docs(harnessx-removal): the plane replaces HarnessX in README, docs and specs (task 8)

README: "Configuring the HarnessX agentic plane" becomes "The agentic
plane (ax)" (ousast plane run|status|doctor|remember, per-task Model,
budget and egress, pointer to ops/ax/README.md, one tagged retirement
line); "The HarnessX self-improving cycle" becomes "The self-improving
cycle", and its MetaAgent paragraph is replaced by the memory proposer
(`ousast improve --memory`, provenance, train-on-test guard). The fusion
panel_model/decider_model lines and the [harnessx] spend caps are gone.
The README reference test now passes without its strict xfail.

docs/examples.md section 6 runs the plane; docs/threat-model.md describes
the per-task EgressPolicy, the usd/calls budgets and the start-request
credential path. RELEASE_NOTES.md gets an Unreleased entry: HarnessX
removed, the retired keys and their errors, the silent loss of the
second hunter for users who set [models] hunter, and the plane as the
agentic path.

Dated retirement notes (2026-09-30) head the requirements, design and
brief of harnessx-self-improving-rulesets, learning-harness,
three-stage-scan and openrouter-sast-harness, and follow the spec table
in steering/overview.md; history is unchanged. All tasks are checked, so
spec.json moves to implementation-complete.

Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo

.kiro/specs/learning-harness/brief.md

@@ -1,5 +1,8 @@
 # Brief: learning-harness
 
+> **Retired 2026-09-30:** HarnessX was removed by `harnessx-removal`; the agentic plane is ax
+> (`ai-service-plane`), and evolutionary proposals come from the plane memory. History below is unchanged.
+
 ## Problem
 
 The tool is a hand-authored static analyzer with a learning loop bolted on the side, and the loop cannot learn. Three deliberate choices cap it: closed vocabularies (sinks, sources, obligation kinds) that grow only by writing a new spec; a corpus that is the only teacher and a shape that is the only lesson; and a ban on retaining anything the model discovers. Results have ceilings by construction, the loop trains on its own test set, and there is no failure analysis feeding the next round. Detection logic is ~16% of the source and the other 84% cannot say whether the 16% works.