Building Enterprise Agentic AI for Claims Automation with Databricks

Summary

Suncorp deployed 16+ production agentic systems serving 3,000+ staff, covering use cases from simple claims summarization to complex coverage determination, fraud assessment, and human-in-the-loop escalations for workers compensation and other complex claim types.
The reference architecture uses Agent Bricks for supervised workflows, Lakebase for shared agent memory across orchestration platforms, and Unity AI Gateway for traffic management and compliance controls within the Databricks Data and AI platform.
End-to-end observability through MLflow combined with OpenTelemetry gives Suncorp production-grade monitoring and quality measurement that satisfies regulatory compliance requirements across Australia's insurance industry.

Building Enterprise Agentic AI for Claims Automation with Databricks

Claims settlement is one of the hardest workflows in insurance: multiple systems, multiple processes, and high regulatory requirements. Suncorp shares how they transformed this using agentic AI on Databricks. Instead of monolithic automation, they built a reference architecture based on data preparation, LLM-driven reasoning, multi-step agent coordination, and most critically, end-to-end observability and governance.
This talk covers the technical pillars: using Agent Bricks for supervised workflows, Lakebase for shared agent memory across orchestration platforms, Unity AI Gateway for traffic management and compliance controls, and MLflow plus OpenTelemetry for production observability. See how Suncorp deployed 16+ production agentic systems serving 3,000+ staff, scaled from simple summarization to complex decision agents covering coverage determination, fraud assessment, and human-in-the-loop escalations, all while maintaining regulatory compliance and measurable quality.

Chapters

FAQs

How does Suncorp use AI agents to automate insurance claims processing?

Suncorp built a multi-agent architecture on the Databricks Data and AI platform covering the full claims workflow — from fragmented intake through fraud assessment, coverage determination, and human-in-the-loop escalations. They deployed over 16 production agentic systems that now serve more than 3,000 staff, addressing a process where complex claims like workers compensation previously took 12–14 months to settle.

What is Databricks Lakebase and how is it used for AI agent memory?

Lakebase is a managed PostgreSQL capability within the Databricks Data and AI platform that provides a persistent, shared memory store for AI agents. Suncorp uses Lakebase so that agents orchestrated across different platforms can share state and context without losing continuity between workflow steps in the claims process.

What is Unity AI Gateway and why does it matter for insurance compliance?

Unity AI Gateway is a traffic management and compliance control layer within Databricks that governs how AI requests are routed and audited across an enterprise deployment. Suncorp uses it to ensure that all agent activity meets regulatory compliance requirements in the insurance industry while maintaining the operational flexibility to scale.

How does Suncorp achieve observability for production AI agents?

Suncorp built an observability platform using MLflow combined with OpenTelemetry to monitor their production agentic systems end to end. This gives the team the visibility needed to measure agent quality, detect issues, and demonstrate regulatory compliance across their 16+ deployed agentic systems serving the claims workflow.

