Reading / 2026-05/2026-05-03t110027-getting-up-to-speed-on-multi-agent-systems-part-2-the
Getting Up to Speed on Multi-Agent Systems, Part 2: The Vocabulary
Breaks down the shared taxonomy across three MAS surveys — covering agent types, coordination structures, strategies, and internal components — so readers can quickly categorize and critically compare any paper in the field.
May 03, 2026 · tech · Christopher Meiklejohn
Topics
- multi-agent-systems
- ai-agents
- distributed-systems
- ai-research
- benchmarks
Cited by
- AI agents
AI agents are LLM-powered systems that plan, act, and iterate autonomously; active research and engineering practice reveal deep tensions between coordination complexity, reliability, tool design, and the human oversight they still require.
- Benchmarks
Benchmarks in multi-agent AI research measure coordination overhead, error propagation, and task performance, exposing how architectural choices translate into real costs across single- and multi-agent systems.
- Distributed systems
Distributed systems theory supplies the vocabulary and failure models that recurring engineering problems demand, from durable execution frameworks to multi-agent LLM coordination to merge queue consistency bugs.
- Multi-agent systems
LLM-based multi-agent systems coordinate multiple AI agents on decomposed tasks, but empirical work shows failure rates of 41–87%, with information synthesis rather than coordination being the core bottleneck.
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