Claim
Four subagent patterns are settling as standard for agentic architectures: Inline Tool, Fan-Out, Agent Pool, and Teams. Each adds control surface and debugging surface at a real cost. The trade is not a free upgrade.
Mechanism
Inline Tool: a single sequential agent calls tools in order. Simple to trace, slow for independent tasks. Fan-Out: a coordinator dispatches parallel subtasks to specialist agents and merges results. Faster for independent work, but merge logic and error propagation require explicit design. Agent Pool: persistent workers pick tasks from a queue, enabling load balancing. Adds queue-management complexity. Teams: specialized agents coordinate with each other via routing and context-passing logic. Highest control surface, highest debugging surface.
Conditions
Holds when: The task has independent subtasks (Fan-Out), persistent workload (Pool), or requires specialized coordination (Teams). Inline Tool is right when tasks are sequential and debugging simplicity matters more than throughput.
Fails when: Fan-Out is chosen for dependent tasks where merge logic cannot be cleanly defined. Teams patterns are adopted before simpler patterns have been exhausted.
Evidence
Phil Schmid's analysis of production agentic systems, published May 5, 2026, drawing on patterns observed across deployed LLM applications.
Signals
- Fan-Out patterns show 3-5x throughput improvement on independent subtasks vs Inline when tasks are truly parallelizable
- Debugging time rises with each pattern layer; trace IDs and error budgets become required infrastructure
- Agent Pool patterns stabilize when task arrival rate exceeds single-agent processing capacity
Counter-evidence
Pattern taxonomy is descriptive, not prescriptive. Real systems often mix patterns within a single workflow. Overhead of more complex patterns can exceed benefit for infrequent or short-duration tasks. Patterns are still evolving.
Cross-references
- Agents come in three classes, tag each loop or under-resource it: complementary taxonomy of agent types
- Agents work when treated as a team, not a single super-tool: Teams framing from the GTM application side
- GTM automation shifted from triggered batch automations to continuous-monitoring agents in under 18 months. The agent decides when to act.: continuous monitoring is Fan-Out applied to GTM signal processing