Roo Code: how to review code when a multi-agent orchestrator plans and executes in parallel sub-agents

2026-04-29 · 5 min read · ZenCode

Roo Code is an open-source VS Code extension — originally a fork of Cline — that has evolved into one of the most capable agentic coding environments available inside an editor. Its defining feature beyond standard agent capabilities is the Boomerang task system: an orchestrator agent (typically running in Architect mode) can break a task into subtasks, spawn specialized sub-agents to handle each one, and collect their results back into a synthesized output. The orchestrator plans and coordinates; the sub-agents write and edit code; the parent task receives the merged result.

This multi-agent pipeline changes the review problem in a fundamental way. With a single-agent tool, you review one output from one execution trace. With Roo Code’s orchestrator model, the code that lands in your codebase was produced by multiple agents working in sequence, each with its own context window, each without visibility into what the others were doing. The gap between the plan you approved and the code that landed is wider than in any single-agent tool — and the review traps that fill that gap are specific to this architecture.

The three Roo Code attention traps

1. Orchestrator plan approval as code review

When you invoke a Boomerang task, the orchestrator agent produces a plan: a structured breakdown of the goal into subtasks, each assigned to a sub-agent, with a description of what each sub-agent is expected to do. You see this plan before any code is written, and in most workflows you approve it before the sub-agents begin. The plan is typically clear, coherent, and reads like a correct high-level design for the feature.

That approval feels like a review. You read the design, it matched your intent, you signed off. But the plan operates at intent level — what to build — not at code level — what was actually written. Each sub-agent takes the orchestrator’s description of its subtask and implements it independently, making implementation decisions the plan never specified: which abstraction to use, what to name the interface, whether to handle a particular edge case, where to place the new logic relative to existing code. The plan you approved doesn’t determine those decisions. The sub-agent does.

The fix is to treat the orchestrator plan as a task brief, not a review artifact. Set a rule before starting any Boomerang session: the review happens on the sub-agents’ diffs individually, not on the orchestrator’s summary of what they did. When the session completes, open the git diff for each sub-agent’s work in sequence, before reading the orchestrator’s synthesis. The plan tells you what was intended; the diffs tell you what was done.

2. Sub-agent isolation as quality insurance

Each Roo Code sub-agent runs in its own context window with a scoped view of the codebase — specifically the files relevant to its assigned subtask. This isolation is architecturally intentional: it prevents one sub-agent’s assumptions from contaminating another’s context, keeps individual tasks focused, and avoids the context-bloat that degrades single-agent performance on large codebases. In practice, it means each sub-agent produces output that is internally consistent and logically correct within its scope.

The isolation that makes each sub-agent’s output clean also means no sub-agent has visibility into what the others changed. Two sub-agents can write code that compiles and passes tests in isolation but conflicts at integration. One sub-agent defines a shared data structure one way; another assumes a different shape for the same structure. One sub-agent adds a new function to a utility module; another independently adds a function with the same name to the same module. One sub-agent modifies authentication middleware behavior; another adds a new route assuming the old behavior. None of these conflicts are visible to any individual sub-agent — and the orchestrator’s synthesis step collects results, it doesn’t perform a semantic cross-agent consistency check.

The fix is to make integration the explicit focus of your review rather than delegating it to the orchestrator summary. After reading individual sub-agent diffs, do one cross-agent pass: look for shared modules touched by more than one sub-agent, look for interface definitions that appear in multiple diffs, and check that any shared state or data structures are handled consistently. This pass takes five minutes and catches the class of bugs that sub-agent isolation structurally cannot prevent.

3. Context compression through long orchestration sessions

Roo Code includes intelligent context window management: when a conversation approaches the model’s context limit, prior messages are summarized and compressed so the session can continue. In a long Boomerang task — orchestrator planning followed by multiple sub-agent turns followed by synthesis — the context window fills faster than in a single-agent session, and compression happens earlier. The orchestrator’s summary of the compressed context is accurate about what was done but loses something specific: the constraint reasoning from the early planning phase.

When you set a constraint in the original task — “don’t change the public API”, “keep backward compatibility with the existing config format”, “avoid adding any new dependencies” — that constraint is in the first few messages of the orchestrator’s context. Across a long multi-agent session, those messages get compressed. The compression captures that a plan was made and subtasks were defined, but the original constraint wording and its reasoning tend not to survive verbatim. A sub-agent in the fourth turn of a long session may be working from a context that summarized your constraint as “maintain compatibility” rather than the specific thing you actually meant by it.

The fix is to verify constraints explicitly against the final output, not against the agent’s self-report of how it handled them. After the session completes, re-read your original task specification — the exact words you used to define the constraints — and check each constraint against the actual diff. Don’t ask the orchestrator whether it maintained backward compatibility; check the diff for any change to the public interface. The constraint verification takes two minutes and catches the cases where compression substituted a general version of the constraint for the specific one.

How this differs from similar tools

Cline (#8) is Roo Code’s upstream. In Cline, a single agent executes the full task with per-tool-use approval prompts. The trap is approval fatigue: clicking through 40 approvals turns a conscious review into a reflex. Roo Code’s Boomerang system changes the cadence: you approve the plan upfront and the sub-agents run with less interruption, which reduces approval fatigue but creates the plan-as-review trap instead. Both tools create attention problems at the approval boundary; the boundary is in a different place.

Plandex (#44) also operates on a plan-then-execute model for multi-file changes. Plandex uses a single agent with a dedicated planning pass; Roo Code uses multiple specialized agents. The isolation-as-quality-insurance trap is more acute in Roo Code because the sub-agents are genuinely separate contexts, not a single agent revisiting its own plan.

OpenHands (#40) runs a single autonomous agent with a sandbox environment. It has a similar plan-approval moment (the task description you provide) but no orchestrator/sub-agent separation. The context compression trap applies to OpenHands in long sessions, but the cross-agent conflict trap is unique to Roo Code’s multi-agent architecture.

Aider (#9) works in a terminal with explicit file scoping per session. There is no orchestrator layer — Aider always operates as a single agent on the files you add to its context. The review model is simpler: one diff, one execution trace. Roo Code’s multi-agent architecture introduces structural complexity that Aider deliberately avoids.

The base review checklist (#22) applies to each sub-agent’s diff individually. The Roo Code-specific layer adds the cross-agent integration pass and the constraint verification step on top of the base checklist.

What Roo Code gets right

The Boomerang system solves a real problem: large tasks overwhelm single-agent context windows and produce degraded output. By splitting a complex feature into focused subtasks handled by scoped agents, Roo Code often produces individually cleaner code per subtask than a single agent handling the entire feature in one pass. The mode system — Architect for planning, Code for implementation, Debug for diagnosis — lets each agent operate with a prompt optimized for its function rather than asking one agent to context-switch between roles. For large refactors, multi-file feature additions, and complex debugging tasks, the orchestrator model genuinely improves output quality at the individual subtask level.

The traps above are not arguments against using the orchestrator model. They are arguments for matching your review process to the architecture that produced the code. Single-agent output requires a single review pass on a single execution trace. Multi-agent output requires per-agent diff reading, an explicit cross-agent integration check, and constraint verification against the original task spec. The additional review steps are not overhead — they are the minimum required to cover the gaps that the architecture structurally introduces.