Full transcript

[00:08] All right, I think we've got the quorum and thank you for joining for this session. I am Shivam. I'm one of the solutions architect at Databricks. I'm based out of Sydney, Australia and I'm working quite closely with my customers at Suncorp. Kranty. Everyone, I'm Kranty. I lead our AI engineering team at Suncorp, based in
[00:25] Australia again. Awesome. So, before we start with our slides, just a quick icebreaker. How many of you in the recent past have raised a claims through your insurance providers?
[00:41] Quite a few of us. A lot of us, right? And how was that experience? If you If you try to recollect the experience, and especially if you go through some of the complex claims processes, it becomes quite cumbersome and the the the journey is not really very friendly, as you could
[00:57] imagine when it involves a lot of sub processes, a lot of subsystems, and not just systems. I think there are a lot of people involved trying to make sure that we get past, let's say, lodging the claim, identifying fraud, and so many other steps. So, there are a couple of
[01:14] challenges that's there in this workflow, and in our session today, we are going to talk about how you can use Databricks, and especially what we are doing at Suncorp, to solve some of these challenges. So, for today's session, I think what we are going to talk about is pretty much around how you can settle your claims
[01:30] using agents, and how Databricks is playing a very important role across enterprise AI observability in the insurance industry.
[01:48] Awesome. So, a couple of friction points to talk about why it is technically so hard. So, if you look at the key, you know, the key imperatives when you have to settle your claim, there are a couple of things which makes it a very difficult process. Fragmented intake. Let's think about how
[02:04] do you submit your claim? It could be through these systems, it could be through via phone calls, or back in the time it used to be through post as well. The cycle time to go through this entire process to settle the claims is usually quite lengthy.
[02:21] Just to give you an example, think about workers compensation. Let's say me as a worker, if I got a workplace injury, just to get that through is always a very long process and it typically takes probably around 12 to 14 months to settle some of those claims.
[02:38] The data and systems that you kind of touch internally, that's not going to be just one system. You are going to touch upon a couple of systems. So, what that means for you is you've got siloed databases, you've got siloed systems and there are no centralized governance
[02:56] across those systems. So, that kind of kind of makes it harder for you to understand, "Hey, what's going well and what's not going well?" Um let's talk about how data breaks can actually solve some of these problems. So, I'm going to talk about agentic
[03:11] claims automation in this set. Through agentic claims automation, what we are talking about is four key processes that you will run in any enterprise ecosystem. The first one is data preparation. So, by data preparation, we what we mean
[03:28] is through variety of data sources, a variety of channels, you want to ingest the data in near real time and you want a technology that can support that. For those who have attended the keynote today, you would heard you would have
[03:44] heard about Lake Flow. And one of our offerings within the Lake Flow suite is called Lake Flow Connect, which kind of helps us with data ingestion from different source systems and then it is facilitated by declarative pipelines which kind of makes it easier for the
[04:00] data practitioners to just focus on writing the business logic, thinking about data quality, and not worry about maintenance and management of pipelines. So, declarative pipeline is a very important um component to this.
[04:15] Now, while we are talking about ETL and technology to facilitate the data movement, something which is very important is the sanity or uh the quality of the data. We have to make sure that we are by the time it reaches business that data has
[04:32] been standardized, it has been confirmed, and there is a model which kind of brings business semantics into it. Within our world of Databricks, what we call as gold layer is basically a logical schema convention which allows you to store gold standard data
[04:49] uh for your business. I think we'll stick to this one. The next The next step is AI agents. So, this is kind of something which we are introducing recently and in the agent era, I don't want to miss out. So, I get a formal if I don't have AI in my
[05:05] reference architectures for Suncorp. So, Kranti hates me if I don't have any AI mentioned in my slides. So, through AI agents, what we want to do is if there are couple of steps which are required to be done before you settle those claims, you want to use agent bricks to
[05:20] solve some of those steps before beforehand. So, typically in a life cycle, what we are talking about is some deterministic steps and some non-deterministic steps. Just to give you an example, a deterministic step step could be a rule engine which will say, "Hey,
