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 might all see AI Canvas’s means to hurry troubleshooting, carry siloed groups collectively, and allow automation throughout your complete stack.
AI Canvas received’t be out there till October. Nevertheless, we wished to supply our CCIEs, CCDEs, and Cisco Licensed DevNet Specialists 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 via 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 study extra and change into 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 assumed I’d provide some pattern use instances that can assist you get began.
Tailor-made situations and coaching paths
As a CCIE, you’ve bought years—typically a long time—of expertise in networking, and also you’re totally in control 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 situations and coaching concepts so that everybody in your group can study the talents wanted for the community you at present 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, nevertheless it’s skilled explicitly on a depth and breadth of Cisco-specific materials. It’s additionally skilled on the supplies and coursework out there in Cisco U. You may attempt a immediate comparable to this one:
- I’m the tech lead for a small group of community engineers. I have to rapidly get them in control on the networking know-how 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 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 typically you want extra steerage. The Deep Community Mannequin AI Assistant can assist make CVDs extra navigable. It might entry different sources to assist flesh out CVDs and provide options for enhancing or optimizing designs.
It might 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 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. 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 enhancing or optimizing designs.
- What sort of storage know-how ought to I think about for booting my blades in a UCS B chassis?
In the event you’re having points with a CVD, you possibly can ask the Deep Community Mannequin AI Assistant the place it is best to begin wanting.
Automation assistant
The Deep Community Mannequin AI Assistant also can assist with automation. You would ask it questions comparable to:
- I’m an professional in community structure and want some assist automating our department SD-WAN deployment. What can be a well-supported, easy-to-learn instrument that may assist me help this? My group 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 can assist analyze community diagnostics, comparable to syslog messages and debug output, and study downside signs to offer perception that may 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 would merely enter the message into the assistant and say you want recommendation or a spot to begin. As a result of it’s skilled on Cisco’s syslog codecs, it may give 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 possibly can 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 approach, you should utilize the assistant to information you to the purpose the place you’re comfy taking up.
A whole lot of debugging is actually several types of diagnostic knowledge and trying to find the needle in a haystack that can assist you understand what to do subsequent. The Deep Community Mannequin AI Assistant can assist with that course of. For instance, if it’s essential to troubleshoot routing adjacencies, you’ll probably want to assemble knowledge from a number of units 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 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 bought:
Sooner or later, 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 possibly can 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 might determine hard-to-spot points and dramatically enhance 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 appeared 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 gadget. So my SNMP supervisor was confused, and the SNMPv3 entice wasn’t being trusted. Bug discovered!
Whereas most of us are fairly conversant in 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 superb at analyzing these fields and explaining their network-relevant context. Whereas the assistant received’t resolve 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 might be useful to skilled community engineers. I hope they’re a helpful springboard on 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 instances you’ve give you that I would by no means consider. AI is an extremely highly effective instrument that may make us extra environment friendly and, frankly, much less harassed. 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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