the mark on the networking expertise we use in our surroundings, 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 obtained 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 will probably entry different sources to assist flesh out CVDs and provide options for bettering or optimizing designs.
It will probably additionally summarize the CVD, supplying you with a high-level overview earlier than studying the entire thing. You’ll be able to 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 can assist validate an present design with respect to a CVD and provide options for bettering or optimizing designs.
- What sort of storage expertise ought to I contemplate for booting my blades in a UCS B chassis?
When you’re having points with a CVD, you possibly can ask the Deep Community Mannequin AI Assistant the place you must begin wanting.
Automation assistant
The Deep Community Mannequin AI Assistant can even assist with automation. You would ask it questions equivalent to:
- I’m an knowledgeable in community structure and want some assist automating our department SD-WAN deployment. What could be a well-supported, easy-to-learn device that might assist me help this? My group doesn’t have a substantial amount of coding expertise. May you present examples and hyperlinks to related documentation and coaching?
Troubleshooting
The Deep Community Mannequin AI Assistant can assist analyze community diagnostics, equivalent to syslog messages and debug output, and study drawback signs to offer perception that may be missed by human eyes. Though generative AI remains to be a younger expertise that may make errors, expert-level IT professionals are well-equipped to guage the output for accuracy and detect hallucinations.
For instance, the Deep Community Mannequin AI Assistant may assist interpret a syslog message. You would merely enter the message into the assistant and say you want recommendation or a spot to start out. As a result of it’s skilled on Cisco’s syslog codecs, it may give steering and cross-reference different knowledge.
When you’re working with a number of knowledge sources, the evaluation turns into extra complicated. With the Deep Community Mannequin AI Assistant, you possibly can describe the symptom you see after which ask, “What ought to I search for?” (In fact, you must all the time watch out about pasting uncooked output into AI.) On this approach, you need to use the assistant to information you to the purpose the place you’re comfy taking on.
Numerous debugging is actually various kinds of diagnostic knowledge and looking for the needle in a haystack that may assist you recognize what to do subsequent. The Deep Community Mannequin AI Assistant can assist with that course of. For instance, if it’s worthwhile to troubleshoot routing adjacencies, you’ll possible want to collect knowledge from a number of units and correlate the info to establish a root trigger.
You are a community troubleshooting assistant. Assist me diagnose why my OSPFv3 session just 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 Useless 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 obtained:
Sooner or later, many people find yourself troubleshooting on the protocol stage (packet seize or it didn’t occur, proper?), the place issues get complicated in a short time. On this case, you possibly can 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 will probably establish hard-to-spot points and dramatically improve the efficacy of deep networking troubleshooting.
The AI assistant may give you extra that means and context than you may 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 subject, 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 system. So my SNMP supervisor was confused, and the SNMPv3 entice 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, contemplate how helpful this may be for a protocol you’re not extremely educated about on the subject stage. The AI assistant is great at analyzing these fields and explaining their network-relevant context. Whereas the assistant received’t remedy the issue for you, when used correctly, it may give 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 could possibly be useful to skilled community engineers. I hope they’re a helpful springboard to your pondering. When 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 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 confused. 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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