[05:37] is the claim settlement value less than 5K? Maybe avoid some of the you know, mundane processes, make it a lot simpler to settle those claims." Or the complex ones could be, let's say if your home is damaged or if there is a flooding that has happened and your your
[05:53] sofa and the furnitures are damaged, that's when you want something more complex which can process images, which can work with your data which is more unstructured or volatile in nature and bring some value out of it. Now, in order to achieve these steps,
[06:09] what we are basically saying is, "Hey, you should we recommend using Agent Bricks that gives you a suite of services which says, "Let's build a multi-step workflow and in that workflow, make sure that you've got a sequence of steps defined
[06:25] that will allow you to get create a supervisor agent." A supervisor agent can basically say, "Hey, for deterministic rules, just run an if else. For non-deterministic rules, make sure we run this through a complex machine
[06:40] learning process or probably in these days more around the world of retrieval augmented and generation steps. Now, while building this business logic is very important, something which I'm going to touch upon in my future slides is Unity AI Gateway.
[06:57] Ali in our keynote today emphasized a lot around governance, especially when it comes to AI. I think what we are looking at at the moment is most of the insurers, and I know for sure but Suncorp would like to have hundreds and thousands of agents spun up over the next 12 to 24 months.
[07:15] Now, having governance is very critical, having right guardrails is very essential. Unity AI Gateway is going to be the offering from our end to solve some of the AI governance challenges.
[07:33] That's not enough. We also know for sure that you will have ecosystem in we will have a new ecosystem different vendors who have agentic capabilities and in order to facilitate or manage those agents, you need a system which will allow them to speak to each other.
[07:49] Now, in order to speak with each other, let's say if you have couple of agents and they have to speak amongst each other, you want a memory that they can share. And in order to share the memory, we're talking about our service called Lakehouse, which again is our OLTP kind of offering.
[08:05] Uh it is managed Postgres, uh open source, and we are very much invested to make sure that we isolate compute with storage, and Lakehouse becomes very critical for any agent use cases that's running in your world. It will give you the ability to say,
[08:20] "Hey, if you want to see what was the result of agent one and use that as a reference to take a decision in your agent two." Lakehouse would be the place where you kind of have this communication structure. The last piece, and I think this is again one of the most important things.
[08:37] I'm This is the last one I think, but it is probably P0 for every enterprise that I'm speaking to. It is observability and governance. You will have hundreds and thousands of agents. But how do you ensure that you have a history of information about what they
[08:53] have done, how they have responded, and what was the accuracy behind it? How can you proactively stop those agents from sending or sharing harmful content? That becomes very critical in your world. So, observability I would
[09:08] say through uh open telemetry, MLflow 3 Eval, and also dashboarding and apps would kind of help you to build solution that says, "Hey, I have a single stop shop where I can view how my agents are performing, and I can take any necessary
[09:25] actions to stop or improve my agents." So, let's double click on observability on Databricks. So, as you can see, I think there are key there are three important pillars
[09:40] that I'm going to speak about today. I've got Open Telemetry Tracing, MLflow 3 Evaluation, and Unity AI Gateway. Now, let's talk about Open Telemetry. In our keynote today, you would have heard about Zero Bus. And Zero Bus is
[09:56] all about, "Hey, how can we give our customers an ability to connect to APIs and then manage to bring those agentic traces onto Databricks?" So, we want to make sure that any information or any insights that's generated by your
[10:12] agents, it is ingested or acquired onto Databricks, and that happens through Open Telemetry. It's a open-source protocol, widely adopted by the industry, and think of it very similar to how you've got, let's say, a restful interface, an HTTP API. And so, I think
[10:29] OTEL is kind of a standard in the AI or agentic agentic era. MLflow 3 Evaluation. So, ML We we are the creators of MLflow. MLflow has been a part of our product
[10:45] for like 10, 15 years now. MLflow 3 Eval is kind of an extension which allows you or which brings observability onto Databricks. It gives you this surface area where you can continuously monitor the responses
[11:00] generated, have a reinforcement learning kind of a algorithm in place which will let you let you get in and say, "Hey, these are the responses. I think that it needs certain improvements. There are certain traces which we want to, you
[11:15] know, rerun, retest, and then fix my model." MLflow 3 will be the surface area for that. The last one, again, is um Unity AI Gateway. With AI Gateway, what you're basically saying is across your platform or across
