{"id":3082,"date":"2025-09-02T12:16:45","date_gmt":"2025-09-02T06:46:45","guid":{"rendered":"https:\/\/ninjacart.com\/blog\/?p=3082"},"modified":"2025-09-02T13:40:26","modified_gmt":"2025-09-02T08:10:26","slug":"agentic-ai-risk-models-mcp-servers-and-ai-agents-part-b","status":"publish","type":"post","link":"https:\/\/ninjacart.com\/blog\/agentic-ai-risk-models-mcp-servers-and-ai-agents-part-b\/","title":{"rendered":"Agentic AI &#8211; Architecting the future of Credit Risk at Ninjacart &#8211; Part B"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3082\" class=\"elementor elementor-3082\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5271db9 e-flex e-con-boxed e-con e-parent\" data-id=\"5271db9\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5d98124 e-transform elementor-widget elementor-widget-text-editor\" data-id=\"5d98124\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-12,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"p2\">In the <a href=\"https:\/\/ninjacart.com\/blog\/agentic-ai-how-we-developed-platform-components-for-our-automated-credit-risk-system-part-a\/\">first chapter<\/a> of our Agentic AI series, we unfolded the foundation\u2014the core platform components painstakingly built to usher in a new era of automation at Ninjacart, specifically for Credit Risk analysis. These building blocks laid the groundwork for something more powerful: intelligent Agents capable of decision-making, learning, and collaboration.<\/p><p class=\"p2\">Now, in this second part of the story, we journey deeper into the heart of the system\u2014where innovation meets execution.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68b4372 elementor-widget elementor-widget-heading\" data-id=\"68b4372\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Architecting the future of credit risk<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bb2e8c9 elementor-widget elementor-widget-image\" data-id=\"bb2e8c9\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"11456\" height=\"5323\" src=\"https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Detailed-Design.png\" class=\"attachment-full size-full wp-image-3153\" alt=\"\" srcset=\"https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Detailed-Design.png 11456w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Detailed-Design-300x139.png 300w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Detailed-Design-1024x476.png 1024w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Detailed-Design-768x357.png 768w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Detailed-Design-1536x714.png 1536w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Detailed-Design-400x186.png 400w\" sizes=\"(max-width: 11456px) 100vw, 11456px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-81f74b2 elementor-widget elementor-widget-text-editor\" data-id=\"81f74b2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h3 class=\"p2\">Detailed Design<\/h3><p class=\"p2\">For the Credit Risk Automation system, beyond the foundational platform components, the following technical elements were addressed:<\/p><ul><li><b>System Integration<\/b>: Existing microservices were encapsulated as MCP Servers to enable seamless data ingestion from legacy and external systems.<\/li><li><b>Memory Architecture<\/b>: Implemented a dual-layer custom memory management system to support both short-term and long-term contextual retention for agents.<\/li><li><b>Risk Modelling<\/b>: Integrated advanced risk models to enhance decision-making accuracy and provide explainable outputs, ensuring model transparency and regulatory compliance.<\/li><li><b>Agent-Based Execution<\/b>: Deployed multiple specialised agents, each simulating the functions of their human counterparts by carefully drawing bounded contexts for each of the agents, to parallelise and automate domain-specific tasks.<\/li><li><b>Agent Orchestration Framework<\/b>: Established an orchestration layer to coordinate inter-agent communication and workflows, ensuring consistent and goal-aligned output generation.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-afa29dc elementor-widget elementor-widget-text-editor\" data-id=\"afa29dc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h3 class=\"p2\">From Legacy to Intelligence<b><\/b><\/h3><p class=\"p3\">To bring these Agents to life, we didn&#8217;t start from scratch. Instead, we tapped into the wealth ofservices already in our ecosystem. Our approach was clear: maximize the potential of what wealready had while integrating state-of-the-art advancements to elevate the entire process.<\/p><p class=\"p3\">When we set out to transform Credit Risk decisioning, we knew that automation alone wouldn<span class=\"s1\">\u2019<\/span>tbe enough. Our agents needed more than rules\u2014they needed <b>context<\/b>. And that<span class=\"s1\">\u2019<\/span>s where <b>MCP(Model Context Protocol) <\/b>came in.<\/p><p class=\"p3\">MCP wasn<span class=\"s1\">\u2019<\/span>t just another tool\u2014it became the <b>nerve center <\/b>for our agentic system. It gave our agents the ability to operate dynamically by pulling in real-time business context. For CreditRisk, this meant accessing critical borrower data scattered across multiple internal services\u2014financial history, transaction patterns, credit exposure, and more. What once took hours of manual stitching now happened in milliseconds. MCP made context available on demand, powering every decision with relevant, up-to-date insights.