Business leaders researching Gemini for their organization often run into a confusing problem before they even get to features or pricing: Google has used the name Gemini Enterprise for more than one product, and most of what shows up in a search is describing the wrong one. Getting the naming straight matters, because the actual capabilities, pricing, and buying process differ significantly depending on which product a business is really looking at. This blog explains the th

Most businesses do not lack data. They lack a fast, reliable way to turn that data into an answer a decision maker can act on the same day it is needed. BigQuery, Google Cloud's fully managed, serverless data warehouse, was built to close that gap, letting organizations store, query, and now increasingly converse with massive datasets without managing the underlying infrastructure themselves. This blog explains what BigQuery is, why a business might need it, how implementatio

Businesses exploring AI adoption often start by evaluating individual pieces separately, a language model here, a vector database there, a monitoring tool somewhere else, before realizing how much effort goes into just connecting them all. Vertex AI, Google Cloud's unified AI and machine learning platform, was built to remove that friction, bundling model access, infrastructure, governance, and deployment tooling into a single environment. This blog explains what Vertex AI is

An agent built purely on prompting can reason about a problem, but it cannot search the web, query a database, or run a calculation on its own. LangChain Tools exist to close that gap, wrapping ordinary functions in a structure an agent can discover, call, and learn from within its reasoning loop. Alongside protocols like MCP, LangChain's own tool system remains one of the most widely used ways developers give agents the ability to actually act. This blog explains what LangCh

An agent that can reason brilliantly but cannot reach a database, call an API, or read a file is not particularly useful. For years, every one of those connections had to be built as a custom, one-off integration between a specific model and a specific tool. The Model Context Protocol, known as MCP, was introduced by Anthropic in November 2024 to solve exactly this problem, and by 2026 it has become the dominant standard for connecting agentic AI systems to the tools and data

Google has been positioning Gemini less as a chatbot and more as the engine behind agents that plan, call tools, and carry out multi-step work over extended periods. With native function calling, a family of models tuned for different points in an agentic workflow, and infrastructure purpose built for running agents at scale, Gemini has become a serious option for teams building agentic AI systems, independent of which orchestration framework sits on top. This blog explains w

A framework can define how an agent plans, delegates, and hands off tasks, but the actual thinking, deciding which tool to call, interpreting a result, and figuring out the next step, comes from the underlying language model. OpenAI's models are among the most widely used for exactly this purpose, providing the reasoning and tool calling capability that sits at the center of most agentic AI systems, regardless of which orchestration framework wraps around them. This blog expl

Microsoft's agentic AI story used to be split across two separate projects, AutoGen for multi-agent experimentation and Semantic Kernel for enterprise grade orchestration. Microsoft Agent Framework brings those two lineages together into one framework, built by the same teams, aimed specifically at teams taking agents from prototype to production. This blog explains what Microsoft Agent Framework is, how it fits into agentic AI development, how implementation generally works,

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

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 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

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

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

Many “enterprise agents” begin as impressive demonstrations and end as abandoned chat windows. The demo can answer a policy question. It may even create a ticket. But it was built in the default environment, uses the maker’s connection, has no test set, exposes more tools than it needs, and goes directly from one person’s browser to the organization’s Teams app store. Nobody can state which users it serves, which systems it may change, what happens when an action fails, how m

"Design is a funny word. Some people think design means how it looks. But deeply, if you dig down, it’s how it works. To design something really well, you have to get it. You have to feel it in your gut. You have to understand what it’s about." The Bicycle for the Mind and the Broken Promise of Email In 1980, I came across a study published in Scientific American that changed the way I thought about human technology forever. The researchers were measuring the efficiency of lo

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