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 write custom rules for Google Antigravity 2.0: where agents.md and .rules files go, how rule resolution works, and copy-paste configs for strict mode, allow lists, turbo mode, and auto-retry.

How Google Antigravity 2.0 runs test-driven development in a sandbox: writing spec tests first, executing them, and self-correcting failing code until the suite is green — no manual review of edge cases needed.

Cut Google Antigravity token costs by up to 90% using Gemini context (prompt) caching: how the cache works, TTL management, and when caching makes agentic coding dramatically cheaper on large repos.

How to orchestrate subagents in Google Antigravity 2.0: define_subagent and invoke_subagent, isolated branch workspaces, and how parallel background threads keep your main context clean.

How Google Antigravity 2.0 performs multi-file refactoring: AST-based call-graph mapping, surgical chunk replacements, and compiler feedback loops that keep cross-module renames from breaking your build.

A deep dive into Google Antigravity 2.0's sandbox and zero-trust permission model: how file edits, shell commands, and network access are isolated, and how prompt-injection risks are mitigated.