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1559113 e-transform elementor-blockquote--skin-border elementor-blockquote--button-color-official elementor-widget elementor-widget-blockquote\" data-id=\"1559113\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-12,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"blockquote.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<blockquote class=\"elementor-blockquote\">\n\t\t\t<p class=\"elementor-blockquote__content\">\n\t\t\t\tThe\nModel Context Protocol (MCP)\nis an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardised way to connect LLMs with the context they need.\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-059b8a4 e-transform elementor-widget elementor-widget-text-editor\" data-id=\"059b8a4\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:0,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>So, the data for the credit decisioning will be powered by our existing services, which will be used as MCP Servers. Some of the services that we used to enable our Credit Risk Automation are<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f58631 e-transform elementor-widget elementor-widget-text-editor\" data-id=\"2f58631\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-24,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ol><li class=\"p3\">USS (User store Service)<\/li><li class=\"p3\">DAM (Digital Asset Management Service)<\/li><li class=\"p3\">GEMS (Generic Entity MicroService)<\/li><li class=\"p3\">LSP (Loan Service Provider Service)<\/li><li class=\"p3\">Risk Service<\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e48404 e-transform elementor-widget elementor-widget-heading\" data-id=\"0e48404\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-24,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Challenges faced during MCP implementation<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a3298f e-transform elementor-widget elementor-widget-text-editor\" data-id=\"5a3298f\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-24,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li class=\"p2\">We had to take a step-wise approach for implementing Spring AI-based MCP servers in our existing microservices.<\/li><li class=\"p2\">First step, we had to convert the existing services, which were running on Java 8 \/ Spring 2. x to Java 21 \/ Spring 3.4.x<\/li><li class=\"p2\">Second step, we added the tools necessary for that particular service to expose to the agents<\/li><li class=\"p2\">Not only this, the rule book, which we discussed in Part A, is a new service introduced also was created natively supporting MCP.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73c9543 e-transform elementor-widget elementor-widget-heading\" data-id=\"73c9543\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-8,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Risk Models<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2fac443 elementor-widget elementor-widget-image\" data-id=\"2fac443\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1002\" height=\"628\" src=\"https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Risk-Models_4.png\" class=\"attachment-full size-full wp-image-3176\" alt=\"\" srcset=\"https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Risk-Models_4.png 1002w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Risk-Models_4-300x188.png 300w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Risk-Models_4-768x481.png 768w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Risk-Models_4-400x251.png 400w\" sizes=\"(max-width: 1002px) 100vw, 1002px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-af3a13f elementor-widget elementor-widget-text-editor\" data-id=\"af3a13f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"p2\">But tools, no matter how sophisticated, are only part of the equation. Impact comes from models that know where to look.<\/p><p class=\"p2\">That<span class=\"s1\">\u2019<\/span>s why, for <b>Eligibility Analysis<\/b>, we didn<span class=\"s1\">\u2019<\/span>t rely on a single algorithm. Instead, we deployed acurated ensemble of machine learning models, each crafted to evaluate a specific dimension of creditworthiness. One model examined historical repayment behaviours. Another looked at market signals. Others captured behavioural trends, income stability, and utilization patterns. Like expert advisors working in sync, they delivered a 360\u00b0 view of borrower risk.<\/p><p class=\"p2\">Together, MCP and the model suite enabled something truly powerful: context-aware, precision-grade decisioning\u2014automated, explainable, and fast. A system where agents don<span class=\"s1\">\u2019<\/span>t just act\u2014they understand.<\/p><p class=\"p2\">The risk models play a critical role in assessing a borrower&#8217;s creditworthiness. To achieve optimal performance, we conducted extensive evaluations across multiple modelling approaches and ultimately adopted the <b>FinGPT framework<\/b>, leveraging <b>Qwen <\/b>for domain-specific financial analysis and <b>FinR1 <\/b>for advanced reasoning capabilities.