nWave-ai nw-throughput
Constraint
The bottleneck is usually the shared machine or a causal boundary, not the number of available agents. Keep cloud reasoning busy while allowing at most one heavy local build/test/install workload. Measure load and stop adding lanes when contention increases end-to-end wall time.
Strategy
- Select the nearest measurable constraint and shortest falsifier.
- Fan out only independent work with disjoint ownership in detached worktrees.
- Keep one heavy local build/test/install box; serialize its use.
- Fan in on the whole delivered value. Keep required review, EXAMINE,
integration, observation and cleanup; the orchestrating LLM chooses and
invokes them, while DES persists typed facts and advisory
NEXTdata. - Measure wall time, cost, tokens, retries, tool calls, and human intervention against the outcome.
Bounded observed correction context
For a bounded correction with an observed counterexample, targeted context may be used when it supplies the observation/counterexample, requested delta, exact candidate, and references to the applicable authorities. Fetch further context when a required fact is missing; do not repeat the entire narrative handoff. Preserve semantic decisions and declared quality, then return the commands actually performed, their evidence, and any facts still missing. This is a conditional context choice, not a blanket removal of guidance.
P2: diagnose a changed interface surface once
When the first failure reveals an interface migration, the orchestrating LLM chooses the related property x consumer test-file scope and runs that complete scope through the repository's native test executor before paying for the next broad run. Collect all failures in the selected scope, repair the coherent contract, then run the required broad verification. Selected files are evidence about that scope only; they do not establish whole-project coverage.
Use the repository's own affected-test selector when it exists. In this Python
repository, python3 scripts/hooks/pytest_touched_files.py --diagnose PATH ...
is available: it launches every selected runnable file within the normal
bounded ceiling without early stopping, clears ambient PYTEST_ADDOPTS
selection filters, and prints the exact argv plus deferred and uncovered paths.
Repository collection configuration still applies. It returns nonzero for
failures, zero collection, an oversized scope, or a scope left incomplete by
deferred/uncovered paths. The ordinary pre-commit command remains fail-fast and
bounded. This example is not a
cross-language requirement: another repository uses its native executor and
must report the corresponding selected, deferred, and uncovered scope loudly.
The LLM selects the scope and the next commands from the observed failures. Do not add a controller, automatic retry, phase transition, or gate for this workflow.
Feature evolution before cleanup
After the LLM has completed whole-feature review and required evidence, it runs
des lane integrate --repo-root ROOT --worktree PATH --keep-worktree; after
that whole-feature integration it invokes des evolution --repo-root ROOT --input - once on destination ROOT before cleanup. Never invoke it per slice;
DES does not impose a completion gate. The
closed v1 JSON carries schema_version, explicit date (YYYY-MM-DD) and
feature_id, purpose, key_decisions, delivered_work,
verification_results, problems, lessons, and durable_artifacts.
Problems and lessons use explicit applicability plus a nonblank reason; use
not_applicable rather than invented content. Destinations default to
docs/evolution/YYYY-MM-DD-{feature-id}.md and use
documents.evolution.destination from unified global configuration with a
project override. On resumed finalization retry the exact same input; it is
idempotent. This command invokes no provider and does not choose a phase,
feature size or cleanup action.
Example shape: {"schema_version":1,"date":"2026-09-09","feature_id":"feature-id","purpose":"...","key_decisions":["..."],"delivered_work":["..."],"verification_results":["..."],"problems":{"applicability":"not_applicable","reason":"...","items":[]},"lessons":{"applicability":"applicable","reason":"...","items":["..."]},"durable_artifacts":[{"label":"evidence","path":"docs/evidence.md"}]}. Artifact paths are repository-relative or HTTPS.
Preserve and commit the generated evolution document and its evidence on ROOT
before cleanup, then explicitly run des lane finalize --repo-root ROOT --worktree PATH. Finalize only removes a clean lane whose tip is already
reachable from ROOT; it never merges or reintegrates. The destination may have
advanced with the evolution-document commit after integration. LLM chooses these
commands; NEXT remains advisory.
For features and bugfixes alike, the orchestrating LLM evaluates and revises
S/M/L from current evidence, selects applicable waves and upstream corrections,
and may ignore NEXT. DES does not use a size label to route or execute work.