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Learn how to build an enterprise financial audit system using Okta, MCP-style context optimization, LangGraph, RAG, and persistent memory to reduce token usage and improve security, compliance, and auditability.
Benchmark context compaction strategies for AI coding agents by measuring cache efficiency, token usage, latency, model performance, and workload reliability.
Build a LangGraph multi-agent system that uses RAG and persistent memory to analyze Samsung Health wearable data, generate health recommendations, and escalate critical alerts.
Learn how to test AI agent tool selection with large tool catalogs using Microsoft Agent Framework, measuring selection accuracy, latency, reliability, and tool discovery performance.
Benchmark Microsoft Foundry Hosted Agents by measuring sandbox startup time, isolation overhead, compute usage, memory, filesystem performance, and agent execution efficiency.
Learn how to build AI tool approval workflows with Microsoft Foundry using human-in-the-loop controls, permissions, authorization, auditing, and secure agent execution.
Most developers view Retrieval-Augmented Generation (RAG) as a tool for answering questions: "What is our refund policy?" or "Show me the transaction history." In high-frequency domains like QR Code Payments, RAG can be integrated into a Predictive Intelligence Workflow. Here, retrieval doesn't just fetch facts; it fetches contextual patterns, historical anomalies, and dynamic risk rules that feed into a predictive model. The LLM then acts not as a chatbot, but as a reasoning engine that synthesizes retrieved data to predict outcomes (e.g., fraud probability, merchant churn, settlement delay) before they happen.
First, let's address a critical misconception: Transformer-based architectures are NOT typically used for the core "Wallet Creation" module (which is usually a simple CRUD operation involving user registration, KYC verification, and database record creation). However, transformers ARE uniquely valuable in the Intelligent Wallet Onboarding & Personalization Engine that runs during wallet creation. This is where the real enterprise value lies.
Benchmark end-to-end voice agent latency from speech recognition through model response and tool execution to speech synthesis, identifying bottlenecks across the complete conversational AI pipeline.
Learn how to build a domain-specific explainability validation framework for EduTech credit models using SHAP, LIME, LangGraph, Chroma, persistent memory, and Model Context Protocol (MCP). This article explores how to validate AI-generated explanations across technical soundness, pedagogical validity, regulatory compliance, and historical consistency. It demonstrates a multi-agent architecture that combines learning-science knowledge, live student performance data, policy guidance, and explanation history to identify unstable or misleading explanations before they reach students, advisors, or compliance teams.
Build secure hosted AI agents using isolated runtime environments with controlled compute, memory, filesystem access, permissions, network boundaries, and enterprise security controls.
Extracting value from EduTech reviews requires moving beyond sentiment analysis toward pedagogically-grounded structured extraction. The NLP pipeline described here combining domain-specific classification, evidence-span preservation, confidence gating, and MCP-native output formatting transforms unstructured student voice into machine-actionable curriculum intelligence.
Build browser automation agents with Playwright and .NET that can navigate websites, interact with web pages, execute controlled actions, and apply production-ready agent safety guardrails.
Intent alignment is the missing layer in financial RAG systems. By treating analyst review text as semantic anchors and enriched ledger descriptions as structural bridges, organizations can transform vague business questions into precise financial queries without requiring users to learn GL codes or accounting terminology.
AI coding agents change software ownership and assurance. Learn how enterprises govern AI-generated code, security, provenance, and quality.
Learn how to tune Chroma retrieval for FMCG catalogs and build a LangGraph multi-agent RAG assistant for product, compliance, and inventory queries.
Measure AI agent token waste caused by poor tool descriptions and evaluate tool-selection accuracy, prompt efficiency, latency, token usage, and inference costs.
Test AI coding agents against dangerous infrastructure changes using adversarial scenarios for Terraform, IAM, databases, deployments, permissions, and destructive cloud operations.
This dual-signal intent alignment architecture reduced GL misclassification rates by 41% during quarter-end close in pilot deployment, while cutting average classification research time from 22 minutes to under 4 minutes per ambiguous transaction.
In accounting and financial management RAG, relevance isn't a nice-to-have—it's a compliance requirement. A wrong retrieval in tax guidance or audit standards can trigger regulatory findings. After tuning ChromaDB across multiple financial deployments, these decisions had the largest measurable impact
During mass disruption, agents ask questions spanning multiple domains with vastly different precision requirements. A single retrieval configuration either floods safety-critical queries with noise or starves broad informational queries of context.
In airline recommender systems, collaborative filtering (CF) and behavioral models excel at answering "What did similar passengers book?" but fail catastrophically at "Why are they booking now?" Semantic retrieval fills this gap. Here’s how they complement each other in production:
In digital banking customer onboarding, "drift" isn't an abstract ML concept—it's a regulatory and conversion risk. When embeddings or product catalogs drift silently, customers get wrong KYC requirements, outdated fee disclosures, or broken eligibility checks. Here’s how we detect it
Unlock enterprise AI with Agent Skill Registries. Discover, govern, and version reusable skills for .NET platforms, ensuring robust lifecycle management.
Build cross-system BI agents with MCP. Discover data, invoke tools, and ensure data provenance for reliable AI-driven insights.
Unlock advanced AI: Learn how RAG, MCP, and AI Agents combine for dynamic knowledge, universal connectivity, and autonomous task execution.
Automate infrastructure health checks with Python! Build a tool to monitor endpoints, ports, CPU, memory, and disk for faster, consistent operational insights.
Benchmarking graph vs. non-graph for cash management reveals graph's superiority for relational, multi-hop risk workloads, proving its value.
Securely design multi-tenant AI agent skills with tenant isolation, authorization, filtering, and audit logging for enterprise platforms.
Enterprise AI needs accountable execution, not just RAG. AgentFactory offers governed autonomy, verification, and stateful recovery for AI.
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