[11:33] your ecosystem, you need one control plane for all the agents that are built. As I mentioned, with hundreds and thousands of agents playing an important role, what you probably need is a governance control plane which says, "Hey, let me set budgets. Let me set
[11:51] budget on XYZ developer to use Claude code." I know that for over the next 12 months, every organization will end up using one of the AI tools to help or speed up your use cases and the practitioners would be using them to
[12:07] build some of the next-gen initiatives. With Unity AI Gateway, you can make sure that you are governing what they are doing. You can do something called as rate limiting, which will allow you to understand, you know, how and what to be processed over the time.
[12:22] And last but not the least is budgets. So, you can set the budget and say, "For every developer, as an example, let's allow them to spend $100 a month or $200 a month." So, that's something you kind of want more centralized and governed.
[12:37] So, So, this is just a small GIF that I've kind of made for our session. It talks about the workflow and how it looks like. So, for anything that you are doing, you want guardrails in there. You want PII protection. You want to make sure that you filter out unsafe
[12:53] data. You want to score the quality um of the outputs that are generated. And all of this is facilitated through, as I said, Unity Catalog, Unity AI Gateway, MLflow 3 Eval, and OpenTelemetry.
[13:15] So, let's double click on Unity Catalog. So, what is I'm so sorry. You Let's double click on Unity AI Gateway. What is Unity AI Gateway? It allows you to enforce governance across your estate. It will give you a couple of capabilities. Logging, so any sort of traces or any sort of information
[13:31] generated by your agents, it will be logged and managed centrally. Compliance, there are certain industry standard compliance that would be required. There will be certain guardrails that will be required to make sure that you govern your your ecosystem
[13:46] in the right way. So, we want you to define that and in order to define that, you got to use Unity AI Gateway. Cost controls, I'm not going to click on it, but this is all about, "Hey, how we can limit the rate? How we can make sure we set budgets based on users or based
[14:02] on different uh you know processes that you have in your system?" And traffic management. Basically, what traffic management is saying is, let's say if you've got two versions of your models or two versions of your agents and you want to route traffic to test and approve one of the
[14:18] version of those agents, you can use AI Gateway. You can say, "Hey, all the 80% of your production data, let's route it through version one because it looks settled, but the remaining 20% let's route it through version two because we
[14:34] are testing and we are evolving that version over a period of time." So, consider your version two to be a challenger model, which will probably get more traffic over the long period of time. The last one is governance. I think Unity Catalog was a game-changer for us
[14:52] a couple of years back. I remember our CTO and co-founder Matei Zaharia, he was in Australia announcing Unity Catalog in 2022 and he had this vision that we want to make sure that all the controls for AI and data, of course, sits within Unity Catalog. So, anything to do with
[15:10] Unity AI Gateway, it is kind of going to complement Unity Catalog behind the scenes. Now, the the other most important concept that we have is model serving. So, I'm So, for those who are not familiar with model serving, model serving is basically one of our services
[15:27] which will allow you to host your models, host your agents, or host your let's say LLMs. Now, typically, we've got a couple of different category of models that can be hosted. Custom models, which you have built and you want to host it, you want to serve it in real time, or you want to host it
[15:44] for a batch use cases, you can use model serving. Um the popular first-party models, chat GPT, sorry, GPT, Claude, um Opus, a lot of them can still again be hosted through model serving.
[15:59] When we start talking about coding agents and third-party services, I think um if you heard from the keynote, we have announced something called Omni agent, which is an open-source framework, which will kind of act as a harness between your coding agents, your
[16:15] MCP tools, and model serving in a lot of way. It kind of gives you an ecosystem which will let you say, "Hey, if you are using both Codex and Claude code to build some of your models, you would need Omni agent, let's say, back by model serving for hosting those models
[16:31] to get some of the stuff across the line." So, in the long run, I think you will hear a lot about how we can have Unity AI Gateway, Omni agents, and Genie Ops play a role in this ecosystem.
[16:50] I'll double click on Lakebase, which was a lot about share shared memory. So, what is Lakebase? Um prob- probably a year or a year and a half back, we acquired a company called uh Neon, and we brought in the capability to have managed PostgreSQL within Databricks.
[17:05] The intention was to bring the best of both worlds. So, we had Lakehouse, which was already serving the purpose of facilitating use cases around warehousing and machine learning use cases. The other part which was missing for us was OLTP kind of systems. So, we
