Blog Posts

Introducing a powerful new feature in Gentoro that lets you automatically generate agentic tools from any OpenAPI spec—no integration code required.

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.

Build production-ready AI agents faster with Gentoro’s native LangChain support—no more glue code, flaky APIs, or auth headaches.

Agentic tools let AI act beyond text generation, using inferred invocation to interact with real-world applications. Learn how MCP simplifies AI-tool connections.

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 Gentoro’s support for Model Context Protocol (MCP) simplifies AI tool integration, enabling smarter workflows with Claude Desktop and more.

Explore how function-calling and MCP revolutionize enterprise workflows by simplifying LLM usage and showcasing their unique roles in development.

Learn how MCP servers streamline enterprise LLM development, overcome framework hurdles, and power scalable, efficient generative AI applications with ease.

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.

Understand the hidden token costs of OpenAI's function-calling feature and how Contextual Function-Calling can reduce expenses in LLM applications.

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.

Introducing Gentoro, a middleware solution that enables AI agents to securely interact with enterprise systems while simplifying development and ensuring compliance.

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