Should you use AutoML or build custom models for your forecasting needs? At Foresight AI, we faced this dilemma head-on. This blog unpacks our decision-making process, real-world implementation on Vertex AI, and why a hybrid forecasting strategy powered by automation, feature engineering, and MLOps is the future.

AI Resolve X is SquareShift’s generative AI-powered platform built to tackle today’s overwhelming cybersecurity threats. With the surge in CVEs and analyst burnout, AI Resolve X streamlines vulnerability management by eliminating false positives, accelerating patch cycles, and enabling natural language querying through its built-in VMBot. Discover how enterprises can move from reactive defense to proactive risk mitigation while saving millions and retaining top talent.

Cloud FinOps is more than a cost-cutting tool; it’s a cultural and operational shift that enables organizations to bring accountability, visibility, and continuous optimization to cloud spending. In this first part of our five-part series, we break down the foundations of Cloud FinOps and share actionable steps to help your teams take charge of cloud financial management.

Discover how to create a powerful Slack bot for Google AgentSpace that transforms how teams interact with data. This hands-on guide walks you through secure setup, conversational triggers, and cross-functional automation, perfect for developers and productivity leaders looking to boost workplace efficiency.

Extend your AI power with Custom AI Agents in Google AgentSpace. Learn how to design, build, and deploy intelligent agents tailored to your unique workflows improving productivity, streamlining processes, and securely connecting your enterprise systems with cutting-edge AI. From choosing the right model to enabling multi-agent collaboration and measuring performance, this comprehensive guide gives you everything you need to bring intelligence to the edge of your business.

Google AgentSpace is transforming enterprise productivity by seamlessly connecting Google Workspace with external platforms like Jira and SharePoint. This comprehensive guide explores how AgentSpace’s third-party connectors, intelligent data search, and powerful Actions enable secure, AI-powered access to all your data,while respecting existing permissions through native ACL support. Discover how easy it is to set up, sync, and automate workflows, and see why AgentSpace is th

Unlock the true potential of your AI development with seamless LangChain MCP Integration. In this comprehensive guide, discover how to build powerful multi-tool AI agents by combining LangChain with Anthropic MCP tools. Learn to orchestrate multiple specialized MCP servers, dynamically aggregate tools, and supercharge your agents with intelligent prompts, resources, and executable actions. Dive in now to revolutionize your AI workflows and create agents that are smarter, fast

In today’s AI-first enterprise landscape, businesses are sitting on mountains of valuable data scattered across documents, emails, databases, calendars, and collaboration tools like Google Workspace. Yet, the true challenge isn't storing all this information it’s finding it, understanding it, and acting on it quickly. That’s where Google AgentSpace steps in as a transformative solution.

In the fast-evolving world of generative AI, it's no longer enough for an AI to just create content it must also be able to critique, learn, and improve. This blog explores the concept of a "Reflection Agent", a powerful construct that enables AI to refine its own outputs through feedback loops. Specifically, it focuses on building a self-correcting AI Tweet Generator using LangChain and LangGraph, with Google’s Gemini 2.0 Flash as the underlying large language model.

Google AgentSpace is set to revolutionize the future of work by offering a smarter and simpler approach to AI agents and data interaction. Unlike many current AI agent systems that rely on Retrieval-Augmented Generation (RAG) and require complex setup and constant adjustments , AgentSpace streamlines the process. Users can connect their tools once, ask questions in plain English, and receive instant answers.

This blog post provides a comprehensive guide on building a smart Elasticsearch agent that facilitates natural language querying of data, eliminating the need for users to master Elasticsearch's complex DSL. The agent's workflow, orchestrated by LangGraph and powered by Google's Gemini model for natural language understanding and query generation, involves normalizing user queries, translating them into Elasticsearch DSL, executing the search, and summarizing the results for

Best Practices for Building Agent Apps with Vertex AI

Empowering Your Agent with Knowledge: Datastores in Vertex AI Agent Builder

Understanding Swarms: A Collaborative Agentic Framework for AI

Supercharging Your Healthcare Agent: API Integration with Vertex AI Agent Builder

Building a Simple Healthcare Appointment Scheduling Agent with Vertex AI Agent Builder

AI development often feels like an uphill battle, requiring deep coding expertise and machine learning skills.