Hi. Good morning everyone. I'm Bala pna. Today I'm going to talk about how Agent TK is transforming enterprise integration from being reactive to resilient. Most modern enterprises confront significant challenges in maintaining reliable integrations across increasingly complex application ecosystems. As organizations adopt specialized software solutions, the dependability of these interconnections. Directly impacts business continuity and operational excellence. This presentation explores how care is transforming the enterprise application integration and EDA frameworks through site reliability engineering principles. The result is a self-healing, highly observable integration systems that substantially improve the key reliability, metrics, and operational performance. This is the key for any business success. Before we dive deep into the agent TKA framework, I would like to emphasize the integration challenges that these enterprises face in today's landscape. There are three main things that I would like to highlight on first scale requirements, the need to manage growing, number of integration points. Yes. every day there is a new language model. Every day, there are hundreds of new apps being built. And these are required for the business to stay relevant. And if these are integrated into the ecosystem, then the integration team needs to adapt itself and scale itself to integrate with these new head systems. Second is the system complexity, the in intricacy of, integrating complex heterogeneous and diverse software ecosystem. With this growing challenge of more and more apps being built, each enterprises has hundreds and thousands of applications, interconnected for various reasons. So these complex ecosystem is a real challenge for integration teams. And third is the time sensitivity. Any failure in integration, and if it is always connected to any business critical time, critical interfaces. It is going to cause a huge financial loss for the business. So the business will be always be on top to fix these integration, failures. So it is always time critical to address these integration, challenges. So these are the three main challenges the integration team faces in today's enterprises. This is where I would like to propose, the agent TK framework to resolve these integration challenges. Let us get into the Agent K framework, specialist agents, collaborative intelligence, self-healing systems, and continuous optimization. These four components weave together to make the fabric of the agent K. In integration specialist agents. We have task specific problem specific agents, which takes care of. Resolving a specific problem and modeled in such a way that they don't, overlap with the other agents. when you put together all these agents in a workflow, it needs to be overseen by an orchestrator in aa orchestrator decides. what agents needs to be involved in this specific workflow and they decide and create that workflow using a workflow agent. And once that is done, if there are any issues in this whole business workflow, we just deduct it and resolve those errors without any human intervention using the self-healing system or the remediation, plan that these orchestrator have. EA can Hal need. So we need to put a self-healing mechanism to ensure and monitor and detect any issues. The whole business workflow, can be optimized by learning new designs, by learning, the pattern and understanding, the business workflow that is suitable for our business and optimized in a. Best possible way by adhering to the rules and AI governance. Let's understand how these agent aea, correlates with the SRE model. This slide shows the pillars of the site, reliability, engineering model, automation, service level, objectives, incident management. Observability capacity planning, shared responsibility, which all enhance the service reliability. This slide resonates with the agent TK framework where we do automation. We ensure that the service level duties are measured and addressed. the incidents are automatically handled. We have an observable, highly observable case where we monitor and the capacity is planned. And forecasted and the responsibilities naturally shatter. Let us get into the technical architecture of the agent Dke and understand better let us get into the technical architecture and framework of the A Agents in EEA and EDA piece. This diagram shows the business flow and the agent flow. The business flow has, the external users communicating to the B2B software, which internally transforms the data from the external users, like trading partners, customer suppliers, or government agencies and banks, into the, format that the internal application ERP solutions CRM solutions require, based on their own data architecture or the schema. And that is consumed by those applications and applies the business logic to. Or complete the O2 C or P two P or inventory management or any kind of, application level business logic are applied and the corresponding acknowledgements or the invoices or the payments or anything is being rerouted back to the same, partner in the current framework. the reports are generated from these application layer for the management and stakeholders to ensure, the forecast is good. the baseline of the cloud company is doing good. All these are happening in today's world. Over and top of this business flow, we build an agent workflow. The customer service agent is the front end of this whole agent workflow, which can handle any queries or ments from both internal and external users. The customer service agent takes these requirements, splits it into individual task, and sends it to the AI orchestrator. The AI orchestrator consumes these tasks and, refines it and send it to the specialist agents. And each specialization agent completes the task by connecting to the corresponding application layer and ensuring the business logic is implied. And, completing the whole any, business processes. This whole, architecture is governed by the governance layer. The governance layer has it, security, data security, yay. Governance and policies. All those are. based on the corresponding market that this particular business is delivering it to, every data which is being recorded in this business process is being stored in the data lake that is then being refined and again stored into a training data which trains these language models and AI agents. This way, we complete the whole framework and the continuous, optimization. continuous, an autonomy and, the, highly efficient, integration model is being built using this agent, a framework. Let's understand its, users and other functionalities. In the next slide line, let us understand the various specialist agents that are. Defined in this process. There could be like several hundreds of specialist agents that we can build. one is the validation agent that ensures the data integrity and SMA schema compliance in case of, formatted data. It could be an idoc, it could be edifact, it could be e an CX tool or any