WAVES — Workers · Aggregate · Verify · Extend (Cursor)

Run wave-based orchestration inside one local Cursor session. A wave is a bounded round of isolated agents working in parallel, then a round that verifies what came back, then a deliberate decision to build on it — not an open-ended loop. You are the orchestrator: you discover, decompose the goal into independent slices, fan them out to parallel workers (the Task tool, run in the background = "Multitask Mode"), read each worker's structured handoff, verify it, and synthesize one deliverable. Workers are isolated and return exactly one handoff.

The shape of every wave — WAVE:

• W — Workers. Fan out isolated workers across disjoint slices (the bounded

parallel round).

• A — Aggregate. Wait for all of them and merge their structured handoffs at

the synthesize barrier.

• V — Verify. The moat: check the evidence behind each handoff before you

trust it.

• E — Extend. Decide — deliberately — whether to launch another wave, or stop.

A loop doesn't know when to stop; a wave does, because verification is the stop function. (Invoked explicitly with /waves: a run spawns more agents than usual, so it's opt-in, not auto-triggered.)

This is the local, zero-setup adaptation of the Cursor team's orchestrate plugin (which spawns cloud agents over the Cursor SDK). Same principles — planners plan, workers hand off up, no cross-talk — without any of that cloud setup. For heavyweight cloud fan-out, see "Escalating" below.

When to use

• A large goal that splits into independent slices (research areas, data

chunks, files/modules, audit dimensions).

• The work is mostly read / research / analysis — the safest thing to

parallelize locally (see "Parallel writes" for why).

• A single linear pass would be slow and you want real speedup from concurrency.

When to skip

• Small or linear tasks (just do them — fan-out overhead isn't worth it).
• Work needing tight back-and-forth or shared mutable state between steps.
• Parallel edits to the same files — local workers share one filesystem.

Core principles

Adapted from orchestrate. These keep the run converging without coordination.

1. Orchestrator plans and synthesizes; it does not do the heavy lifting.

Discovering, decomposing, reading handoffs, and writing the final deliverable are your job. The bulk reading/research/analysis is delegated to workers.

1. Workers are isolated. A subagent has **no access to the user's message,

your prior steps, or sibling workers.** Every worker prompt must be fully self-contained: goal context, its exact slice, where to look, what to return.

1. One worker, one slice, one handoff. The worker's final message is the

only thing you read back. Define its exact shape (see references/handoff-format.md).

1. Parallelism is for reading, not writing. Local workers share the

workspace; concurrent writes to overlapping paths corrupt each other.

1. Continuous motion. A handoff can reveal new work. Spawn a second wave

(driven by a handoff gap or a new user request). Stop only when every slice is terminal and the synthesis is complete.

1. Verify before you trust. A worker's Status: success is a claim, not

evidence. Check each handoff against something re-openable before folding it into the synthesis. See "Verification" below and references/verification.md.

The loop

Track it with TodoWrite so the waves stay visible.

Step 0 — Discover first (serial, in the main session)

Do not fan out blind. Spend a few cheap tool calls in the main session to learn the shape of the problem: list the directory, read the schema, sample the data, confirm coverage/size. This is what tells you the natural decomposition (how many chunks, which workstreams). Skipping discovery produces overlapping or mis-sized slices.

Step 0.5 — Stage the data (when it's remote or messy)

explore workers are read-only with no MCP or internet access, so they can't reach remote hosts, databases, or networked sources. If the source is remote or wrapped in noise, the orchestrator must stage clean inputs before fanning out:

• Pull remote → local. SSH/rsync/export the relevant data to a local

scratch dir so read-only workers can read it (e.g. query a remote SQLite read-only, export the rows, rsync the markdown).

• Clean + normalize once, centrally. Strip wrappers, boilerplate, and binary

blobs (base64, logs); fix timestamps. Doing this once beats making every worker re-derive it (and keeps noise out of their context).

• Pre-chunk for the workers. Split into the exact per-worker files/ranges so

each prompt can point at one path.

