Build Reliable, Secure, and Performant Agents using Fabrix.AI AgentOps Platform

Super excited to be here. Uh, presenting to this delegation, this is actually our first time, uh, at Tech Field Day. And, uh, my name is Ish Makar.

I'm the, uh, chief Marketing Officer here at, uh, fabrics ai. And, uh, before this, been in the Valley for almost 30 plus years. Uh, one company in particular was Waca, uh, before this, where I was the head of AI and Strategic Alliances, worked with guys like Fredrik and others, right?

So, understand the infrastructure side of things for AI extremely well. And there's a lot of action happening today. But what I'm going to talk about is something on top of the AI infrastructure today, right?

So this is an agent platform, uh, which I and a colleague Rashid will be talking. So we'll have it, uh, kind of divided into two sections. So I'll be giving the company and the product introduction as well as, uh, you know, Rashid will go do a deep dive and then get to the demo.

Okay? So with that, this is kind of the time allocation we have. We have a section for q and a, but feel free to ask us questions as we go along.

If there are pressing questions. Uh, this is, uh, extremely new for all of us. Okay?

So we are all learning as we go along. So who is Fabric? com, right?

For last, uh, eight years or so, from a go to market perspective, are very well recognized in the AI ops space. We were an observability and AI ops player to begin with. And then, um, starting last, uh, beginning of the last year, right, January, we rebranded ourselves to fabric start ai, right?

For a reason. Obviously, the use cases had changed with Agent Take coming on the horizon, our customers were asking us to kind of get new functionality, right? And that was really the genesis to fabrics ai.

So again, um, we are headquarter here in the East Bay, uh, with, uh, you know, r and d office in India, satellite offices across the globe. Uh, we're very well known in the analyst community, right? And obviously, as you guys know, agent is all new.

Nevertheless, we did get featured into two prominent Gartner publications. One, we just actually came out this, this week with Arun, okay? So we'll talk about that as we go along.

So, fabrics, uh, the founding team, right? So we have our CEO and the CPU in the back, I Rashid here, but this is kind of our fourth startup, the founding teams, right? So we have had successful exits before three of them.

So we've been in this business of actually kind of building companies and startups, which are kind of morphed into larger business units, serving tens and thousands of customers. Okay? So this is not our first rodeo, okay?

So again, uh, for fabrics, uh, uh, in the AI ops days, we had some real large market customers that are also now a good amount of large customers, uh, who are actually in production. Some of them are actually doing POC, okay? So you can see, uh, fortune 400 customers, uh, right, MSPs and CSPs, communication service providers.

So obviously because of our pedigree, we, we tend to work with, uh, the likes of Cisco and IBMA lot, right? Uh, and that's where the, the partnerships come from. And then expanding to other verticals beyond IT ops, right?

So our classics, uh, kind of, uh, uh, sweet spot, was it operations, knock ops, right? And AI ops, right? But because of agent ticket kind of enables us to kind of cross the domain across in Bops and SecOps and so on and so forth, right?

Because we are essentially collapsing at the data level and the application level, okay? And we'll talk about that as we, we go along. Okay?

So let's kind of walk. Hey, why agent take when, when, you know, we are an AIOps player? So on the left hand side, you'll see this, right?

So the classic workflow, uh, in an AI ops environment. So you have an application and infrastructure, uh, stack, which is sending signals, uh, to the AI ops kind of, uh, stack, right? Or, or, uh, basically the backbone, which is essentially leveraging machine learning and to some extent, uh, you know, deep learning to basically do correlation, root cause analysis, right?

But the process for remediation is, is primarily manual, right? So now what you are doing is what I call the last mile problem is you are basically exposing dashboards, you're exposing incidents, and it's up to the domain experts to interpret that and kind of formulate and remediation path, right? So heavy kind of, uh, intervention with, with, uh, you know, uh, with humans in there, right?

So from there, as, uh, you know, the customers started asking, and, and again, this, what drove us actually, uh, to the agent ops was actually a customer environment, right? So we had a telco, uh, which suffered, you know, an eight hour of outage just because somebody inadvertently did ACL changes, right? Access control list changes.