<\/p><p class=\"p2\">In addition to foundation models, we incorporated <b>XGBoost <\/b>for structured data modelling and employed a <b>Chain-of-Thought (CoT) <\/b>prompting strategy to enhance decision explainability. This hybrid approach enables the system to provide a transparent, step-by-step rationale behind eligibility decisions, thereby improving trust and auditability.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5e8924 elementor-widget elementor-widget-text-editor\" data-id=\"d5e8924\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h3 class=\"p2\"><b>Brains behind the bots<\/b><\/h3><p class=\"p2\">In general, the memory for an agent is something that we provide via context in the prompt passed to LLM that helps the agent to better plan and react, given past interactions or data not immediately available.<\/p><p class=\"p2\">There are three types of long-term memory:<\/p><ol><li class=\"p2\"><b>Episodic <\/b>&#8211; This type of memory contains past interactions and actions performed by the agent. After an action is taken, the application controlling the agent would store the action in some kind of persistent storage so that it can be retrieved later if needed. A good example would be using a vector Database to store the semantic meaning of the interactions.<\/li><li class=\"p2\"><b>Semantic <\/b>&#8211; Any external information that is available to the agent and any knowledge the agent should have about itself. You can think of this as a context similar to one used in RAG applications. It can be internal knowledge only available to the agent or a grounding context to isolate part of the internet-scale data for more accurate answers.<\/li><li class=\"p2\"><b>Procedural <\/b>&#8211; This is systemic information like the structure of the System Prompt, available tools, guardrails, etc. It will usually be stored in Git, Prompt and Tool Registries.<\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-47d4277 elementor-widget elementor-widget-image\" data-id=\"47d4277\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1920\" height=\"1302\" src=\"https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Memory-Header.png\" class=\"attachment-full size-full wp-image-3154\" alt=\"\" srcset=\"https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Memory-Header.png 1920w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Memory-Header-300x203.png 300w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Memory-Header-1024x694.png 1024w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Memory-Header-768x521.png 768w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Memory-Header-1536x1042.png 1536w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Memory-Header-400x271.png 400w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-700ceb5 e-transform elementor-widget elementor-widget-text-editor\" data-id=\"700ceb5\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:27,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h3 class=\"p2\">Rewiring Risk<\/h3>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62f8930 e-transform elementor-blockquote--skin-border elementor-blockquote--button-color-official elementor-widget elementor-widget-blockquote\" data-id=\"62f8930\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:12,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"blockquote.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<blockquote class=\"elementor-blockquote\">\n\t\t\t<p class=\"elementor-blockquote__content\">\n\t\t\t\tAgentic generally refers to the capacity to act independently and achieve outcomes\nthrough self-directed action. In the context of AI, it describes a system or agent that can\nautonomously make decisions, take actions, and adapt to changing circumstances. This means the agent doesn't require constant human guidance but can work towards specific\ngoals independently.\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-690f07c e-transform elementor-widget elementor-widget-text-editor\" data-id=\"690f07c\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:17,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"p2\">To power our automation efforts, we implemented a multi-agent architecture using LangGraph. Each agent was purpose-built\u2014designed with a singular function in mind\u2014and together, they operate as a coordinated system to streamline the Credit Risk process. Think of it as a decentralised intelligence network, where agents specialise, collaborate, and communicate to eliminate friction in decision-making. We also have Human in the Loop (HITL) as part of the agent to accommodate Human feedback when in doubt.