Perplexity MCP prevents AI agent hallucinations by providing real-time web research with citations from its search engine. It allows agents like Claude or Cursor to answer questions by researching the web, ensuring answers are backed by current, verifiable sources.
This article discusses how building an MCP server ecosystem transformed AI agents, enabling them to move beyond isolated language processing. This solution allowed agents to search the web, query databases, and interact with various internal tools, overcoming their initial limitations.
This content addresses the complexities of AI agents using external tools, highlighting the often-skipped steps of identifying capabilities, providers, costs, and credentials. It introduces Rhumb, which uses "Index" and "Resolve" to manage these steps, demonstrating with cURL examples for preflight web search resolution and cost estimation.
This article details how splitting a complex task for an AI agent into multiple roles drastically reduced execution time and token cost. Initially, a single agent took 20 minutes to pick five topics, but splitting the task across three agents completed it in 3 minutes with a 60% cost reduction. The key was to separate the WebSearch function from the judging agent to streamline the pipeline.
This post details how to set up the Exa integration in Strands Agents, exploring its core tools. It also presents real-world use cases demonstrating how agents leverage web search for multi-step tasks.
OpenClaw v2026.3.28 now supports native web search through Grok's (xAI) infrastructure and integrates MiniMax as a new image generation provider alongside DALL-E. The update also expands credential recognition for various AI models and refines configuration validation.