And it, it took them almost eight hours to find the problem to remediate the problem, right? And these are perfect use cases, which can be remedi remediated with agent takeoffs, agent iops, agent TKI ops, right? So what's the new stack?

Like, we still have that application stack, we interface with, we have signals coming in right now, the LLM is at the center of reasoning or the decision making, right? And we'll talk about the, the good and the bad part of that as well, right? Because it doesn't come free, right?

I mean, LLMs, at the end of the day, they are non-deterministic processes, right? But what they enable us to do is now you can have workflows which are semi-autonomous or autonomous, right? Or why call semi-autonomous is you typically customers till they build trust, they will have human in the loop, right?

HITL, what we call, uh, the, the, the LMS themselves can do deep research, right? So you don't have to depend on a domain expert saying that, Hey, have you gotten my networking logs or have you gotten my metrics from the application, right? The LLMs are good in doing what we call chain of thoughts or graph of thoughts, right?

Uh, kind of reasoning, okay? Uh, and then, uh, it's cross domain. It can talk to a multiple, and especially it's true with us.

Uh, this is one of the major prerequisite for an a successful agent platform that you have to be able to connect to, you know, a number of different endpoints, right? Because in IT ops particularly, you have storage your network, you have applications, you have a PM tools, what have you, right? And then, uh, the inplace data access is also something which is very unique to us, and we'll kind of talk about it, uh, uh, as we go along.

But each of these tools, what we interface with are domain specific tools, right? And they don't want to integrate with each other, but by agent take and what we have in terms of what we call dynamic tooling, we are able to get to those end points and data sources, right? Directly, right?

And we'll talk about how we do that, okay? So, uh, that's kind of the setting the stage on how we are transitioning from AIOps to agent TKI, right? Um, now having kind of shown the promise of agent take, right?

There are also challenges with agent because the primary kind of LLM, uh, what you're depending on, right? The reasoning, uh, LLM is actually, uh, a non-deterministic LLM, right? And, uh, it, it's what we call stochastic processes, right?

But in addition to that, there are other elements also which contribute to the, to the hallucination, right? So particularly there are three main kind of, uh, you know, reasons what, what we kind of, um, articulate as the reasons for hallucination. So first and foremost, context and data management, right?

So context is the king, as I say, right? So the output tokens, which an LLM Pro kind of puts out is directly proportional to the veracity of the input tokens, right? That means simply put that context is how much curated context you are providing to the LLM really determines your accuracy, right?

So that's the most important thing. And you'll see our key IP being that middleware, which comprises of context engine and what we call dynamic tooling, right? Also data management at scale, right?

So we are not talking about, you know, an experimental demo. It's very easy. It's one thing to do a demo, right?

Where you have five devices, it's another thing when you are in a real life production environment, you are getting 10,000 rows of SQL, right? You're getting large logs, you're getting metrics, right? You're interfacing to almost hundreds of devices, right?

Uh, how do you manage all of that, right? So that's where the data management part comes in. How, I mean, for, for MCP, as you guys know, right?

The model context protocol is very transformative, right? You don't have now EFLs then in your code anymore, right? You're essentially handing over a manual to the LLM, right?

Hey, these are my tool sets, and out of this, select what you feel appropriate, right? But at the same time, it also comes at a cost because every conversation has to go to an LLM and back and forth, right? So how we have simplified that, uh, is also, uh, our key ip, lack of connectivity.

As I said, we connect to almost 1700 plus data sources, right? And even beyond that. So that's very crucial when it comes to IT ops and AIOps, uh, use cases, right?

And finally, agent tops is where, you know, hey, having a platform is one thing. Operationalizing is, is another thing, right? And what I mean by operationalizing is observability security, right?

Explainability, right? Trust governance. How do you bring all of this, uh, where the customers you're building in the trust with the customer, okay?

Uh, and that's what really, you know, all these studies, what you're seeing about MIT or Cisco talking about, hey, there's a lot of noise, but there's only 5% of the value, right? And I call that as agentic value gap, right? So you're doing a lot of experimentation and demos, but it's really, you know, very little of enterprise use, uh, coming out of this agent use cases.

