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Designing Approval Gates for High-Risk AI Agent Actions
Design robust AI agent approval gates for high-risk actions. Ensure safety, auditability, and accountability in production agent architectures.
Learn it from developers who built it.
Tutorials, how-tos and deep dives on AI, .NET, C#, cloud and developer tools, written by the community.
Design robust AI agent approval gates for high-risk actions. Ensure safety, auditability, and accountability in production agent architectures.
Build a low-latency, multi-agent RAG system for loan & mortgage credit risk analysis using LangGraph and Neo4j.
Unlock scalable AI agent expertise with MCP-served skills. Centralize, govern, and distribute domain knowledge efficiently.
Learn when to avoid multi-agent systems in fintech. A decision framework for efficient, cost-effective, and performant architectures.
Secure AI agents by rigorously testing permissions. Learn how to prevent unauthorized access, tool chaining, and prompt injection.
Fixing state bleed bugs in multi-agent AI for finance. Learn about mutable references, async concurrency, and hardened architectures for enterprise AI.
Secure AI agent actions with tamper-evident audit logs. Learn to build cryptographic hash chains for trusted, auditable AI operations.
Automated feedback signals for digital banking RAG refine AI agents by embedding structured data in LangGraph traces, driving continuous, data-driven optimization.
Unmasking the illusion of enterprise AI governance. Discover why tech giants' 'black-box' solutions fail and the only real path to AI accountability.
Gate2ASI AI's AgentFactory outpaces tech giants with its governed digital workforce, built from scratch for structured AI operations.
Gate2Asi AI's AgentFactory builds a governed digital workforce for enterprise AI, moving beyond agents to accountable work orders and auditable outcomes.
Gate2ASI AgentFactory: Vertical, parallel, and council-based AI architecture for a governed digital enterprise workforce.
Build scalable Enterprise AI Skill Libraries with MCP Tool Collections in .NET for reusable, governed AI capabilities and a centralized skill catalog.
Build resilient, asynchronous AI agent workflows with Azure Service Bus and .NET, overcoming synchronous bottlenecks for enterprise tasks.
Scale enterprise MCP with dynamic tool discovery. Optimize LLM prompts, enhance security, and improve accuracy by surfacing relevant tools.
Build an enterprise RAG system with LangGraph and a Parent Document Retriever to retrieve full context, coordinate agents, and validate financial-analysis responses.
Build deterministic, observable, and resilient prompt-routed workflows with Azure AI Foundry Model Router and Microsoft Agent Framework for robust orchestration.
AI-powered build diagnostics with Microsoft Binlog MCP Server. Analyze MSBuild .binlog files via natural language for faster debugging.
Build scalable enterprise AI with Microsoft Agent Framework. Decouple monolithic AI into specialized agents for complex workflows.
Unlock AI agent insights beyond logs & metrics. Track decisions, tool calls, prompts, and context for robust debugging & optimization.
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AI Agent vs. Workflow: Understand the crucial difference with a banking example. Learn when to use LLMs for structured tasks vs. dynamic decision-making.
Build enterprise AI agents with Neo4j and C#. Learn to model relationships, integrate with ASP.NET Core, and enhance LLM reasoning.
Learn to build an MCP server with ASP.NET Core, expose enterprise tools for AI agents, and apply dependency injection, logging, security, and scalable design practices.
Navigating financial RAG contradictions: A hybrid framework for deterministic, LLM-driven, and human-in-the-loop resolution.
Uncover the ultimate retrieval strategy for financial anomaly detection: Agentic Multi-Modal Retrieval, combining text, graph, and time-series data.
AI Agents vs. Workflows: Understand architecture, use cases, and best practices for deterministic automation and goal-driven decision-making.
Unlock precision in financial RAG with a 6D metadata framework. Enhance retrieval, reduce hallucination, and ensure compliance.
Mitigating financial LLM hallucinations with a multi-agent architecture, provenance tracking, and layered defenses for regulatory reporting.
Build enterprise multi-agent ReAct loops with raw Ollama & LangGraph for low latency, strict control, and persistent memory in regulated environments.
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