[17:20] realized that you need systems wherein you have um Lake base that will allow your agents to speak to each other or have a real-time transactions um happening across the workflow. Or let's say if you've got your upstream systems, when you are logging into an
[17:37] insurance portal and you are saving any information or raising a claim request, Lake base can play a very big role in there as well. So, what Lake base can do is it can be your front-end system which can act as a online database for your real-time use cases.
[17:53] So, there are three typical kind of uh you know, items that you can store within Lake base. Those are categorized under short-term memory, long-term memory, and shared memory. So, think of short-term memory as sessions and conversations happening for a workflow.
[18:08] A long-term memory could be cross-session knowledge across, you know, different entities or different domains within the organization. And shared memory is basically multi-agent coordination across a workflow using a persistent state.
[18:28] Now, we've got the agents, we've got the ecosystem that talks about what and how things happen behind the scenes. For any executive to understand and facilitate decisioning, they need a front-end experience to understand what to do, what is going on,
[18:44] what is the problem, or you know, how is it helping us. Just to measure those KPIs, you need an app that can allow you to monitor what's happening, what are the agents doing, what is the ROI associated with it, and
[19:01] so on. So, in order to build an app, you basically need five steps. You kind of author the agent, configure Unity AI Gateway, you deploy deploy the app which will have a front-end uh business logic, and then monitor what's going on in that
[19:16] ecosystem. So, this workflow will say, "Hey, let my executives have a view of how my claim settlement workflows are performing." They can actually look at it and say, "Oh my god, I think we are losing a lot of money. We are probably settling a lot more claims with agents
[19:31] than that we are what we were doing through a manual process." So, there's something which they'd like to investigate and explore around. So, that's pretty much it from my end. I'll pass it over to Kranti, and he'll talk you through his journey with
[19:46] Databricks at Suncorp. Thanks, Shivam. By raise of hands, how many of you work in insurance? Awesome, quite a few. How many of you have got agents in production that are well governed? Nice, see a few hands. Okay, great. So,
[20:03] I I'm Kranti, I lead our AI engineering team at Suncorp. We are one of the largest insurance companies in Australia. Um so, we offer different types of products through our subsidiaries, AAMI, GIO, Apingle, all of it all of whom have become household names in Australia.
[20:19] Um and we do want to provide exceptional customer service, and we want to make insurance affordable in Australia. And we realized that the best way to do this is, you know, having these two key pillars of platform modernization and
[20:34] AI-based decision-making embedded across all our business units. Um at Suncorp, we've been doing a lot of data science work for, you know, a couple of decades now, but the last 3 years has all been around generative AI. Um we took some baby steps as well, but,
[20:51] you know, we've we've come a long way since then. And the way we see this at Suncorp is, you know, AI obviously it's the whole transformation is based on four pillars. So, the first is having an enterprise strategy, which is top-driven. It's not
[21:07] something, you know, that that's done in a silo. It is top-driven. It is part of board or CXO agenda. Um and, you know, well understood, all the metrics, everything properly tracked and well governed. The next one is people.
[21:24] You know, AI transformation is not like implementing a new platform or a new technology. So, this is touching the lives of people, people who are making decisions for our customers on a daily basis. So, we we need to uplift our people capability, not just empower them
[21:39] with fancy tools like co-pilots and others, but also teach them how they can make use of it so that they can use them as part of their day-to-day job to, you know, boost productivity and to help our customers. And risk is a very important area. Now,
[21:56] you always go back to this analogy. Um we see AI transformation as driving a race car. Now, risk and controls are not like speed bumps. They are more like brakes. You can go as fast as you want on the track knowing that the brakes are there
[22:12] to provide that control. Um and and that is what we have done. So, we realized the importance of risk, and we've developed a controls library of about 100 different controls across different type of AI applications, and, you know, for different types of risk.
[22:28] And lastly, technology. Um technology foundations, last but not the least. So, we've developed internally what we call Sanjivini. I'm proudly flaunting the logo on my t-shirt here. Uh Sanjivini is not like, you know, chatbot or a model like ChatGPT, but it is our internal AI
[22:45] engine that powers all the generative AI applications that we have at SunCorp. So, it has got all the standard modules, frameworks, everything that we need from a governance standpoint, all the connections that basically help all our teams develop using what we have so they