Making sense so far, folks? Any questions, comments? I'm Waiting for you to get to it because I have a ton of questions, but I'm waiting for you to kind of lay it out a little bit More, okay?

Sure. Yeah. So, we'll, we'll get to the, to the gist of it, right?

So again, if you see the progression with agent take applications, right? So just like with the three tier application, we introduce a middleware layer, right? Uh, for complex application scenarios.

Similarly, with agent take, what we are seeing is most of the applications today are the, the, the two tier one where the AI agents and LLMs are directly talking to MCP endpoints, right? So MCP has to be unable for the endpoints because they, that is where you get to a structure with the LLM can understand. Now, what happens when there is data sources, which are not MCP enabled, right?

Which is pri primarily telemetry. You are, you are reading raw data or you are reading from legacy devices, right? So that's a pitfall.

That's another reason why, you know, what leads to hallucination on the right hand side is our solution. So what we have introduced is what we call this fabrics. Middleware, right?

And there are two main components to this middleware. So one is what we call the context engine, and the second one is what we call universal tooling, right? And the context engine in simpl, in simplistic term, what it does is it provides curated data to the LLM only gives it what it needs.

It's not dumping everything, right? Because that's where the, the LLMs you, you are overriding the context. You're corrupting the context and the L LMS hallucinate, right?

And the second part, the universal tooling is taking care of how do you dynamically connect to this disparate data sources, right? You get that data schema and you normalize in a way such that the LLM understands, right? So tho that's the key ip, which we'll be talking about.

And what does that do? It allows us to talk to obviously any MCP data source, right? If you have an MCP data source, that's great, right?

And we'll provide you still all that context, um, capabilities and so on. But also any API, right? So any API, uh, based data source we have, we will actually create that, uh, MCP wrapper on top of that through the middleware, right?

So, which is huge as well as raw data and legacy system, right? So just to give you an example of this, right? What I mean by this, right?

So say my intent, right? Uh, I'm providing an intent to the platform saying that, Hey, I want to get metrics from this application servers. Now, it so happens that those application servers are monitored by, uh, Dynatrace as an a PM tool right now, uh, my LLM and my, my system goes and checks that, hey, uh, there is no tools, uh, MCP tools available for, uh, getting the metrics from Dynatrace, right?

At that point, what we'll do is we'll launch and tool handler a tool creator, which will actually scrap the internet. It'll look for publicly available Dynatrace APIs, right? And it'll formulate the tools, four or five tools, right?

Which, uh, essentially are, are are there to talk to the di Dynatrace controller, get that metrics right? And we have a demo, um, demonstrating that, right? So this is what I mean by dynamically creating, um, you know, um, the, the ability to go and talk to data sources, which are not MCP enabled.

Making sense? So you guys sit at the data la the data acquisition layer of an AI process, right? About the data acquisition, the middleware sits on the above the data acquisition.

There is a data fabric which integrates with the data sources, Okay? And what y'all do is you take, lemme see if I get this right. So you, you somehow provide the data or clean the data, get it ready for the MCP, but you also work with data sources that aren't from, that aren't involved with MCP to get them prepared to go into the data set.

That'll be used to train a model. Yes, absolutely. Right?

So in short, yeah. And we will also, uh, integrate with that. That's what we call the dynamic MCP.

Okay? And you're correlating the data from all those sources so that I understand, you know, what the information in one system means to, to the other, and how it's giving me the full picture. Yeah.

That's, that's further down, right? So now we are sending it all to the LLM. Mm-hmm.

Right? And which is actually correlating and doing the root cause analysis and so on, right? And we provided a template, which we'll talk about, right?

We'll give it some examples that, hey, this is how you should go about doing this, right? Isn't it Ray Lui, Silverton Consulting, isn't there a challenge here with normalization of the data coming from different data sources and trying to understand the range and those sorts of characteristics of the data you're getting? Yeah, absolutely.

And that's where our, our value prop comes in. So the middleware actually takes care of that normalization, right? It normalizes it in a way where the LLM understands that structure.

Because if you give LLM unstructured, which doesn't have structure, is bound to ate. So that's, that's really the value prop. You're absolute, I'm also interested in how your context engine manages the context.