• Verify before you spawn. Print counts and per-slice bounds; confirm the

partition sums to the total (e.g. 8 chunks × ~388 = 3,097) so no slice is a silent blind spot. Fix anomalies (bad sort, dups) centrally, then re-check. (Details: references/verification.md §1.)

In practice this serial prep is often the largest phase; the parallel fan-out is fast once inputs are clean.

Step 0.7 — Triage: classify each slice (worker type + verification tier)

Before fanning out, classify each slice on two axes — this is the classify-and-act pattern, routing the right work to the right handler:

• Worker type — read-only (explore) / web-research (generalPurpose) /

shell / competing-attempt (best-of-n-runner) / specialized review (bugbot, security-review). (See the table under "Choosing subagent_type".)

• Verification tier — how much checking the slice's stakes justify:

auto-accept (low-stakes, corroborated) → single verifier (medium) → multi-model panel (high-stakes) → debate (contested, no ground truth). Spend the verification budget where a wrong claim is expensive, not uniformly.

Step 1 — Decompose into independent slices

Split along whichever axis makes slices independent:

• Data chunks — partition large data and give each worker a disjoint range

(e.g. messages 1–500, 501–1000, …).

• Workstreams — separate research/analysis areas (e.g. "research voice

stack", "research the Notion SDK", "audit auth code").

• Files / modules — disjoint, non-overlapping path sets.

Each slice needs: a one-line scope, what to look at, and a defined output. For a big wave (roughly 5+ workers), state the decomposition plan to the user before spawning so they can redirect cheaply. If you have many slices, fan out in waves (launch a batch, let it complete, launch the next) rather than all at once, so you stay within practical concurrency limits.

Step 2 — Fan out in parallel

Send one message with multiple `Task` tool calls — that is what makes them run concurrently. Set run_in_background: true on each (Multitask Mode). Pick subagent_type per slice (table below). Give each a 3-5 word description and a self-contained prompt ending with the required handoff format.

Then end your turn. You are notified as each background worker completes — do not AwaitShell, poll, or read output files in a loop. The Task call itself confirms the launch.

Step 3 — Collect and synthesize

As handoffs arrive, read each one: note Status, extract Key findings, and mine Open questions / Suggested follow-ups — each bullet may become a second-wave task. Reconcile conflicts across workers.

Don't trust a handoff because it says `success`. Verify each finding's evidence (cited file:line / URL / metric resolves and says what's claimed), recount headline numbers from the source, and route low-confidence, conflicting, or citation-heavy claims to a verifier before they enter the draft. See "Verification" below.

Step 4 — Second waves (continuous motion)

If handoffs exposed gaps, dependencies, or follow-ups, spawn another parallel wave the same way. Repeat until no slice is pending and nothing new surfaced.

Step 5 — Deliver

Synthesize all handoffs into the single artifact the user asked for (roadmap, report, summary, plan). Cite which worker produced which finding when it helps, and carry each claim's confidence through (verified / single-sourced / unverified) — never launder a low into a confident sentence.

Then write any code/files yourself, or spawn a dedicated implementation wave (mind "Parallel writes"). Verify the deliverable, not just the handoffs: re-run/curl/validate served artifacts, regression-check sibling routes, and re-read the critical files you wrote (see references/verification.md §6).

Bounded waves — size, caps, and when to stop

A wave is bounded on purpose. "Loop-until-done" (spawn until a stop condition) is a real pattern, but unbounded it burns tokens for little gain: candidate generation is cheap, but selection plateaus, and extra rounds are non-monotonic — more iterations can lower quality, not just cost. Bounded waves keep the exploration and drop the runaway.

• Width: N = 3–8 workers per wave. Size N so you can fully verify all N. Go

wider only when a cheap automatic check (tests, schema, exec) gates the results.

• Depth: ≤ 2–3 waves, capped up front. Stop when a wave surfaces nothing new

and its outputs are near-duplicates of the last (stagnation), or when quality dropped versus the prior wave.

• Budget: ~60% generation / 40% verification. Selection is the scarce

resource; spend there.

• Match width to difficulty: easy → 1 + a light refine; medium → 3–5;

hard/open-ended → 5–8 for approach diversity; hardest/novel → don't loop, escalate the model.

