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 the whole stack.
AI Canvas received’t be accessible till October. Nevertheless, we needed to supply our CCIEs, CCDEs, and Cisco Licensed DevNet Specialists the chance to work with the Deep Community Mannequin as quickly as attainable. So we’re making the mannequin accessible to CCIEs and different consultants by way of 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 might help them study extra and turn out to be extra environment friendly. However we understand that agentic ops is model new, and that you simply 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 situations and coaching paths
As a CCIE, you’ve received years—typically 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 situations and coaching concepts so that everybody in your workforce can study the talents 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 skilled explicitly on a depth and breadth of Cisco-specific materials. It’s additionally skilled on the supplies and coursework accessible in Cisco U. You may attempt a immediate akin 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 know-how we use in our surroundings, together with BGP, MPLS, and OSPF. Might you construct me a customized examine plan?
Once I requested this query of the Deep Community Mannequin AI Assistant, I received 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 basically blueprints, and IT professionals are accustomed to working by way of them. However typically you want extra steering. The Deep Community Mannequin AI Assistant might help make CVDs extra navigable. It may possibly entry different sources to assist flesh out CVDs and provide solutions for enhancing or optimizing designs.
It may possibly additionally summarize the CVD, supplying you with a high-level overview earlier than studying the entire thing. You possibly can ask it questions akin 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 might help validate an present design with respect to a CVD and provide solutions 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?
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 may also assist with automation. You might ask it questions akin to:
- I’m an professional in community structure and wish some assist automating our department SD-WAN deployment. What can be a well-supported, easy-to-learn device that may assist me assist this? My workforce doesn’t have a substantial amount of coding expertise. Might you present examples and hyperlinks to related documentation and coaching?
Troubleshooting
The Deep Community Mannequin AI Assistant might help analyze community diagnostics, akin to syslog messages and debug output, and study 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 guage the output for accuracy and detect hallucinations.
For instance, the Deep Community Mannequin AI Assistant may assist interpret a syslog message. You might 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 can provide steering and cross-reference different information.
When you’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 need to all the time watch out about pasting uncooked output into AI.) On this means, you need to use the assistant to information you to the purpose the place you’re comfy taking up.
A variety of debugging is actually taking a look at several types of diagnostic information and looking for the needle in a haystack that can assist you understand what to do subsequent. The Deep Community Mannequin AI Assistant might help with that course of. For instance, if you could troubleshoot routing adjacencies, you’ll probably want to assemble information 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 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 tackle FE80::300:241 link-local ipv6 tackle 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 received:
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 (akin to that from Wireshark or Tshark) to the Deep Community Mannequin AI Assistant, which may break down the body particulars for you. It may possibly establish hard-to-spot points and dramatically improve the efficacy of deep networking troubleshooting.
The AI assistant can provide you extra which 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 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 system. So my SNMP supervisor was confused, and the SNMPv3 lure wasn’t being trusted. Bug discovered!
Whereas most of us are fairly conversant in a variety of community applied sciences, we might not be consultants in each one of many protocols we run on our community. Subsequently, think about how helpful this may be for a protocol you’re not extremely educated about on the discipline stage. The AI assistant is superb at analyzing these fields and explaining their network-relevant context. Whereas the assistant received’t clear up 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 a number 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 on your considering. 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 give you 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 burdened. However we should work out the perfect methods to make use of them, and we’re all on that journey collectively.
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