'cause context, uh, growth is a major challenge in most of these things. Yeah, absolutely. So we'll be going actually really deep in, in, um, rashid's section on that context engine.

But just to clarify, right? So this, I mean, again, we come from that, uh, world, right? So what we are talking about is not the size of the context, right?

There is a lot of, uh, uh, innovation happening with KV cash and all of that, right? Mm-hmm. We are talking about the purity of the context, right?

So it's not about, obviously size matters as well, right? But now a lot of these LLMs are coming out with a lot of bigger, uh, context memory, but really it's, uh, it essentially boils down to how pure your context is, right? Because that's how the, the output is going to be determined How pure, Yeah.

Purity, well, not pure storage, but, um, alright, so this is, so what are we talking about, right? So at a system level, this is our platform. It interfaces with number of other capabilities, right?

It could be application devices, it is, uh, uh, IT and business tools. We work with a number of different data platforms, right? And we have, uh, da, uh, data, um, platforms in our bundled with the product.

Like we have, uh, uh, graph db, we have, uh, you know, and, um, um, OO open search and so on. But we also can work with customers external tools, right? That's another value proposition.

What we bring, we work with a number of different LLM models, right? And pretty much all the latest ones, right? Like GPD five, DO two is out.

Now we are already testing it, right? It is been out only three weeks or so, right? Uh, cloud, right?

We support, uh, uh, others as well. Okay? Then we interface with output tools, which are essentially ITSM tools, right?

They could be automation tools and so on and so forth, okay? And on the, the, the user interface to the platform is, uh, through two ways, right? So there is a studio, our own studio, not the Microsoft, so what we call it a co-pilot, right?

Which is mostly conversational queries, right? So you can ask conversational queries, you can test your hypothesis and then convert that conversation into an agent right there. The other user interface is what we call an agent studio, right?

So it allows you to build your own agent. So the platform, uh, out of the box, we provide about 50 agents or so, right? Across different categories.

So we have AI ops, observability, SecOps, bops, what have you, okay? And then the real power of the platform comes for partners and end users to build their own, own, um, own agents for bespoke use cases, right? And that's where now we are kind of, you know, broadening the use cases beyond IT ops into SecOps or bops and so on and so forth.

Yep. So before you go on, this is, uh, Jack Poller with Paradigm Technica. Yeah.

Um, on your, your architecture diagram there, you've got a couple of benefits listed. Faster deployment, MTDR reduction, alert noise reduction. Can you explain a little bit about what sort of, what are we talking about there?

In what context? Yeah, so, uh, in terms of, uh, the consolidation of the tool, right? So now you have, you know, individual domain specific tools, right?

Right. Like you have, uh, Dynatrace, right? You have, uh, item tools or a PM tools or what have you, right?

Or you have infrastructure tools. So we have the ability to now go directly, uh, go and talk to the devices ourselves, right? Okay.

So in some scenarios, you may, you may, over a period of time, you may see that, hey, uh, this platform can actually pro, uh, cater to those use cases. So, so those use cases would be monitoring your AI infrastructure? Oh, well, yes.

Yeah. It's not just AI infrastructure, it's actually the customer deployment, right? It's application stack.

The infrastructure stack stack, okay. Okay. Yeah.

Okay. Yeah. And so then the M-T-T-E-R, yeah.

So the next one is alert noise reduction, right? So that's the standard use case in the AIOps world, right? Okay.

You're getting a lot of alert fatigue and you're doing, correlating that, understanding the context, right? Then, uh, the other one is faster deployment. So the platform is, is what we call it a data driven platform, right?

So what that means is there's very little, it's a low code platform. So we go and we, we, uh, basically do data discovery, right? Uh, we understand the schema, right?

And then we create, uh, you know, our declarative templates, right? So that's another value proposition of the platform where you can, it's composable, right? So that's faster deployment.

And MTTR reduction is literally, uh, you know, one use case, which we'll talk about, right? Uh, where, uh, in a standard way it was, is it was taking a long time for them to get to a remediation, okay? Right?

That was a problem statement. Making sense so far? Yeah.

Yep. Josh, here with another follow-up question to that. Um, how much data ingest are you doing, right?