• Anti-poisoning handoff: carry only a distilled, verified handoff (the

winner + a short critique) into the next wave — never raw transcripts or losing candidates. Long, irrelevant context measurably degrades reasoning.

Loop-until-done is justified only when ALL hold: a cheap, reliable ~ground-truth verifier exists; the signal is crisp and actionable (a failing test, not "try harder"); each iteration shows measurable progress; the work is easy–medium difficulty; and it stays hard-capped. That fits code-with-tests and exec-feedback pipelines; it misfits open-ended research/writing/design (verify in bounded waves instead).

Verification

The orchestrator's highest-leverage job. You can't make a worker smarter at inference time, but verifying a handoff is far cheaper than producing it, and in a multi-wave run one unchecked bad handoff compounds into the synthesis.

• Gate before spawn — counts, coverage, partition-sums (Step 0.5).
• Cheap checks every handoff — evidence present + resolves, scope match,

contradiction skim, citations actually support the claim.

• Self-checks in the prompt — cite-or-drop, confidence tags, "read

COMPLETELY", live sources, flag-unverified. (Don't rely on freeform "double-check yourself"; give an oracle or a separate verifier.)

• Dedicated verifier worker for high-stakes / contested / citation-heavy

claims — give it the claim + sources but not the generator's reasoning, and have it reason against a rubric/reference before its verdict (reference-guided + CoT is the cheapest reliable judge upgrade). Never show the generator the verifier's rubric (anti-gaming). For the highest-stakes calls, a multi-model panel + synthesis checks harder still (see "Multi-model fan-out").

• Measure & cross-check — re-run the oracle, recount from source, require ≥2

independent sources that actually entail the claim (a citation being present ≠ the claim being supported).

• Escalate low-confidence / conflicting findings (re-task with a tighter

prompt → dedicated verifier → ask the user, who may choose a stronger model) instead of folding them in.

Strongest on objective, checkable work (counts, code, facts-with-sources); on taste/judgment, verify the sub-claims, don't fake a grade. Keep claims honest: isolation reduces error propagation / path dependency, but don't claim a quantified "prevents poisoning" — there's no isolation-only ablation. Full playbook: references/verification.md.

Choosing subagent_type

Only pass model when the user explicitly requests a specific model (and if a requested model is unavailable, say so rather than silently substituting). The deliberate exception is a user-requested multi-model panel — see "Multi-model fan-out" below.

For review/audit slices, Cursor also exposes specialized subagents when available (e.g. bugbot, security-review, ci-investigator, ci-watcher) — prefer them for those slice types.

Multi-model fan-out (panel + synthesize)

The default fan-out runs each slice on one model. A high-stakes slice — an architecture/design call, a risky correctness question, a security or audit pass, or a key research synthesis — fan the same slice out to a panel of different models, then act as judge and synthesizer.

Reconcile; don't concatenate. Label CONSENSUS (2+ models agree) vs lone-model findings, resolve contradictions, dedupe overlap, and carry each claim's confidence into one answer. The synthesis is where most of the value lives — per OpenRouter's Fusion research, roughly three-quarters of the gain comes from the synthesis step, not the model diversity — so invest there rather than stapling outputs together. Evidence: a panel of independent models + a judge + a synthesizer matched and surpassed a single top-tier model on hard, deep-research problems (OpenRouter, Surpassing Frontier Performance with Fusion, openrouter.ai/blog/announcements/fusion-beats-frontier/).

Run it in Cursor: pass a different `model` to each sibling Task worker on the same slice, launch them in one message with run_in_background: true (parallel), then synthesize. This is the one time the orchestrator picks models — only when the user asks for a multi-model pass or the slice is explicitly high-stakes — so ask which models to use; don't guess slugs. That's the deliberate exception to the default rule (otherwise don't set model).

Caveat: a panel multiplies token cost (you pay every worker) and adds latency — reserve it for high-stakes slices, not routine ones. The adversarial multi-model review (a panel of reviewer models + one synthesized verdict) is this same pattern applied to code review.

