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Learn how to build a consistent 360-degree customer feature store using LangGraph, RAG, multi-agent orchestration, conflict detection, and reconciliation.
Learn how to design AI agent memory in C# with short-term context, long-term preferences, semantic retrieval, expiration, and practical memory best practices.
Learn how to debug AI agents when correct-looking answers hide tool failures, stale data, wrong decisions, and incorrect execution paths using tracing and validation.
Governance in a feature store is not about restricting innovation; it is about ensuring sustainability and reliability. By automating the detection of feature leakage and encouraging reuse through intelligent multi-agent systems, enterprises can protect their ML investments from technical debt and regulatory risk.
Handling the chaos of real-time data streams is not optional for enterprise Graph RAG systems it is a foundational requirement. By leveraging LangGraph’s persistent state, we can implement robust idempotency checks to kill duplicates, buffering logic to handle out-of-order events, and checkpointing to support safe replays.
Learn how long-running AI agents can recover from connection loss using durable state, retries, idempotency, checkpoints, and resilient C# workflow design.
Learn how Microsoft Foundry connects AI agents with A2A 1.0, covering agent cards, authentication, transports, timeouts, and troubleshooting in production.
In our POC, the Graph RAG agent successfully identified a hidden supply chain risk that the traditional model missed, resulting in a significant score adjustment and a "Superior" verdict. This demonstrates that Graph RAG is not just a technological upgrade, but a risk-mitigation tool that provides tangible, measurable value to enterprise lending portfolios.
Learn how to add human approval to long-running C# AI agents using workflow state, approval events, authorization, idempotency, and durable execution patterns.
Handling the dichotomy between online serving and offline computation is one of the most significant hurdles in enterprise MLOps. While offline features provide the statistical depth required for robust model training, online features provide the temporal relevance needed for real-time action. By implementing a Multi-Agent LangGraph architecture, organizations can bridge this gap effectively. This system not only serves features but also actively monitors for training-serving skew and applies contextual policies to manage data inconsistencies. As AI systems become more autonomous, the ability to maintain this balance will be crucial for ensuring both performance and reliability in production environments.
Learn how Microsoft Agent Framework helps C# AI agents recover from failures using durable workflows, retries, checkpoints, and safe execution patterns.
Learn how to design reliable AI agent workflows in C# using state machines, explicit transitions, approval states, retries, persistence, authorization, and execution controls.
Predictive clickstream analytics requires moving beyond descriptive dashboards to prescriptive, real-time systems. The most valuable signals—session velocity, error loops, and cross-session re-engagement—are inherently contextual and benefit from multi-agent reasoning. By implementing a LangGraph-based RAG architecture with persistent state, enterprises can transform raw telemetry into intelligent interventions.
Learn how to trace AI agent tool calls with OpenTelemetry in .NET, track model and tool latency, retries, errors, and build secure production observability.
By leveraging LangGraph, we created a system where the Graph Traversal Agent maps the structural landscape, the RAG Agent provides the regulatory and historical context, and the Synthesis Agent delivers a decision that is both mathematically grounded in graph theory and explainable through natural language. This architecture ensures that enterprises can detect sophisticated synthetic identity fraud rings that would otherwise slip through the cracks of flat, tabular data pipelines.
In our hybrid architecture, the classical XGBoost model produces a deterministic probability of default from tabular features. The LLM layer then analyzes business plans, owner letters, and other unstructured documents to identify qualitative risk signals that classical models cannot see. Through RAG, the system retrieves relevant underwriting policies to ensure the LLM's adjustments align with institutional guidelines. The synthesis agent combines these outputs into a final decision with full explainability.
Learn how Microsoft Foundry Agent Inspector and tracing help find slow tool calls, failed agent runs, retries, latency issues, and execution problems during development.
Learn how to measure wasted compute in AI coding agents by tracking model calls, tool usage, retries, repository context, tests, tokens, and task efficiency.
Our Multi-Agent LangGraph POC demonstrates how specialized agents can handle the complexity of chunking, graph construction, and contextual retrieval, resulting in highly accurate and explainable AML investigations. This approach transforms raw, fragmented data into a coherent web of financial intelligence.
Learn how local AI models keep sensitive data closer to your application, with practical guidance on access control, tools, storage, logging, and security.
Learn how AI coding agents manage long-running software tasks using context, repository exploration, tool execution, testing, checkpoints, and iterative debugging.
By architecting a Multi-Agent LangGraph system with RAG, we empowered specialized agents to collaboratively investigate causality. The Data Agent extracts metrics, the RAG Agent surfaces critical historical and market context, the Causal Inference Agent applies rigorous Difference-in-Differences methodology, and the Reporting Agent delivers an explainable verdict.
Compare Rust and TypeScript for AI agent runtimes, covering memory, concurrency, process execution, performance, deployment, and architecture trade-offs.
Learn how multiple AI coding agents work on one repository, manage Git worktrees, coordinate tasks, avoid conflicts, and safely validate integrated changes.
The transition from traditional feature-based ML pipelines to RAG-powered multi-agent workflows marks a critical evolution in Fintech credit scoring. While traditional models are fast and mathematically rigorous, they are inherently blind to context. By integrating LangGraph, we created a system where the Financial Agent handles the math, the RAG Agent provides the real-world context, and the Underwriting Agent synthesizes both.
Unsafe exploration is not an RL problem it is an architecture problem. By combining RAG-driven constraint retrieval, action masking, and deterministic guardrail nodes within a LangGraph workflow, we create a system where the RL agent can learn and optimize within a provably safe boundary. For digital wallet onboarding, this means better conversion rates without regulatory nightmares.
RAG-based market intelligence transforms a pricing engine from a reactive calculator into a proactive strategist. By grounding decisions in real-time external data, we ensure that wallet creation incentives are not just mathematically optimal, but competitively viable. This POC illustrates the power of combining LangGraph, RAG, and RL to build intelligent, context-aware financial systems.
Offline simulation and counterfactual analysis are not just "nice-to-haves"; they are essential safeguards for AI in finance. By integrating these techniques into a LangGraph workflow, we create a loan processing engine that is not only efficient but also transparent, fair, and robust. This approach allows institutions to deploy AI with confidence, knowing that every decision has been stress-tested against a myriad of "what-if" scenarios.
Correlation tells you what is happening; causation tells you what to do about it. In churn prediction and pricing, acting on correlation leads to margin erosion and ineffective interventions. By integrating Causal Inference and Uplift Modeling into a LangGraph multi-agent architecture, supported by RAG over historical experiments, enterprises can build pricing engines that are not just predictive, but truly prescriptive.
Designing the State, Action, and Reward is the most critical step in building an RL-powered pricing or onboarding engine. By defining these clearly and wrapping them in a LangGraph multi-agent architecture, we can create systems that are not only intelligent but also compliant and context-aware. This POC demonstrates how to move from static marketing to dynamic, learning-driven user engagement.
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