In this article, you will discover how to create an agent-native research canvas app that utilizes human-in-the-loop features, using tools like Langraph, CopilotKit, and Tavily. The topics covered include understanding AI agents, visualizing a LangGraph agent using LangGraph Studio, and designing the user interface with CopilotKit. You’ll learn how AI agents can perform research tasks ...
This article explains how to create an agent-native research canvas app using Langraph, CopilotKit, and Tavily. You’ll learn what AI agents are and how to build and visualize a LangGraph AI agent using LangGraph Studio. The tutorial covers creating a user interface with CopilotKit, emphasizing the integration of human-in-the-loop capabilities to enhance reliability. By the ...
Applied Labs, founded by veterans from Scale AI, recently secured $4.2 million in seed funding to enhance AI reliability for business operations and support tasks. Many companies struggle with unreliable AI, but Applied Labs aims to solve this by developing AI agents that efficiently handle complex workflows. The team combines advanced technology with a human-in-the-loop ...
The latest AI agents for contact centers are designed to engage in meaningful conversations and solve customer problems effectively. When combined with human agents, these AI systems can operate at their best. Instead of merely acting as a backup when AI struggles, humans should guide these AI agents as experienced colleagues. This approach reduces bottlenecks ...
CopilotKit is an open-source framework that allows developers to easily integrate AI copilots into their applications, offering a range of functionalities from simple chatbots to complex AI systems. Its latest feature, CoAgents, enables seamless collaboration with AI that understands context and adapts in real-time. Users can build dynamic applications with advanced features like real-time state ...
Most AI applications today rely on traditional chat interfaces, which can be limiting and create unnecessary friction. At LangChain, we are exploring a new type of AI called ambient agents. These agents listen for important signals and only engage with users when needed, reducing the need for constant conversations. We introduced LangGraph to facilitate this ...
Many AI applications today rely on traditional chat interfaces, which can create extra work for users and limit their efficiency. At LangChain, we are exploring a new approach with “ambient agents” that respond to relevant signals and only seek user input when necessary. This design minimizes interruptions and allows multiple agents to operate at once. ...