Generate-and-filter & tournaments

For open-ended ideation or "produce the single best X", generate several candidates and filter — don't trust one attempt:

• Cheap filter first. Gate candidates through a near-ground-truth check

(tests, schema/exec, dedup/clustering) before spending judge tokens — generation is cheap, judging is not. (AlphaCode's shape: generate many → filter ~99% by tests → cluster → submit a few.)

• Selection ladder, not all-pairs. Dedup/cluster → shortlist → pairwise-judge

only among finalists. A naive O(N²) tournament spends most of its tokens comparing also-rans.

• Use `best-of-n-runner` for competing implementation attempts (each in its

own git worktree), then inspect/test/merge the winner yourself.

• For high-stakes judgment calls, the multi-model panel (above) is the

generate-and-filter of verification.

Worker prompt = the contract

A worker cannot ask follow-up questions. Under-specified prompts drift silently. Write each as if you get one shot. Every worker prompt includes:

1. Overall goal (context only — "don't try to own the whole goal").
2. This worker's exact slice (the disjoint range / area / paths).
3. Where to look (dirs, files, data ranges, which MCP/tools to use).
4. What to return — the structured handoff from references/handoff-format.md.
5. Self-verify rules — cite-or-drop every claim, tag confidence

(high|med|low), and state what you could not verify. Analysis workers: "read COMPLETELY (N lines) and report the count." Research workers: "use live sources, not training data."

1. Scope boundaries — what's explicitly OUT of scope (so it doesn't overlap a

sibling worker).

1. Return only the handoff. The worker's entire final message becomes your

context — tell it to return only the structured handoff and to write any large artifact to a file and cite the path instead of pasting it inline.

1. Edit workers: you are not alone. For implementation slices, warn the

worker that siblings may be active: own only the listed paths, don't revert others' changes, and never spawn its own subagents (only the orchestrator fans out).

See references/handoff-format.md for the copy-paste worker handoff template and references/examples.md for a full worked run (the health-coach research job in the screenshots) plus reusable decomposition recipes.

Parallel writes (the local gotcha)

Local subagents share one working directory. Concurrent writes to overlapping files clobber each other.

• Parallel reads/research/analysis: always safe. This is the default use.
• Disjoint edits you keep all of: use regular workers only when path sets are

strictly disjoint, or do the edits serially after the research waves finish.

• Competing attempts at the same task: use best-of-n-runner (each in its

own git worktree/branch), then inspect, test, and merge the chosen result yourself — worktrees prevent clobbering, not the merge.

Escalating to cloud orchestration

For genuinely large builds (many concurrent code-writing agents, runs that outlive this session, PRs per task), escalate to the Cursor team's orchestrate plugin if it's installed. It spawns cloud agents via the Cursor SDK, isolates each on its own branch, and reconciles handoffs from disk/git. It requires its own setup (credentials and a runtime, plus optional Slack) — check its docs — and is invoked explicitly (e.g. /orchestrate <goal>).

Cursor subagent mechanics here were checked on 2026-06-15. The details most likely to drift: the cloud orchestrate plugin's setup, and whether Cursor caps concurrent background subagents (not officially documented). Re-verify those if they matter to your run.

Checklist

• [ ] Discovered the problem shape before decomposing.
• [ ] Triaged each slice (worker type + verification tier).
• [ ] Slices are independent (disjoint data/areas/paths).
• [ ] Each worker prompt is fully self-contained (no reliance on chat history).
• [ ] All Task calls sent in one message, run_in_background: true.
• [ ] Ended turn to await completions — no polling loop.
• [ ] No two parallel workers write the same paths.
• [ ] Verified coverage before spawning (counts/bounds/partition-sum).
• [ ] Read every handoff; spawned follow-ups for open questions.
• [ ] Wave bounded (width ≈3–8, ≤2–3 waves); carried only distilled handoffs forward.
• [ ] Verified each handoff's evidence (not just its Status); escalated

low-confidence / conflicting / uncited findings before synthesizing.

• [ ] Verified the final deliverable (re-ran/validated; re-read critical writes).
• [ ] Synthesized one deliverable from the handoffs.