particular. Format, that the particular business use, it could be HEPA standard or any other standards. All those are insured in this validation by this validation agent. And, translation agents. Translation agents take care of transforming the data from one format to the other format. The third is the monitoring and detection agent. It identifies the anomalies and. potential failures in different, cases and ensure the Y agent doesn't hallucinate or any other process has any bug. And those bugs are remediated orchestration agents. These are agents which takes care of building this whole workflow of, collaborative, structure that multiple agents can work together. Connector agents are. To establish a new connection to your new application, or a new a system or a new, enterprise to establish all these connections. A connector agent can consume whatever the requirement is, and, generate the corresponding firewall, changes, requests, and all those, internal requirements that are necessary for completing the connection security agents. Security agents ensure the security of all the data security and, user security by ensuring, that it is all the data is either encrypted or decrypted in the right format. And, similar to, all the cloud native infrastructure where the IAM module takes care of the, user, security, even here, the specialist agents takes care of. Connecting, user security and data security. Similarly, we can build any number of AI specialized agents, and these AI agents ensure, to complete a specific task or resolve a specific problem. This is a pictor representation of how agentic workflow in E-A-A-D-A integration happens. First, the rating partner requirements are submitted. to the customer service agent. The customer service agents, in its own natural language processing, redefines it and marks down as various steps and send it to the orchestrator. The, a orchestrator then takes those, whole checklist and redefines them. As modular components that will be assigned to the specialist agent. From there, if there are any EEA EDA requirements, the validation agents ensures that the data or the format that is required or specified in the requirements, follow a specific protocol or specific validation. From there, there is a technical processing phase where it evaluates the best possible optimal. set up strategy. The whole, process is being then implemented, using the corresponding application and communicated, to the database for ensuring that the end-to-end business process flow is completed. This process flow is being validated and quality assured by the qualities assuring agent, and the further data is being used for training the AA agents. This is a simple workflow that explains the EDA integration post implementing agents in the integration space. Agent TK implementation in enterprises do come up with its own challenges, data privacy, concern being the primary. One of them organizations are really worried about the sensitive data exposure to the AA processing. Our approach in this architecture uses federated learning techniques that allows models to improve without raw data leaving secure environments, and we also ensure specialized encryption techniques are being followed throughout the business process. This maintains the compliance and regulations like global data privacy regulations and hipaa. The second is legacy system integration issues. Most legacy system doesn't even have an a PE capability and there are many limited connectivity, that are available for these agents to connect. So ma making these agents work with these legacy systems is one issue, and these two are not the primary issues alone. There could be like a cultural resistance, there could be also issues with, other governance issues. Our approach is not a big bandwidth method. Let us go with a phased manner where you decide and define only a specific agent where, the organization is comfortable with, and you expose the only those data that are not as sensitive, or not sensitive for this particular agent, and see how these agents, explore and provide business value. I. Based on this approach, we can definitely, keep scaling these agents for different, applications and on a phase manner we should be able to achieve the highest autonomy levels. These, agents also enhance the observability framework, which is a key pillar of SRE model. Comprehensive tracing, business aligned, service level objectives and predictive alerting. These are all required for a successful business to be relevant and successful in the business. The end to end visibility in every transaction is important for ma various stakeholders, so that has to be inbuilt in this whole agent process and business aligned. service level objectives that directly reflect business impact rather than technical metrics, bridging the gap between IT performance and business outcomes. A driven predictive alerting based on understanding the data and business flow and completely training the previous transactions, help and enhance the observability framework. Another feature of this whole, agent TK is self-healing capabilities. First is the anomaly detection where whenever there is an, hallucination in the GT K or if there is an issue with the business process flow, this whole infrastructure takes care of identifying those anomalies and deduct it, automatically. And then identifies what caused these anomalies by root cast analysis. And that provides a list of checklists that needs to be done for remediation and that is handled in an automatic fashion. And this whole process is logged in our knowledge base so that the next, agent or any other. Business process doesn't face the same issue again, and the remediation step can be implemented wherever is required. This health healing capability enhances this whole integration process, to your new level of SRE. This chart shows post implementation of Agent TK in different sectors in our evaluation metrics and. It shows that healthcare has 87% financial services has 94%, manufacturing has 79%. Retail has 82%. Logistic has 91% improvement. this helps patient care efficient processing time, reducing the waste, improving the sales, improving and optimizing the supply chain in various sectors. This definitely shows that implementing the agent TK in any sector is a boom. These are the key metrics that are recorded post the implementation of Agent K in application integration, 92% reduction in MTTR 78% fewer incidents and 320% throughput increase, and 99.99% system uptime. These are impressive numbers and this will definitely drive business to implement Agent K in enterprise integration. All these values, definitely impact the, business baseline and make the business more competitive. These metrics definitely prove a point that agent TK is the future of integration and a business will reap benefit out of it. If you have any further questions regarding implementation of these agent TKA in enterprise integration, you can always reach me at my email. Id thank you for attending this session. Bye.