AI Agents

Autonomous AI agent that decomposes complex goals into actionable sub-tasks, leveraging tools and internet access for self-directed execution.

ByteDance's AI agent builder platform supporting custom bot creation, plugin ecosystems, and multi-channel deployment for conversational AI applications.

Multi-agent orchestration framework that coordinates role-playing LLM agents through structured task decomposition and inter-agent communication.

Visual workflow platform for building LLM-powered applications with drag-and-drop pipeline design, RAG integration, and one-click deployment.

Production framework for building context-aware LLM applications with composable chains, agent orchestration, RAG pipelines, and tool-use abstractions.

Explore the evolution of AI-powered development, comparing autocomplete tools like Cursor with autonomous coding agents like Google Antigravity 2.0.

Learn how to configure custom rules and directives in Google Antigravity 2.0 to enforce coding conventions, tech stacks, and security boundaries.

Learn how Google Antigravity 2.0 uses closed-loop TDD to automate writing unit tests, running them in sandboxes, and self-correcting buggy code.

Discover how Google Antigravity 2.0 leverages Gemini's Context Caching to make codebase analysis highly cost-effective by caching static contexts.

Learn how Google Antigravity 2.0 coordinates specialized subagents to tackle complex repository research and automated testing tasks in parallel.

Learn how Google Antigravity 2.0 uses AST parsing and compiled feedback loops to safely apply multi-file refactoring tasks across complex codebases.

An in-depth look at Google Antigravity's zero-trust permission architecture, sandbox execution, and how it prevents LLM hallucinations from damaging your local filesystem.