You're talking about interfacing with the data is a source and building a template around that, but are you also building a data lake underneath, like, or dependent on the platform, because right, all of my data sources are not gonna be equal, right? If I, I'm, if I'm assuming that the customer doesn't already have a data lake and they've gone through that bronze, silver, gold process, right? To, to purify things, yeah.

You're saying I can still interface directly with another product like ServiceNow or, or whatever that's up here like Splunk and not worry about a data lake? Or are you building a data lake? No.

So you would have a data platform. You would have a data platform, but a lot of these things, actually, the correlation and so on, it happens in the context engine. Mm-hmm.

Right? So that's where, but we do have persistent layer, right? You could have Splunk, you could have open search, uh, you could have min, right, what have you, and then ServiceNow and all those are the output layers, right?

Mm-hmm. Where you are basically updating a ticket, uh, or so on. Okay, that makes sense.

Yeah. Yeah. I got you.

Yeah. Yeah. So, uh, real quick, right?

Everything I'll allowed to quickly go through. Um, yeah. So, uh, again, uh, capability wise, right?

So what we say how we are different is basically it's a full stack platform, right? That means there are a lot of agent platforms who only focus on the agent layer, right? So we take care of all the way from the data to the agent layer, to the automation layer, right?

So that's what we mean by full, full stack, uh, purpose built, right? Because a lot of, uh, you know, there's a lot of hype around this. Frameworks like crew and auto gen and so on in the IT world, you're dealing with realtime data, right?

You're dealing with alerts, incidents, time series data. Many of those frameworks don't even support realtime data like crew AI doesn't support, right? So this is a platform which is build grounds up for this particular real time use case, okay?

And it's enterprise grade, right? So this is how he'll, um, Russia, when, when he walks, uh, over in the demo, you'll see this is our catalog. This is the first point of contact for an end customer, right?

Where you have this, uh, you know, 40 or 50 AI agents where you, you are basically, uh, you know, clicking them and demonstrating them. Okay? So I think this, and then a couple more slides will actually, uh, establish, right?

So this is, we are now kind of getting, uh, under the hood on what is our unique value proposition, right? So the middleware is really where, uh, you know, we differentiate against all the other, uh, uh, other platforms, right? Which are like first generation, if you will, right?

And it comprises of the context engine and the universal tooling. And again, as I said, the LLMs, they don't have state, right? So that's why the context becomes important here.

We maintain the state, right? And now when you have all this, you know, large number of, uh, data sets, uh, SQA rows coming to you, or, um, large data, um, large logs you coming to you, we keep, uh, it, we basically summarize it and keep a state, uh, in the context engine, right? So now, once you have that state, when you are having the MCP servers, uh, say server one talking to server two, it doesn't have to go all the way to the LLM for every transaction, right?

That state enables it to kind of converse it between the two, two different MCP servers. Anyway. So the three main features of the, of the platform is the agent tops model, right?

So this is where we actually are operationalize, right? The use cases where you are essentially able to bring in, uh, you know, uh, those, what we call scaffolding, uh, of, of the, uh, of the elements, right? Where your trust governance and, uh, observ and so on.

Then there is a middleware, which is where, you know, we are doing shared context intelligent caching. And the tooling, tooling is where we are able to dynamically go and talk to data sources, uh, using, uh, either MCP or raw, raw, all this, right? And then finally, this is all built on top of the data fabric, which allows you to actually connect to this 1700 plus, 1800 plus data sources, okay?

Uh, yeah, I, I'm not going to walk you through this, right? But essentially the capabilities we have, like say for example, how do you build trust with a stochastic model, right? So this is where, uh, you know, we have this, what we call prompt templates, right?

Which are, are basically give some examples to the LLMs that, hey, this is how you'll go about root cause analysis, right? This is how you would go about looking at the, uh, CBE, right? And then what we do is we use the LLM to choose those instructions, right?

That's what we call dynamic instructions. So that's kind of a trust part. Everything flows through with a persona, right?

So we have guard rails around that, that's very important. So, and level three engineer will see only certain tools and data sets, which are different than an in info, uh, IT manager, right? Who will see a different set of data sets and diff different tools, right?

Same thing with governance. Uh, there is finops model. So you can leverage look at, hey, what's your cost based on a project, right?