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7287ceb elementor-widget elementor-widget-text-editor\" data-id=\"7287ceb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The advantage to this architecture is that each agent can scale individually based on the need. Here is a sample of how all the above components are used in a single agent<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1571482 elementor-widget elementor-widget-image\" data-id=\"1571482\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1478\" height=\"843\" src=\"https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Agentic-arch-header.png\" class=\"attachment-full size-full wp-image-3152\" alt=\"\" srcset=\"https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Agentic-arch-header.png 1478w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Agentic-arch-header-300x171.png 300w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Agentic-arch-header-1024x584.png 1024w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Agentic-arch-header-768x438.png 768w, https:\/\/ninjacart.com\/blog\/wp-content\/uploads\/2025\/06\/Agentic-arch-header-400x228.png 400w\" sizes=\"(max-width: 1478px) 100vw, 1478px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b69e48b e-transform elementor-widget elementor-widget-text-editor\" data-id=\"b69e48b\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:24,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In the Credit Risk Automation workflow, four primary agents were deployed, each responsible for a specific stage of the decisioning pipeline:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b590768 elementor-widget elementor-widget-text-editor\" data-id=\"b590768\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"p2\"><b>1. Document Verification Agent<\/b>: This agent is responsible for validating the authenticity and completeness of borrower-submitted documents. It also performs structured data extraction from unstructured inputs using OCR techniques. Extracted attributes, such as Market License Numbers, are leveraged for downstream tasks like deduplication and identity validation.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-77afb71 e-transform elementor-blockquote--skin-border elementor-blockquote--button-color-official elementor-widget elementor-widget-blockquote\" data-id=\"77afb71\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-24,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"blockquote.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<blockquote class=\"elementor-blockquote\">\n\t\t\t<p class=\"elementor-blockquote__content\">\n\t\t\t\tSkills: OCR, Name matching, DOB matching, Address matching\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-feb9d1f e-transform elementor-widget elementor-widget-text-editor\" data-id=\"feb9d1f\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-20,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"p2\"><b>2. Screening Operations Agent<\/b>: Executes pre-eligibility risk assessments, including bank statement deduplication, behavioural analysis using historical transaction patterns (where available), and checks against internal fraud rings or blacklists.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-210c338 e-transform elementor-blockquote--skin-border elementor-blockquote--button-color-official elementor-widget elementor-widget-blockquote\" data-id=\"210c338\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-48,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"blockquote.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<blockquote class=\"elementor-blockquote\">\n\t\t\t<p class=\"elementor-blockquote__content\">\n\t\t\t\tSkills: Bank Statement Dedupe, Transactions Behaviour analysis, Blacklist analysis\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9adbcb4 e-transform elementor-widget elementor-widget-text-editor\" data-id=\"9adbcb4\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-42,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"p1\"><b>3. Eligibility &amp; Lender Selection Agent<\/b>: Utilises internal risk models to assess borrower eligibility and dynamically selects the most suitable lending partner based on borrower-lender profile alignment.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20588c6 e-transform elementor-blockquote--skin-border elementor-blockquote--button-color-official elementor-widget elementor-widget-blockquote\" data-id=\"20588c6\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-65,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"blockquote.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<blockquote class=\"elementor-blockquote\">\n\t\t\t<p class=\"elementor-blockquote__content\">\n\t\t\t\tSkills:\nEligibility check using multiple models, Lender selection\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-81b5245 e-transform elementor-widget elementor-widget-text-editor\" data-id=\"81b5245\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-61,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"p2\"><b>4. Reporting Agent<\/b>: Aggregates and summarises outputs from all upstream agents, generating a comprehensive decisioning report. This report serves both operational traceability and as an input to further processing or manual review when necessary.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c6f92f e-transform elementor-blockquote--skin-border elementor-blockquote--button-color-official elementor-widget elementor-widget-blockquote\" data-id=\"5c6f92f\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-85,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"blockquote.