Latest blog posts

This MCP Weekly tracks Copilot’s shift into agentic AI, the security reality of production agents, and MCP’s move from backend plumbing into the UI layer.

Enterprise AI struggles to deliver results when execution breaks across systems. A look at how fragmentation limits real enterprise outcomes.

This MCP Weekly covers MCP’s move to the Linux Foundation, rising support from AWS and Google Cloud, and new security focused MCP servers for production use.

A major week for MCP as AWS unifies cloud architecture, security platforms emerge to protect agent actions, and Snowflake and Anthropic commit $200M to governed AI agents.

This week: MCP turns one as OpenAI’s supply-chain breach underscores rising security demands and major vendors adopt MCP for payments, infrastructure, and long-running agents.

Anthropic’s latest MCP spec is here! No cake, but task workflows, extensions, and enterprise-grade auth. We break down what’s new, what matters most, and what’s coming next for agent infrastructure.

This week: Power BI’s server debut, evolving agent frameworks, and expanding enterprise-grade security and tooling. Plus: lessons from the Cloudflare outage.

Trying to choose between Zapier, n8n, and browser-based frameworks for sales automation? We tested all three and added Gentoro to extend what workflows can do.

What if agents didn’t have to call tools directly? There's a new execution model for agent systems that separates planning from doing, reduces token cost, and makes interactions with APIs faster and more reliable.

This week: Agent Sandbox, an MCP powered cyber espionage case, D365 ERP, Context Forge and new GPT 5.1 tools show MCP becoming core enterprise infrastructure.

Anthropic’s latest blog highlights a major shift in AI agent design. Here’s what this means for the future of MCP-based workflows.

AI agents can call tools, but can they use them reliably? Explore how agent-native interfaces enable smarter, faster, and more scalable AI integration.

Anthropic’s new Skills system gives Claude reusable, modular abilities that live outside the prompt. Here’s what it means for developers, enterprises, and the next wave of agentic AI.

Wrapper and retries and patching - oh my! Discover why current AI integration methods don’t scale and how smarter tooling can fix it.

OpenAPI makes APIs machine-readable, but not agent-usable. Learn why LLMs struggle with traditional specs and how a semantic interface layer like MCP helps.

Learn how the LLM Function Design Pattern reduces fragility in AI apps by consolidating prompts, inputs, outputs, and tools into a single structured unit.

Agents vs. tools? With MCP elicitations, it’s no longer a real distinction. See how this shift changes how developers build AI workflows.

What exactly is Anthropic’s new MCP Registry? How does it differ from npm-style repos? And what does it mean for existing registries and developers? Find out here.

Generate secure, enterprise-ready MCP Tools from OpenAPI specs. Discover why Gentoro’s agent-aligned platform outperforms SDK-based generators for enterprise AI.

A hands-on look at AI no-code platforms, comparing speed, flexibility, and real-world usability when building a live restaurant booking app.

Discover how MCP bridges the gap between traditional APIs and reasoning agents by providing semantic context, structured outputs, and more.

MCP needs an orchestrated middleware layer to work reliably. Learn how Gentoro helps scale agentic workflows with model-aware, reusable runtime infrastructure.

Why do MCP Tools behave differently across LLM models? Learn how model assumptions impact tool usage, and explore practical strategies to improve reliability.

Traditional regression testing fails for modern AI systems powered by MCP. Learn how to rethink testing for non-deterministic LLM-driven workflows.

Enterprise systems aren’t ready for AI agents. From integration bottlenecks to outdated infrastructure, here's why adoption stalls and how Gentoro can help.

Learn how developers are navigating the complex landscape of LLM apps, AI agents, and hybrid architectures — and how protocols like MCP and A2A are shaping the future of AI integration.

Deploy an AI agent using LangChain and Gentoro to automate incident detection, analysis, team alerts, and JIRA ticket creation, enhancing production support efficiency.

LangChain made AI development easier, but as applications evolve, its limitations are showing. Explore what’s next for AI frameworks beyond chain-based models.

Vibe coding, powered by generative AI, is redefining software creation and interaction. Learn how this paradigm shift is transforming development and user experience.

LangChain is powerful but manually intensive. What if agentic AI handled the complexity? Explore how directive programming could redefine AI development.

Discover how API-aligned and user-aligned function designs influence LLM performance, optimizing outcomes in function-calling tasks.

Uncover the technical hurdles developers encounter when connecting LLMs with enterprise systems. From API design to security, this blog addresses it all.

Discover how to tackle LLM output formatting challenges with JSON mode and DTOs, ensuring more reliable ChatGPT responses for application development.

Learn why AI should build function-calling systems dynamically instead of manual coding, and how to future-proof these systems against LLM updates and changes.

Explore function-calling in LLMs, its challenges in API integration, and how User-Aligned Functions can bridge the gap between user requests and system APIs.

Learn how Retrieval-Augmented Generation (RAG) enhances LLMs by connecting them to external data sources, enabling real-time data access and improved responses.