So you can create a sandbox and assign certain personas to it, right? Uh, security, right? So, uh, we have this concept of list agency, right?

So every agent should only get access to a data sets and tool set, which he's entitled for, okay? Reliability and performance observability, right? So again, this observability is very different than the infrastructure observability we are all used to, right?

So we are talking about observability at the agent layer, right? Mm-hmm. So, which is important, right?

And what we do is, uh, not just provide you, uh, all the audit locked trails, but also we have, uh, flows, flow maps, right? Mm-hmm. Which is in real time.

That means say here is a user prompt, which went to the MCP client, it went to the MCP server. This is what, what was the tool set which was accessed. Mm-hmm.

So once you're looking at it, it's very little room for hallucination. Yeah. Uh, this is how we actually show this in the demo itself, right?

So we operationalize it, like, uh, looking at the persona, looking at the tools and so on. Uh, this is actually a positioning slide. Some of you might find this useful.

So there are other players as well, right? And we acknowledge that. So there are observative players.

The traditional one, right? There are AI observatory players, like right? Are reason and so on.

Then there are orchestration frameworks. There are SRE vendors and that there is platform place. So Gartner has actually placed us both in the, in the platform, uh, uh, this as well as in the AI SRE Quad, right?

And that arrow indicates that some of these traditional observative players who lack, uh, agent, um, uh, framework on top of that, we are able to compliment that, right? And they have been our partners for a long time, right? Guys like, you know, uh, Splunk and, and, uh, so on and so forth, okay?

Mm-hmm. So any questions on this? On the positioning?

No. Yeah. Uh, again, so this is some examples of how we work.

Like we have Splunk agents, uh, we have I om agents, right? We have CRM agents. Okay?

Uh, so this is the agent to clear complementing the existing observability and item OM tool. Yeah. And so I actually do have a question.

Yeah. Um, could you kind of give us a, an abstracted description of how one of your customers is actually using this? Yeah, yeah.

So, exactly right. So, uh, I'll skip this. Let me get to this example, right?

So this is an example of en large e energy company, right? So, uh, you have an application and infrastructure stack, okay? Uh, uh, you have a number of different tools, right?

There is observatory tools, there is data tools, there are, uh, it om tools and so on and so forth, okay? We are basically bringing this in all okay? Through, uh, our, uh, our, that dynamic MCP tooling, right?

And this is where, uh, you know, the, the engine basically is able to curate all of that telemetry to provide it to the LLM, okay? Okay. For root cause analysis or SecOps and so on.

So, so This is basically an MCP run in for MQQT, Uh, well, M QT QT could be in the IOT scenario, right? We don't have your MQQT, right? These are more enterprise applications, okay?

Yeah. Yeah. But yeah, I mean, if there woulda been, that would be another endpoint.

So, So great ese Silverton Consulting. My, my question is, you know, a lot of these solutions servers, VMs, networking storage, GPUs, they're moving more and more to MCP server solutions themselves. So they're supplying a lot of the MCP endpoints for what your universal tooling is doing.

I, I understand from a legacy perspective, a lot of legacy solutions out there may never get to there, but, but, uh, from more current infrastructure perspective, they're all moving to that, in that direction. Yeah. The other question I have is, and, and so the LLMs and the agent tech workload flows are also moving towards more context, sophistication, management, compression, those sorts of things.

It's, it's, it's, it's, I guess my question really is do you have a, a, a, a solution that, that will, you think will last in this environment? Well, yeah. I mean, again, uh, that's a, there's a good, good point, right?

So again, there are, most of the enterprise solutions do have MCP, right? But there is also a large number of, uh, you know, a legacy or raw data particularly, right? Is the same thing with open telemetry, right?

We talk about open telemetry. There's 80% of the data sources still don't support it open telemetry, right? So that's the problem.

Certain, coming to the second part of it, as I said, that the caching, what the infrastructure caching we are talking about is, is different. That's good. Good announcement.

But it's really the purity of the, of the context what matters here, right? Which is what we are Focusing. It's the second time you mentioned that.

Yeah. Yeah, exactly. I mean, you'll hear that all the time.

By the way, I'm going to.