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 capability to hurry troubleshooting, convey siloed groups collectively, and allow automation throughout your complete stack.
AI Canvas gained’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 potential. 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 will help them study extra and turn out to be extra environment friendly. However we understand 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 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 acquired years—generally many years—of expertise in networking, and also you’re absolutely in control in your group’s IT infrastructure. However what about your crew 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 crew 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, however it’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 strive a immediate similar to this one:
- I’m the tech lead for a small crew of community engineers. I have to shortly get them in control on the networking expertise we use in our surroundings, together with BGP, MPLS, and OSPF. May you construct me a customized examine 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 generally you want extra steerage. The Deep Community Mannequin AI Assistant will help make CVDs extra navigable. It may entry different sources to assist flesh out CVDs and provide strategies for bettering or optimizing designs.
It may additionally summarize the CVD, supplying you with a high-level overview earlier than studying the entire thing. You possibly can ask it questions similar 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 strategies 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’ll be able to ask the Deep Community Mannequin AI Assistant the place you need to begin trying.
Automation assistant
The Deep Community Mannequin AI Assistant also can assist with automation. You possibly can ask it questions similar to:
- I’m an professional in community structure and wish some assist automating our department SD-WAN deployment. What could be a well-supported, easy-to-learn software that may assist me help this? My crew 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, similar 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 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 possibly 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 skilled on Cisco’s syslog codecs, it can provide steerage and cross-reference different knowledge.
When 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?” (In fact, you need to at all times 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 snug taking on.
A number of debugging is actually various kinds of diagnostic knowledge and trying to find the needle in a haystack that can assist what to do subsequent. The Deep Community Mannequin AI Assistant will help with that course of. For instance, if it’s good to troubleshoot routing adjacencies, you’ll possible 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 is just not 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:
Sooner or later, many people find yourself troubleshooting on the protocol degree (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 (similar to that from Wireshark or Tshark) to the Deep Community Mannequin AI Assistant, which may break down the body particulars for you. It may determine hard-to-spot points and dramatically enhance the efficacy of deep networking troubleshooting.
The AI assistant can provide 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 sphere 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 accustomed to 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 area degree. 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 far simpler.
These are simply among 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 considering. When you 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 circumstances you’ve give you that I would by no means consider. AI is an extremely highly effective software that may make us extra environment friendly and, frankly, much less harassed. However we should determine one of the best methods to make use of them, and we’re all on that journey collectively.
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