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.
2. 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.
3. 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).
4. Parallelism is for reading, not writing. Local workers share the workspace; concurrent writes to overlapping paths corrupt each other.
5. 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.
6. 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.
7. Decomposition is entropy reduction. A vague goal is high-entropy — many plausible plans still fit it. Your first job is to shrink that space (dig locally, then pull from attached resources, then ask the user only if it pays) before you slice it; slicing a high-entropy goal yields overlapping, mis-sized slices. See "Entropy-first decomposition."

Entropy-first decomposition

Before you fan out, treat the goal as an entropy-reduction problem: shrink how many plausible interpretations and plans still fit what you know. A vague, high-entropy request ("build a Flappy Bird game", "make my app faster") doesn't slice cleanly yet — reduce the uncertainty first, then decompose the low-entropy version. Name what's uncertain, because the two kinds resolve differently:

• Specification uncertainty — what the user wants (ambiguous goal, missing acceptance criteria, unstated constraints). Resolve by stating an explicit assumption and proceeding — or, only when a wrong guess is expensive, by asking.
• Environment / knowledge uncertainty — facts you don't have yet but can get (repo shape, schema, API behavior, current docs, data size). Resolve by gathering, not by asking.

Spend the cheapest action that buys the most certainty first — an information-gain ladder — and aim each probe at the unknown whose answer eliminates the most plans: the highest-information question is the one that splits the surviving interpretations roughly in half, not the one easiest to look up.

1. Dig locally first (cheap). Tool calls in the main session (list, read the schema/README, grep, sample data). This is Step 0, framed as entropy reduction; it often collapses most of the uncertainty for free.
2. Then pull from attached resources. If the environment doesn't hold the answer, spawn a small scouting wave of research workers to fetch it (web, Exa/Ref MCP, docs) — route these read-heavy slices to the cheap, fast model (see "Picking the model per slice").
3. Ask the user last, and only when it pays. Ask only when residual specification uncertainty is high and the question's expected information gain clearly beats its cost. Most requests carry enough to proceed on a stated assumption; over-asking is its own failure mode.

Then cascade: one high-level request becomes a decomposition wave (understand → locate unknowns → draft the plan) → verify → an execution wave that builds the ordered subtasks, with more scouting sub-waves wherever entropy stays high. Order the plan least-to-most — do the first-order subtasks first and let each verified result lower the uncertainty for the next. Keep the living plan in TodoWrite, and stop reducing when entropy is low enough to act: the verification gate doubles as "is the uncertainty low enough to commit?" One caution: a plan-then-execute pass fixes missing steps, not a misread goal — only the specification check above catches wrong framing, which is why it comes before planning. (Worked example + wave shape + paper grounding: references/examples.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.
• One scratch dir per run. Keep staged inputs, worker artifacts, and between-wave syntheses in one place (e.g. .waves/<run>/ with staging/, handoffs/, synthesis-wave-N.md) so prompts cite paths instead of pasting content and later waves re-read files, not chat history.
• 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: size the run, then classify each slice

Size the run first, out loud. Weigh breadth (how many independent slices), depth (how much reasoning each needs), ambiguity (how well-formed the goal is — see "Entropy-first decomposition"), and stakes (how costly a wrong answer is — this sets verification tiers), then state the chosen shape in one line before spawning — e.g. Run shape: one wave, 4 workers (3 research + 1 data chunk); second wave only if handoffs expose gaps. On the fence between two shapes, pick the smaller and say so. And if triage says no wave is needed, do the task inline and say that — never present inline work as wave coverage.

Then classify each slice on three 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".)
• Dependencies — which slices (if any) this one needs verified output from. Most slices should have none; a real dependency edge is what separates waves.
• 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.

Record the triage as a wave manifest — one row per slice, written before you spawn (in TodoWrite or .waves/<run>/manifest.md):

depends_on defines the wave boundaries: a wave is every not-yet-run slice whose dependencies are all met — and a dependency is met only when its handoff has been verified (Step 3), not merely returned. Launch wave 1 (no dependencies) in parallel; launch each dependent slice with the distilled, verified findings (or their .waves/<run>/ path) folded into its self-contained prompt — and unrelated slices stay parallel. The manifest is also your completion gate: N rows spawned means N handoffs collected and checked off before synthesis — a wave with a missing handoff has a silent hole in it (Step 3).

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 — one per slice whose dependencies are met (handoffs verified, not just returned) — 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

Completion gate first: check off every handoff against the wave manifest — N spawned means N accounted for. A worker that never returns, errors out, or comes back partial/blocked is a hole in the wave, and synthesizing around it silently drops a slice. Worker failure ladder: (1) re-task once, narrower — resume the same worker (each Task returns an agent ID that resumes with context preserved) when the slice just needs continuation, or re-spawn fresh with a narrower scope and a note about what came back; (2) if it fails again, do that slice yourself in the main session; (3) if it stays blocked, carry the slice into the synthesis explicitly as not-covered — never average over a missing slice as if coverage were complete.

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 (Step 3.5). See "Verification" below. A wave's handoffs count as verified only when these checks pass and every claim whose manifest tier demands a verifier has its verdict back — cheap checks alone don't clear a single verifier or higher tier.