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<blockquote class=\"elementor-blockquote\">\n\t\t\t<p class=\"elementor-blockquote__content\">\n\t\t\t\tSkills:\nReport generation\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-77e8f4e e-transform elementor-widget elementor-widget-heading\" data-id=\"77e8f4e\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-55,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7252d70 e-transform elementor-widget elementor-widget-text-editor\" data-id=\"7252d70\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-61,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p class=\"p2\">What you<span class=\"s1\">\u2019<\/span>ve seen so far is just the surface. In the next installment\u2014we<span class=\"s1\">\u2019<\/span>ll dive deep into the deployment architecture of the agents and the components used to make sure our agents have proper auditing, traceability and scalability.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9780c6d e-transform elementor-widget elementor-widget-heading\" data-id=\"9780c6d\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-61,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Contributors<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c3c08ba e-transform elementor-widget elementor-widget-text-editor\" data-id=\"c3c08ba\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-61,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ol><li>Arya &#8211; Agents\u00a0<\/li><li class=\"p3\">Deepan &#8211; Agents &amp; Framework\u00a0<\/li><li class=\"p3\">Jahnavi &#8211; Risk Models\u00a0<\/li><li class=\"p3\">Nidhi &#8211; Risk Models\u00a0<\/li><li class=\"p3\">Alan &#8211; MCP Servers\u00a0<\/li><li class=\"p3\">Sharukh &#8211; Vision\u00a0<\/li><li class=\"p3\">Vijay &#8211; Vision\u00a0<\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f21e269 e-transform elementor-widget elementor-widget-heading\" data-id=\"f21e269\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-49,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">References<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-24d06ca e-transform elementor-widget elementor-widget-text-editor\" data-id=\"24d06ca\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-49,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ol><li class=\"p1\">MCP &#8211; <a href=\"https:\/\/github.com\/modelcontextprotocol\">Model Context Protocol<\/a><\/li><li class=\"p1\">FinGPT &#8211; <a href=\"https:\/\/github.com\/AI4Finance-Foundation\/FinGPT\">GitHub &#8211; AI4Finance-Foundation\/FinGPT: FinGPT: Open-Source Financial Large Language Models! Revolutionize, We release the trained model on HuggingFace.<\/a><\/li><li class=\"p1\">Fin R1 &#8211; <a href=\"https:\/\/arxiv.org\/abs\/2503.16252\">Fin-R1: A Large Language Model for Financial Reasoning through&#8230;<\/a><\/li><li class=\"p1\">XgBoost &#8211; <a href=\"https:\/\/xgboost.readthedocs.io\/en\/release_3.0.0\/\">XGBoost Documentation \u2014 xgboost 3.0.2 documentation<\/a><\/li><li class=\"p1\">Chain of thoughts &#8211; <a href=\"https:\/\/xgboost.readthedocs.io\/en\/release_3.0.0\/\">Chain-of-Thought Prompting Elicits Reasoning in Large Language Models<\/a><\/li><li class=\"p1\">Langgraph &#8211; <a href=\"https:\/\/langchain-ai.github.io\/langgraph\/\">LangGraph<\/a><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-76008a7 e-flex e-con-boxed e-con e-parent\" data-id=\"76008a7\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-530daa2 elementor-widget elementor-widget-button\" data-id=\"530daa2\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/ninjacart.com\/blog\/agentic-ai-how-we-developed-platform-components-for-our-automated-credit-risk-system-part-a\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Explore Part A<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>In the first chapter of our Agentic AI series, we unfolded the foundation\u2014the core platform components painstakingly built to usher in a new era of automation at Ninjacart, specifically for Credit Risk analysis. These building blocks laid the groundwork for something more powerful: intelligent Agents capable of decision-making, learning, and collaboration. Now, in this second [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":3171,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_wp_applaud_exclude":false,"footnotes":""},"categories":[129,5],"tags":[],"class_list":["post-3082","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","category-technology"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Agentic AI - Architecting the future of Credit Risk at Ninjacart - Part B - Ninjacart Blogs<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ninjacart.com\/blog\/agentic-ai-risk-models-mcp-servers-and-ai-agents-part-b\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Agentic AI - Architecting the future of Credit Risk at Ninjacart - Part B - Ninjacart Blogs\" \/>\n<meta property=\"og:description\" content=\"In the first chapter of our Agentic AI series, we unfolded the foundation\u2014the core platform components painstakingly built to usher in a new era of automation at Ninjacart, specifically for Credit Risk analysis. 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