More Agents Won't Speed Up Every Part of Your Pipeline — Designing the Parallel/Serial Line
Antigravity 2.0's parallel multi-agent execution is powerful, but adding agents doesn't make everything faster. Here's how I decide which work to parallelize and which to keep serial, derived from invariants and a dependency graph, with examples from running several sites as a solo developer.
How to Orchestrate Multiple Agents: Drawing the Line Between Parallel and Serial Work
Antigravity 2.0 brings true parallel execution across multiple agents. But making everything parallel does not make it faster. Which work should fan out in parallel, and which should stay serial? This is an orchestration design that does not fall apart, viewed through dependencies and contention.
Parallel or Keep It Serial: The Break-Even Point When Orchestrating Multiple Agents
Should you run agents in parallel or keep them serial? A simple way to estimate the break-even between coordination cost and saved wall-clock time, plus how I actually split parallel vs serial across four scheduled sites.
Bundling Nightly Tasks With agy Async Jobs — A fan-out, poll, join Design
The Go-based Antigravity CLI (agy) can detach jobs and run them asynchronously. Here is a fan-out, poll, join design for firing many long-running tasks at once, collecting their job IDs, and waiting for completion — drawn from an actual nightly batch.
Containing Failure in Antigravity Multi-Agent Systems: Three Boundaries That Stop Cascades
Antigravity multi-agent setups run beautifully in isolation but cascade in production, where one small failure drags the whole orchestration down. These notes organize the fix around three boundaries—layered control, trust separation, and observability with idempotency—down to the TOML and the correlation-ID wrapper.
Orchestrating Six iOS App Updates in Parallel with Antigravity's Main and Sub Agents
Running six iOS app updates in parallel — new resolutions, Firebase SPM migration, StoreKit 2, and ATT ordering — with Antigravity's Main + 6 Sub agents. The notes on what to parallelize and what to keep serial.
AI Agent Orchestration Design Patterns — Task Decomposition, Handoffs, and Loop Control
A practical look at the design challenges you encounter when actually running AI agents: task decomposition granularity, sub-agent handoff structures, and reliable loop termination.
Turning AI Agents into Products — Build Billable Automation Services with Antigravity
Learn how to package Antigravity's multi-agent capabilities into sellable automation services. Covers AgentKit 2.0 orchestration, usage-based billing, and three ready-to-sell agent service blueprints.
Production Multi-Agent Systems with Antigravity AgentKit 2.0: Patterns, Failure Modes, and What the Demos Don't Show
AgentKit 2.0 makes multi-agent systems look effortless in demos, but running them in production is a different problem. This guide covers Planning vs. Fast mode, three real orchestration patterns, and the failure modes — infinite loops, cost blowouts, prompt injection — that bite on day one.
AI Agent Orchestration: Designing and Implementing Multi-Agent Systems
A systematic breakdown of orchestration design patterns for multi-agent systems — covering agent coordination, task delegation, and feedback loops with practical code examples.
Antigravity Multi-Agent Design: 7 Common Pitfalls and How to Fix Them
Deep dive into 7 critical multi-agent design pitfalls: context sharing failures, loop detection misconfiguration, timeout issues, race conditions, credit inefficiency, error propagation, and async debugging. Includes observability patterns and production-ready templates.
Antigravity Multi-Agent Orchestration Guide: From Communication Errors to Production
Complete guide to designing and implementing multi-agent systems with Antigravity. Covers architecture patterns, communication error troubleshooting, and production stability.