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Google Launches Antigravity 2.0 at I/O 2026: A Standalone Agent-First Platform with CLI, SDK, Managed Execution, and Enterprise Support

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Google used its I/O 2026 developer keynote to ship a meaningful architectural shift in how it packages AI-assisted development. The company announced Google Antigravity 2.0 — a standalone desktop application built entirely around agent orchestration alongside an Antigravity CLI, an Antigravity SDK, Managed Agents in the Gemini API, and enterprise support through the Gemini Enterprise […]

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Best Enterprise Level Agentic AI Platforms for 2026

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In 2026, enterprise agentic AI has moved from pilot budgets to production commitments. Salesforce is closing Agentforce deals at 29,000 since launch with $800M ARR. Microsoft Copilot Studio has 160,000 organizations running 400,000+ custom agents. ServiceNow has restructured its entire commercial model around autonomous AI tiers. The question is no longer whether to deploy —

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How to Build an Advanced Agentic AI System with Planning, Tool Calling, Memory, and Self-Critique Using OpenAI API

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In this tutorial, we build an advanced agentic AI system using the OpenAI API and a hidden terminal prompt for the API key. We design the agent as a small pipeline of specialized roles: planner, tool-using executor, and critic, so that we can separate strategy, action, and quality control. We also integrate structured tools (calculator,

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Vercel Labs Introduces Zero, a Systems Programming Language Designed So AI Agents Can Read, Repair, and Ship Native Programs

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Most programming languages were designed for humans who read error messages, interpret warnings, and manually trace through stack output to fix bugs. AI agents do none of those things well. They work better with structured data: predictable tokens, stable codes, and machine-parseable repair hints. That gap is what Vercel Labs is trying to close by

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Meet LiteLLM Agent Platform: A Kubernetes-Based, Self-Hosted Infrastructure Layer for Isolated Agent Sandboxes and Persistent Session Management in Production

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Running AI agents in a local script is straightforward. Running them reliably in production across teams, across restarts, with isolated environments per context is a different problem entirely. BerriAI, the company behind the LiteLLM AI Gateway, is now open-sourcing a purpose-built answer to that problem: the LiteLLM Agent Platform. The platform is described as a

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How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions, and AI Context

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In this tutorial, we explore how to use Repowise to build repository-level intelligence for the itsdangerous Python project in a practical and reproducible way. We start with an already cloned repository, configure Repowise using the available LLM credentials, and initialize its indexing pipeline. We then inspect the generated .repowise artifacts, analyze the repository graph with

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How to Build an MCP Style Routed AI Agent System with Dynamic Tool Exposure Planning, Execution, and Context Injection

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In this tutorial, we build a fully functional MCP-style routed agent system from scratch, combining tool discovery, intelligent routing, structured planning, and execution into a single cohesive workflow. We start by setting up a modular tool server that exposes capabilities such as web search, local retrieval, dataset loading, and Python execution, all defined through structured

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Best 5 Engineering Analytics Platforms of 2026

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Engineering organizations are operating in an environment that is significantly more complex than it was even a few years ago. Modern software delivery now spans distributed cloud infrastructure, platform engineering initiatives, AI-assisted development workflows, microservices architectures, globally distributed teams, and increasingly fragmented operational tooling ecosystems. As complexity grows, engineering leaders are realizing that traditional reporting

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Claude Code’s product lead talks usage limits, transparency, and the “lean harness”

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SAN FRANCISCO—Amid an ever-expanding array of surfaces, growing demand for tokens and compute, and a rapidly evolving user base, Anthropic doesn’t have a long-term road map for Claude Code. However, it’s betting that such a plan would be rendered moot by improvements in model capabilities and new signals from developers on how best to use

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Best AI Agents for Software Development Ranked: A Benchmark-Driven Look at the Current Field

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The AI coding agent market looks almost unrecognizable compared to 2024 or even early 2025. What started as inline autocomplete has evolved into fully autonomous systems that read GitHub issues, navigate multi-file codebases, write fixes, execute tests, and open pull requests — without a human typing a single line of code. By early 2026, roughly

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