AI Agents

The use of artificial intelligence (AI) in financial markets has grown rapidly, with large language models (LLMs) increasingly applied to equity analysis, portfolio management, and stock selection. BlackRock research team proposed AlphaAgents for investment research. The AlphaAgents framework leverages the power of multi-agent systems to improve investment outcomes, reduce cognitive bias, and enhance the decision-making…

In this tutorial, we walk through building an advanced AI agent using the mcp-agent and Gemini. We start by setting up a robust environment with all the necessary dependencies and then implement an MCP tool server that provides structured services such as web search, data analysis, code execution, and weather information. By wiring these tools…

Amazon Web Services (AWS) has launched the Amazon Bedrock AgentCore Gateway, a transformative managed service designed to simplify and scale AI agent-to-tool integrations for enterprises. As organizations seek to leverage AI agents in increasingly complex environments with hundreds of tools and services, the Gateway addresses critical pain points: interoperability, security, tool discovery, and infrastructure management—all…

In this tutorial, we implement an AI agent pipeline using Parsl, leveraging its parallel execution capabilities to run multiple computational tasks as independent Python apps. We configure a local ThreadPoolExecutor for concurrency, define specialized tools such as Fibonacci computation, prime counting, keyword extraction, and simulated API calls, and coordinate them through a lightweight planner that…

Are AI agents getting too expensive to use at scale? It’s a hot topic in the world of artificial intelligence, and a fresh study from the OPPO AI Agent Team finally puts some real numbers—and solutions—on the table. Today’s most impressive AI agents can tackle massive, multi-step tasks using the reasoning power of large language…

Guardrails AI has announced the general availability of Snowglobe, a breakthrough simulation engine designed to address one of the thorniest challenges in conversational AI: reliably testing AI Agents/chatbots at scale before they ever reach production. Tackling an Infinite Input Space with Simulation Evaluating AI agents—especially open-ended chatbots—has traditionally required painstaking manual scenario creation. Developers might…

In this tutorial, we explore how we can build a fully functional conversational AI agent from scratch using the Pipecat framework. We walk through setting up a Pipeline that links together custom FrameProcessor classes, one for handling user input and generating responses with a HuggingFace model, and another for formatting and displaying the conversation flow….