Some agentic AI systems are less about open-ended reasoning and more about working through large volumes of documents, data connectors, and retrieval pipelines to get a task done. LlamaIndex Agents grew directly out of LlamaIndex's strength in data indexing and retrieval, giving developers a way to build single and multi-agent systems that stay closely tied to a strong data layer underneath them. This blog explains what LlamaIndex Agents are, how they fit into agentic AI deve

Some agentic AI teams need more than a way to connect an agent to a few tools. They need a framework that treats agents as real software systems, with testing, versioning, debugging, and deployment built in from the start. Google's Agent Development Kit, known as ADK, was built with exactly that production mindset, growing out of the same framework already powering agents inside Google products like Agentspace and the Google Customer Engagement Suite. This blog explains what

1. Context It is the classic enterprise generative AI story. Three months ago, your engineering team built a proof-of-concept Retrieval-Augmented Generation (RAG) assistant using Azure OpenAI and Azure AI Search. You loaded fifty clean PDF product manuals and policy handbooks into an index, hooked up GPT-4o, and ran a live demonstration for executive stakeholders. The system performed flawlessly. It cited paragraph numbers, answered multi-part queries, and summarized technica

Building an agent directly within OpenAI's own ecosystem used to mean piecing together the Assistants API with custom logic for tool calls and multi-agent coordination. The OpenAI Agents SDK was built to close that gap, giving developers a dedicated toolkit for defining agents, giving them tools, and letting them hand off tasks to one another, all without leaving OpenAI's own platform. This blog explains what the OpenAI Agents SDK is, how it fits into agentic AI development,

Some agentic AI problems are best solved not through a single agent working alone, but through several agents talking to each other, questioning results, and refining an answer together. AutoGen, originally developed by Microsoft, was built around exactly this idea, treating conversation between agents as the primary way multi-agent systems get work done. This blog explains what AutoGen is, how it fits into agentic AI development, how implementation generally works, and how i

Some agentic AI tasks are too broad for a single agent to handle well on its own. Research, writing, reviewing, and finalizing a piece of content, for example, benefit from being split across specialized roles rather than one agent trying to do everything. CrewAI is a framework built specifically around this idea, letting developers assemble a team of agents, each with a defined role, that work together toward a shared goal. This blog explains what CrewAI is, how it fits into

Agentic AI systems need more than a single prompt and response. They need to plan, take actions, evaluate outcomes, and sometimes loop back to try a different approach before arriving at a final answer. LangGraph is a framework built specifically to support this kind of stateful, multi-step reasoning, making it a common choice for teams building AI agents rather than simple one-shot language model applications. This blog explains what LangGraph is, how it fits into agentic AI

Building a Retrieval Augmented Generation system involves wiring together several moving parts: a document loader, a text splitter, an embedding model, a vector database, and a language model, all working in sequence. LangChain is a framework built specifically to make that wiring easier, offering pre-built components and a common structure for connecting them into a working RAG pipeline. This blog explains what LangChain is, how it fits into a RAG pipeline, how implementatio

Not every RAG application needs, or can use, a fully closed, hosted only language model. Mistral has carved out a distinct position among LLM providers by offering both open weight models that can be self-hosted and a hosted API for teams that prefer a managed experience. This flexibility has made Mistral a common choice for RAG projects that want more control over deployment without giving up access to strong language model performance. This blog explains what Mistral offers

The quality of a Retrieval Augmented Generation system depends heavily on how well its language model reasons over retrieved context and stays faithful to it. Anthropic builds the Claude family of language models, with a strong emphasis on reliability and careful instruction following, which has made it a common choice for RAG applications where trustworthy, well grounded output matters. This blog covers what Anthropic offers for RAG development, how Claude models fit into a

The language model is the component in a Retrieval Augmented Generation system that turns retrieved context into a coherent, useful answer. OpenAI is one of the most widely used providers of large language models for this purpose, offering models that power everything from simple question answering systems to complex enterprise RAG applications. This blog explains what OpenAI offers for RAG development, how its models fit into a RAG pipeline, how implementation generally work

Speed is often the deciding factor in real time RAG applications, where retrieval needs to happen in milliseconds to keep the overall response time low. Redis, long known as an in memory data store, now supports vector similarity search, commonly referred to as Redis VSS. This brings fast vector retrieval into a system many teams already use for caching and real time data. This blog covers what Redis VSS is, how it fits into a RAG pipeline, how implementation generally works,

Choosing a vector database for a Retrieval Augmented Generation application often comes down to how much flexibility a team needs beyond basic similarity search. Weaviate is an open source vector database that has gained attention for combining vector search with additional capabilities such as hybrid search and flexible schema design, making it a versatile option for RAG development. This blog explains what Weaviate is, how it fits into a RAG pipeline, how implementation gen

As Retrieval Augmented Generation applications grow from small prototypes into large scale production systems, the demands placed on a vector database change significantly. Milvus is a vector database built specifically to handle that kind of scale, making it a common choice for teams working with very large embedding collections and high query volumes. This blog covers what Milvus is, how it fits into a RAG pipeline, how implementation generally works, and how it compares to

Every Retrieval Augmented Generation system needs a way to store and search embeddings efficiently. While many teams reach for a dedicated vector database, others prefer to keep everything within a database they already trust. This is where pgvector comes in. As a PostgreSQL extension, pgvector brings vector similarity search directly into a relational database that many teams are already using. This blog explains what pgvector is, how it fits into a RAG pipeline, how it is t

Retrieval Augmented Generation depends on one core capability: finding the right piece of information from a large collection of data, quickly and accurately. That capability comes from a vector database. Among the many options available today, Chroma has become a popular starting point for teams building RAG applications, especially those who want an open source, developer friendly solution. This blog covers what Chroma is, how it fits into a RAG pipeline, how implementation

Retrieval Augmented Generation has become one of the most practical ways to make large language models work with real, up to date, and domain specific information. At the center of most RAG systems sits a component that often does not get enough attention: the vector database. Without an efficient way to store and search through embeddings, a RAG pipeline cannot retrieve relevant context quickly or accurately. Pinecone is one of the most widely used vector databases for build

Why 1-million-token context windows won't save your 50-turn agentic workflows, and the concrete engineering patterns, mathematical models, and benchmarks to master context compaction. The Long-Context Illusion in Production In the early days of building LLM applications, the context window was a tight bottleneck. Managing a 4,096-token limit for GPT-3.5 required aggressive prompt slicing, brittle truncation heuristics, and constant vector-store lookups. When foundation model

What You'll Learn in This Guide Healthcare organizations are under increasing pressure to improve patient care while managing growing volumes of clinical data, complex regulatory requirements, and an expanding ecosystem of digital systems. Although hospitals have invested significantly in technologies such as Electronic Health Records (EHRs), Hospital Information Systems (HIS), laboratory platforms, and patient portals, healthcare professionals often spend valuable time navig

Your Machine Learning Model Is Changing Even If You Never Retrain It A fraud detection model that blocked suspicious transactions last month may begin approving fraudulent payments today. A demand forecasting model that accurately predicted inventory requirements last quarter can gradually overstock warehouses or leave shelves empty. A healthcare risk model may become less reliable as patient populations, treatment protocols, and disease patterns evolve. The problem is not al