What happened: Teams are splitting single monolithic agents into specialized sub-agents — a planner, a coder, a reviewer — coordinated by an orchestrator, instead of asking one agent to do everything in one long context.
Why it matters: A single agent handling a long, multi-skill task tends to lose the plot: context gets crowded, early mistakes compound, and it's hard to tell which part of a bad output came from which reasoning step. Smaller, focused agents are easier to evaluate individually.
Common patterns
- Orchestrator-worker: a lead agent breaks the task down and dispatches pieces to specialized workers, then assembles the result
- Peer review pairs: one agent produces work, a second critiques it against explicit criteria before it ships
- Pipeline handoff: fixed stages (research → draft → fact-check) where each agent only sees what it needs for its stage
Where it pays off
Long, multi-step tasks that genuinely need different skills — research plus writing plus code, for example — where a single context window would get overloaded and the failure modes of each step are easier to catch in isolation.
Where it's overkill
Single-turn tasks, or anything a well-scoped single agent handles in one pass. Coordination overhead — message passing, state handoff, redundant context — can exceed the benefit fast. Start with one agent; split only when you can name the specific failure it fixes.
Originally published on ayraix.com, practical AI for enterprise builders.
Top comments (1)
The coordination boundary matters more than the number of agents. A practical addition would be a table for ownership: which agent can mutate state, what receipt proves completion, and how a later agent distinguishes an unfinished action from an uncertain one.