Design Multi-Agent Architectures for Robust Agentic Systems

Research on multi-agent systems (MAS) surged from ~820 papers in 2024 to over 2,500 in 2025, but many agentic systems still fail in production because teams focus on prompts rather than architecture. The author identifies a “prompting fallacy”: model and prompt tweaks alone cannot fix system-level coordination failures. Common collaboration topologies are reviewed—supervisor-based (centralized control, good for sequential tasks but a bottleneck), blackboard-style (shared memory for creative iteration), peer-to-peer (direct exchange, good for exploration but prone to drift), and swarms (parallel coverage, useful for research and creative tasks but can cause token-cost explosion and require consolidation). Hybrid patterns—fast specialists in parallel with a slower aggregator—often work best. Models should be “hired” into roles: decoder-only models for generation and planning, encoder-only for analysis and retrieval, mixture-of-experts for selective high capability, and reasoning models for deliberate checking. The article stresses that collaborative scaling differs from neural scaling: adding agents increases coordination costs and can plateau or collapse performance depending on topology, communication overhead, and memory. The key takeaway for practitioners: prioritize organizational design—patterns, role assignment, and scaling limits—over chasing better prompts. Agentic performance is an architectural outcome, not a prompting problem.