Multi-agent systems.
When splitting work across several agents helps, what it costs, and the failure modes the published research keeps finding.
Questions
When should you use multiple AI agents instead of one?
Start with one agent. Split into several when the work is broad and parallel, or one agent has too many tools. What the published evidence says on both sides.
How do AI agents communicate and share state?
Inside one application, agents share a state object or hand off messages. Across systems, they use protocols such as A2A. What to share, and why it matters.
Why do multi-agent AI systems fail?
A study of 1,600+ traces found 14 failure modes in three groups: system design, inter-agent misalignment and task verification. Causes and mitigations.
How do you run several AI coding agents in parallel without conflicts?
Give each coding agent its own git worktree and branch, split work along file boundaries, agree interfaces first, and merge through normal review and tests.
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