[23:02] can develop faster and well-governed and monitored applications. Now, over the obviously we've taken baby steps. Um one of the first use cases, so we we we've got about 18 GenAI applications in production today. And one of the first
[23:19] ones that we did was, you know, simply using LLMs to summarize claims. Now, many of you who work in insurance, you would you would know that summarizing a claim is not like give the entire claim history and ask an LLM to summarize. You need a lot of You need to have a lot of
[23:36] heuristics in there. Um we have customers calling us. They call the claims handlers and they would be like, "Hey, what's happening on this claim? Can you give me an update?" And our claims handlers may be handling 50, 60 claims and they won't remember who that particular claim belongs to.
[23:51] So, it takes time to go through the claim. So, we built this application which we call single view of claims, which would give a nice summary of where the claim is at, a proper timeline showing all the actions and what the next actions are and what to say to the customer. So, that saves a lot of time.
[24:07] It improves customer experience as well. The second one that we deployed in production was a chatbot, which again was targeted to serve our call center consultants. So, they don't have to go through hundreds of knowledge articles to to answer customers questions. If a
[24:23] customer calls and asks them a question, whether that's simple or complex, they'll put it in the chatbot. It provides a well-grounded response in real time within within 3-4 seconds. We've also progressed more complex agents, agents that determine, you know,
[24:39] whether a claim should be covered or not. You know, there could be a claim which is something like, um there was a storm last night and a tree fell on the retaining wall damaging a pipe that's running next to the wall. So, the water seeped into a
[24:55] garage and damaged all the carpets and rugs in in the garage. And that is a contents insurance claim. And as you can imagine, so there's a storm, there's a tree which fell, and there's water leakage. Now, if you're looking at
[25:10] coverage, you need to identify what the loss cause is for that for that event. And based on that, you determine coverage. So, there could be any loss cause here. It could be the storm, it could be water leakage, or it could be, you know, impact of the tree falling.
[25:25] So, how exactly do we determine? So, it's not an easy problem. Our claims handlers spend a lot of time, and it could be complex. So, we have developed a workflow with agents in there which can do all these jobs with 99% accuracy.
[25:41] Since then, we've deployed multiple other other use cases as well into production, targeting complaints handling, the way we monitor risk risk here. You know, across a variety of variety of cases. And for for this work, um and and these
[25:57] are these are being used by about over 3,000 staff at Suncorp, and these are over and above the co-pilots or the Robo and all the other agents that come out of platforms. Um we also, you know, for all our troubles, we received the Databricks
[26:12] AI Innovation Award for Asia Pacific last year, and um Australian Financial Review Awards for AI Safety and um AI Builder Awards as well. Now, where we are right now is we are working through automating our
[26:28] claims processes using agents. Now, this is where we've been where everybody naturally wants to progress. Claims process are inherently very complex. But, at a high level, they are workflows. There are some set stages. You know, you could have
[26:44] an intake to start with. They could be, you know, we we triage we assess the claim, and then we fulfill taking lots of actions, and then we ultimately settle. So, that workflow sort of remains the same. But, within that, there could be several tasks. So, there could be So, we may be, you know, understanding whether
[27:01] all the information is there or not. We may determine coverage. We may assess fraud. We may be, you know, assigning the claim to different builders or assessing what builders produce in their assessment reports. We might be working with other finance companies or mortgage companies to work out different
[27:17] arrangements. We'll be dealing with customers who may have some sort of financial difficulties. So, all these are managed on the claim. And all these tasks these tasks themselves could be done by agents or these tasks themselves could be workflows with certain rules
[27:33] following one after the other or they could be handled by a system of agents. So, at a high level, we're talking about sort of like a BPM and workflow. Within that, multiple tasks and each task could itself be done by an agent or system of agents or workflows
[27:49] themselves. And the way the system responds is it responds to all the events that come through. So, there could be, you know, some sort of system-generated events. You know, things like um, you know, it's been 7 days since we gave the last update. So, we need to do it. Or, you
[28:07] know, there could be some sort of automated automated events that our systems generate. Or, there could be some emails or documents coming from third parties. You know, a doctor who is assessing, you know, the injured person might be sending an assessment report based on which we need to determine, you
