AI Agent Memory Design: 4 Patterns for Context That Persists Beyond a Single Conversation
A practical breakdown of four memory architectures for AI agents — from in-session compression to episodic storage — with implementation code and the gotchas that only show up in production.
n8n × AI Agent Integration— Building Local Workflow Automation with Claude and Gemini
A comprehensive guide to integrating n8n with AI agents to build secure, locally-hosted business automation pipelines. Covers self-hosted setup, Claude/Gemini/OpenAI node integration, and three real-world workflow patterns: email classification, weekly reports, and SNS scheduling.
Accelerating AI Agent Development with Gemma 4
A practical guide to building AI agents with Google's Gemma 4. Covers model size selection, function calling, structured JSON output, multimodal agents, and parallel tool execution with working code.
Antigravity × Gemma 4: Building Production AI Agents with Local LLMs
A complete guide to running Gemma 4 in Antigravity and building production-grade AI agents. Covers model selection, Ollama setup, AgentKit 2.0 integration, and multi-agent scaling.
Building Next-Generation AI Agents on Antigravity with AgentKit 2.0
A comprehensive guide to building AI agents with AgentKit 2.0 in Antigravity. Covers the 16 specialized agents, the Orchestrator, Planning vs Fast mode, and practical implementation patterns.
AI Agent Memory Architecture: Designing Long-Term Memory
A systematic guide to giving AI agents long-term memory. Learn how to build a practical memory system combining vector databases, episodic memory, and semantic search.
Antigravity AI Agents × Apple Vision Pro: the New Development Paradigm for Spatial Computing
A comprehensive practical guide to using Antigravity AI agents for Apple Vision Pro app development. Learn how to automate visionOS builds with RealityKit, ARKit, and SwiftUI Scenes, apply spatial UI best practices, and ship to production with confidence.
Design Patterns and Operations for Autonomous AI Agent Systems
A systematic breakdown of AI agent design patterns for real-world use. Covers ReAct, Plan-and-Execute, Reflexion, Multi-Agent, and Human-in-the-Loop — with selection criteria and implementation tips for each.
Building Custom AI Agents with Antigravity and MCP — External API Integration
Learn how to build custom AI agents using Antigravity's MCP (Model Context Protocol) to integrate external REST APIs and Webhooks. This guide covers design, implementation, debugging, and production-ready patterns.
Harness Engineering: Building Stable AI Agents
Master the four pillars of harness engineering—constraint, information, verification, and correction—to build AI agents that improve automatically and maintain stability at scale.
Build an Auto-Monetization CI/CD Pipeline with Antigravity AI Agents — From Code Generation to Billing
Integrate Antigravity's AI agents into a CI/CD pipeline that automates content generation, deployment to Cloudflare Workers, Stripe billing monitoring, and performance optimization end-to-end.
NemoClaw × Antigravity Revenue Automation Pipeline — Automating Development Business Revenue with Multi-Agent Systems
Learn how to build revenue automation pipelines combining NemoClaw's enterprise AI agent platform with Antigravity's parallel development environment. Covers agent development, test automation, deployment, monitoring, and practical revenue models for development businesses.