This article explores the Model Context Protocol (MCP), an open protocol designed to standardize AI interaction with tools and data, addressing the fragmentation in AI agent ecosystems. It details current use cases, future possibilities, and challenges in adopting MCP.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
The article discusses the OVON agentic framework for mitigating hallucinations in Large Language Models (LLMs). It explains the structured, collaborative pipeline involving front-end and reviewer agents, the use of 'Conversation Envelopes' and 'Whispers' for efficient data exchange, and novel KPIs for measuring success. The article also addresses future directions and the importance of trust in AI systems.
Model Context Protocol (MCP) is a bridging technology for AI agents and APIs. It standardizes API access for AI agents, making it a universal method for AI agents to trigger external actions.
Browser Use is a library that enables AI agents to interact with web browsers, making websites accessible for automated tasks. It includes features for browser automation, agent memory, and various demos showcasing its capabilities.
AGNTCY is building the Internet of Agents to be accessible for all, focusing on innovation, development, and maintenance of software components and services for agentic workflows and multi-agent applications.
Discover:
1. Agent directory
- Registry for agent publishing and discovery
- Tracks reputation and quality
2. Open agent schema framework
- Standard metadata format for agent capabilities
- Verification for agent providers
- Specification at github.com/agntcy/oasf
Compose:
1. Agent connect protocol and SDK
- Standardized agent communication across frameworks
- Manages message passing, state, and context
- Specification at github.com/agntcy/acp-spec
What could these look like in action? A developer can find suitable agents in the directory (using OASF) and enable their communication with the agent connect protocol, regardless of frameworks.
AGNTCY is an open-source collective building infrastructure for AI agents to collaborate, led by Cisco, LangChain, Galileo, and other contributors. The initiative aims to create an open, interoperable foundation for agentic AI systems to work together seamlessly across different frameworks and vendors.
AGNTCY plans to develop key components such as an agent directory, an open agent schema framework, and an agent connect protocol to facilitate this interoperability.
A consortium of Cisco, Galileo, and LangChain proposes an open, scalable way to connect and coordinate AI across different frameworks, vendors, and infrastructure to manage the rapid evolution of AI agents.
LlamaIndex, founded by former Uber research scientists, offers a cloud service for building custom agents over unstructured data. The service includes features like data connectors, utilities for transforming unstructured data, and role-based access control.
Biomedical researchers face significant challenges due to the complexity of topics and the need for trans-disciplinary approaches. The AI Co-Scientist system, powered by Gemini 2.0, aims to accelerate scientific discovery by generating, debating, and evolving hypotheses. It integrates specialized agents to interact with scientists, manage tasks, and allocate resources effectively.
The AI Co-Scientist integrates four key components:
- Natural Language Interface: Allows scientists to interact with the system.
- Asynchronous Task Framework: Implements a multi-agent system for continuous execution.
- Supervisor Agent: Manages the task queue and assigns specialized agents.
- Persistent Context Memory: Stores and retrieves agent and system states.
The system includes various specialized agents:
- Generation Agent: Initiates research and creates hypotheses.
- Reflection Agent: Reviews hypothesis quality and correctness.
- Ranking Agent: Prioritizes hypotheses using an Elo-based tournament system.
- Proximity Agent: Computes similarity graphs for hypothesis clustering.
- Evolution Agent: Refines top-ranked hypotheses.
- Meta-review Agent: Synthesizes insights from reviews and debates.