Only then compress at the barrier: write the distilled synthesis to .waves/<run>/synthesis-wave-N.md and work from that file — next-wave prompts cite paths into the scratch dir, never re-paste raw handoffs. This file is what dependent slices and later waves consume, so nothing unverified enters it as a finding: a claim still awaiting its verdict is carried only as an explicit pending-verification line.

Step 3.5 — Verifier pass (when the tier demands it)

Before writing the wave synthesis, spawn dedicated verifier workers for every claim whose manifest tier is single verifier or higher — and for anything that arrived contested, surprising, single-sourced, or low-confidence. Give each verifier the claim + its cited sources, no generator reasoning, no authorship labels (see "Verification"). Verifiers can run while you draft around them, but their verdicts gate the wave synthesis itself, not just the final deliverable: until its verdict returns, a claim may sit in synthesis-wave-N.md only as an explicit pending-verification line — never as a settled finding, and never in a dependent slice's prompt (Step 0.7's met-only-when-verified rule).

Step 4 — Second waves (continuous motion)

If handoffs exposed gaps or follow-ups — or verified handoffs just unblocked dependent manifest slices — 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 nor any authorship label (judges favor output marked as their own; blind them), 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

Custom subagents and the missing-type fallback. Custom subagents (project .cursor/agents/, user ~/.cursor/agents/, or plugin-provided) show up as their own subagent_type values — worth defining when a role repeats across runs (a verifier, a docs researcher) so its prompt and pinned model live in one file. Two verified gotchas: new agent files register only after a Cursor restart, and a type missing from the enum is not permission to skip the role — run it as generalPurpose with the role's instructions inlined in the worker prompt (passing the intended model on the call) instead.

Picking the model per slice (cost / speed routing)

Model choice is a cost/speed lever — route it, don't put every slice on a frontier model:

• Scouting / decomposition / read-heavy exploration → the cheap, fast model. Cursor's built-in explore and search subagents already default to the Composer fast family (e.g. composer-2.5-fast) for exactly this: fast, cheap, and tuned for codebase understanding and tool use — so read waves are cheap by default and you often need not set model at all. To pin it, pass model: "composer-2.5" (or composer-2.5-fast) on the Task worker, or set the model field (inherit | fast | a slug) on a custom .cursor/agents/ subagent. This is the entropy-reduction workhorse.
• High-stakes verification, synthesis, or a multi-model panel → stronger reasoning, chosen deliberately. For a user-requested or high-stakes multi-model panel, ask which models to use; don't guess slugs (see "Multi-model fan-out").
• Otherwise honor a model the user named; if a requested model is unavailable, say so rather than silently substituting.

Caveats: availability varies (Max Mode, plan, or admin restrictions can force a fallback to a compatible model); slugs drift, so read them off Cursor's model picker rather than hardcoding volatile ones; inherit can be unreliable in some surfaces (omit model to inherit). When a custom agent's model matters, pin it in the frontmatter and pass the matching model on the Task call — the field has been ignored under some conditions (documented fallbacks plus confirmed bug reports), so if a worker's output quality looks off, consider that the intended model may not have run. Respect the user's cost and model preferences over any default here.

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 high-stakes end of model routing — the orchestrator picks frontier 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. (The cheap end — routing scouting and read-heavy waves to Composer 2.5 — is under "Picking the model per slice.")

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. For grading panels specifically, judges drawn from disjoint model families beat a single frontier judge on human agreement while cutting self-preference bias and cost (PoLL) — the family diversity, not the head count, is what does the work.

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.
• Budget check. At equal cost, k independent attempts + a majority vote or cheap filter usually beats critique/debate loops — benchmark any iterative loop against that baseline before paying for it.

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."
6. Scope boundaries — what's explicitly OUT of scope (so it doesn't overlap a sibling worker).
7. Return only the handoff, and keep it a digest. The worker's entire final message becomes your context — tell it to return only the structured handoff, capped at roughly 15 findings with one-line evidence each, and to write any large artifact (tables, logs, full lists) to a file and cite the path instead of pasting it inline.
8. 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 2026-07-03 (custom-agent registration-after-restart, resume-by-agent-ID, and the model-fallback conditions re-verified against current docs and bug reports). 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.
• Reduced entropy before slicing (dug locally → pulled from attached resources → asked the user only if it paid); sliced the low-entropy goal.
• Stated the run shape in one line before spawning (on the fence → the smaller shape); never presented inline work as wave coverage.
• Triaged each slice (worker type + dependencies + verification tier).
• Routed scouting / read-heavy waves to the cheap fast model (Composer 2.5); reserved frontier / panel models for high-stakes slices.
• Slices are independent (disjoint data/areas/paths), or their depends_on edges are recorded in the manifest.
• Wrote the wave manifest (slice / worker type / model / depends_on / verification tier) before spawning; launched dependent slices only after their dependencies' handoffs were verified (distilled findings fed into their prompts).
• Each worker prompt is fully self-contained (no reliance on chat history).
• Each wave's 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).
• Checked every manifest row off at collection (completion gate); ran the failure ladder on missing/blocked slices — no slice silently dropped.
• 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; wrote synthesis-wave-N.md only from verdict-cleared findings (pending claims tagged pending-verification, never fed to dependent slices).
• Verified the final deliverable (re-ran/validated; re-read critical writes).
• Synthesized one deliverable from the handoffs.