Four Agent Orchestration Patterns That Actually Ship
These four patterns solve the reliability ceiling that makes single-pass agent output untrustworthy for production. They give developers composable, low-cost primitives for multi-perspective review, false-positive suppression, and exhaustive search — the difference between a demo and something you can ship.
Single-agent passes miss too much. Quality Gate runs parallel dimension-specific reviews (tech, style, readability) on cheap models, then feeds all feedback into one synthesizer agent that resolves conflicts and revises. Batch Production pipelines the same topic through separate audience tracks — beginner, experienced dev, manager — each with its own research, outline, and draft stages, none blocking the others.
Review-and-Verify tackles false positives head-on: four parallel reviewers produce structured findings, JS deduplication removes overlaps, then each unique finding gets its own agent in a pipeline that tries to refute it; only confirmed issues survive. Loop-Until-Dry handles open-ended investigations like hardcoded-path hunting by cycling through strategies until two consecutive rounds yield nothing new, feeding known results back to prevent repeats.
The patterns compose freely — Review-and-Verify already combines parallel and pipeline — and the decision heuristic is plain: independent subtasks use parallel, same multi-step flows use pipeline, quality checks combine both, verification adds dedup and refutation, and unknown-total tasks loop.
The false-positive problem in agent code review is structural, not a prompt-tuning issue — a single reviewer has no incentive to doubt itself, so an adversarial verification step is the only way to suppress noise.
Loop-Until-Dry's 'two consecutive dry rounds' stopping condition is a pragmatic convergence heuristic that costs almost nothing and prevents premature termination from a single unlucky round.
Offloading deduplication to deterministic JS instead of an LLM call is a design instinct that keeps cost and latency low while avoiding nondeterministic misses — a pattern worth applying anywhere an agent pipeline touches structured data.
The Batch Production pattern exposes a subtle pipeline-vs-parallel trap: parallel would run all stages for all audiences simultaneously, losing per-audience context; pipeline preserves the sequential dependency within each track while still running tracks concurrently.