[28:22] know, how many you know, what wages we need to pay to the injured person who is not able to get back to work. Or, there could be interactions directly with the customers. They might be chatting with us on the app or they might be sending us emails, asking us questions. So, any of these events could
[28:38] trigger these processes or could trigger these agents. Now, given that we have several agents in here we want these agents to be well governed. And that governance is being done through Unity Catalog, obviously, where for every agent, the data that
[28:55] goes through this agent is properly monitored. Um so, we we got like proper data permissions, the way we handle personal information or sensitive data, that is all well tackled tackled through Unity Catalog. At the same time, there may be lots of
[29:10] escalations in the process. For example, you know, you may the agent may decide we need to reject this claim because it doesn't satisfy certain criteria. But, we do not want the agent to send that email to the customer saying, "Hey, your claim is rejected." Because that could be, you know, sensitive detail in
[29:26] there. So, we do want a human gate there where all the rejected claims may have to go through a mandatory human review. So, the way we design these human in the loop escal- all these escalations and have humans work alongside agents, so
[29:41] that is very important. And obviously, observability is really really cri- uh key here. Um we may have a very fancy system, but if we do not have control over the risk, if we are not able to observe whether our controls are working or not,
[29:58] it it counts to nothing. So, there could be several issues in the system, and things may be broken, or there may be some sort of attack on these agents by, you know, external parties. So, how exactly are we going to monitor this? That is done through a central observability platform that we've built
[30:13] on Databricks. And the way everything comes together is by bringing all this data into lake base and lake house. So, we've got these agents, they might be running on different platforms. As you can see, there could be an orchestration
[30:28] platform, which is orchestrating, you know, for the business users. There could be agents developed on multiple platforms as well. So, in in insurance companies, especially, you'll have multiple platforms that support your processes. And you may feel that it's important to have certain
[30:43] operations performed on a different platform. But when you try to automate, you're bringing all of those in. So, the way information is shared between all the workflows and agents across all these platforms, that shared memory is very important. And Lake Base as the as
[31:00] the database here, you know, provides that shared memory between all the agents and workflows. And of course, from an observability point of view, bringing all those traces and telemetry into the lake also we can have proper observability is super important. So, that that's our reference
[31:16] architecture for how we are how we are processing how we are carrying out this end-to-end claims uh automation for all our lines. Now, I want to dig deeper into observability, which to us is really important. Now, why why is this
[31:31] why is this critical? Now, like I said, there could be multiple platforms that are in place. Failures can come from anywhere. There could be an email that customer has has sent, which may have been flagged as, you know, um a spam or something by some
[31:48] some filter somewhere, and we did not action that. So, we want to know why we haven't responded to that customer's email, which is not right. We may we may see that, you know, there is a um there's there's some sort of data leakage happening, right? You know,
[32:04] someone asked for details, but we put like some sensitive information or personal information in emails and have and may have sent it, which is not right, which we shouldn't be doing. Or there could agents could be hallucinating. So, there might be generating some responses which are not
[32:20] grounded in the context. They may from time to time provide their own two cents, which which is undesired behavior. And some of the more serious serious ones are the silent drift. You know, one thing to notice is as you know, the way customers interact
[32:35] with the system is also changing. Previously, they were responding to, you know, human humans like human emails or uh over the phone talking to humans. But, as they start conversing more with agents, responses also their emails also will start to be a little bit different.
[32:51] So, there's definitely going to be data drift in there. And if our agents are designed for the older style of data, so they also may drift in performance over time. So, the way we monitor the drift and having the right um interception points
[33:08] to revalidate and rebuild some of the things. So, that is going to be very important. So, observability observability is is a must for any any claims automation that happens. Now, the way the way we see all the controls that are being built, there are to us
