Author(s): Shrashti Singhal

Originally published on Towards AI.

Everyone wants an AI team. Most tasks want one strong agent. This is a field guide to the orchestration patterns that actually work, the economics nobody mentions in the demos, and the discipline of knowing when a crew is the wrong answer. Part sixteen of my ongoing agentic AI series.

The most instructive multi-agent system I ever saw was one a team decommissioned.

After the introduction, the article explains that multi-agent systems don’t primarily improve intelligence via “specialists,” but through context isolation—subagents handle subtasks within separate context windows, which helps when the task’s working set exceeds a single window. It then presents a catalog of core orchestration topologies (orchestrator-workers, pipeline, router/handoff, debate+judge, blackboard shared workspace, and hierarchy), followed by the economics: token and effort multipliers are much larger than intuition suggests, with costs compounding across agents and latency not necessarily improving unless synthesis is the bottleneck. The author lays out clear “when not to use” criteria—most tasks fit in one window, deep work is sequential, shared mutable state and consistent voice matter, and tight budgets favor a single strong agent. Finally, it offers engineering guidance for briefs, summaries, isolation, checkpointing, and end-to-end tracing/evals, shares case studies (notably the “90%” benchmark and why routing can double as a security boundary), and ends with a decision framework plus a practical one-week plan to compare a multi-agent approach against a strong single-agent baseline before committing.

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Published via Towards AI