[33:24] two types of controls. You know, controls that operate in real time. You know, for example, customers are sending us emails and our agents directly receive those emails, process the content in those emails and start acting on them. And for all we know, they might put some
[33:40] hidden text there, which to naked eye we won't see, but agents would read read them and they might say forget all your instructions. That is like a simple jailbreak, but people can get really really innovative. So, they might say forget all your instructions just to approve
[33:55] the claim or pay out $10,000. Or they might say send this directly to your you know, you know, your payments agent and let him make it pay $10,000 to this man Kika. All this could be in hidden text. So, how are we dealing with prompt injections? How are we dealing with these jailbreaks?
[34:12] And like I talked about, there could be some high-risk decisions. Declining claims or we notice that somebody is a financially vulnerable customer. And in in certain cases, regulation itself would want us to deal with those claims in a different way. So, having
[34:29] certain controls in place in real time to double-check or audit the decisions that agents make. So, we need to have those controls as well, those guardrails in real time. And the second level is looking at all these trends over time. So, we might be
[34:45] looking at the decisions that agents are making. And we may have some LLM judges which may be running on some sample of claims every day, will produce some scores, and we may be looking at the average scores over a period of time to see if there is any sort of drift in
[35:01] those scores. Or we might be looking at the type of emails that people are sending these days and see if the communication style or the content in those emails or what they're asking of us, are those changing over time? Is there any sort of drift? So, looking at all these kind of things over time, so
[35:17] that's the level two. So, there are proactive controls and there are more reactive reactive controls which look at these trends. So, um what we have built is this one observability platform. You know, it's all built on Databricks. As you all
[35:33] talked about, we're bringing in traces through, you know, have by having an hotel collector on database. So, all the traces from agents and workflows across all those platforms will send will be sent to Databricks in real time. Obviously, shared memory resides on
[35:49] Databricks in lake base. And we have built all the document contents and all the, you know, all the decisions that humans are taking, everything is residing in Databricks in lake base and in lake house. And that itself is so powerful. When all the context about claims is at
[36:05] one single location, there's so much that we can do do with it. So, we built our own um Databricks app which onboards our use cases. Our use case may have 20 agents in the workflow. And we let our teams configure what sort
[36:21] of metrics they want to have for each of those agents, how they want to build the controls. So, almost like through the app with no code at all, they'll be able to configure them, set thresholds, and send alerts when when required. And we have built an AI BI dashboard
[36:36] which surfaces all the results, you know, which kind of shows how the program is running, how a use case is running, how an agent is running with at different levels of detail. And of course, you know, a chatbot. You know, who who would want to investigate and look at all the data if
[36:52] something something goes wrong or if they want to find out. A chatbot could be the first first level there, where they would ask questions, and they would get the answers and all the analysis in real time. And I want to quickly talk about that
[37:08] you know, the the same platform that we have built that answers questions for different personas. We may have senior leadership and executives who are interested in how the whole program is running. So, there are certain metrics that they want, which you know, govern through Unity catalog,
[37:23] so they would be able to get those insights through the through the dashboard. And of course, our safety team want to see whether the controls are effective or not. Business users want to see why a certain decision was made on the claim. And and engineers want want the able So, they
[37:40] want to see pretty much everything, but at the same time, they also want to debug if and something goes wrong, and it should not take very long. So, we built a dashboard and added Genie code to that so that it can provide insights at different levels based on who is asking the question.
[37:56] And as we are progressing, the natural next steps are you know, towards sort of using using agents themselves to build these processes end to end, and even have some sort of production monitoring in place. Like, we were going to build something ourselves, but we learned in
[38:12] the keynote this morning that there's going to be Genie zero ops, which is exciting, which will which can monitor these production agents and provide and feedback and, you know, correct them if required. And all of this is done to provide that sort of exceptional service to our customers so that
[38:28] you know, premiums can be more affordable to them. That's all. I'll pause. We've got a minute and a half for any questions. Thank you everyone.

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