[instructions] Sync instruction files with release v0.84.3

.github/aw/syntax-agentic.md

@@ -269,6 +269,7 @@ description: Agentic workflow specific frontmatter fields for GitHub Agentic Wor
     ```
 
   - `setup-steps`/`pre-steps` also apply to built-in jobs (e.g. `activation`): use `setup-steps` for OIDC/secret bootstrap that must run before framework token minting, then verify the result in `pre-steps`.
+  - **`needs`/`if` on built-in jobs** — targeting a compiler-generated job (`agent`, `activation`, `safe_outputs`, etc.) under `jobs:` also accepts additive `needs` and `if`: `jobs.agent.needs` merges with compiler-generated dependencies, and `jobs.agent.if` combines with compiler-generated conditions using `&&`. Use this to gate the agent job on a custom setup job's outcome.
 
 - **`engine:`** - AI processor configuration (string or object: `id`, `model`, `permission-mode`, `agent`, `max-continuations`, `driver`, `copilot-sdk`, `auth`, and more). See [syntax-engine.md](syntax-engine.md) for the full field reference, per-engine support notes, and inline driver examples.
 

.github/aw/token-optimization-observability.md

@@ -0,0 +1,50 @@
+---
+description: OpenTelemetry export and harness-execution-experience learning loops for token optimization in GitHub Agentic Workflows.
+---
+
+# Token Optimization — Observability and Harness Learning
+
+See [token-optimization.md](token-optimization.md) for the full technique index and quick-reference checklist.
+
+## Technique 7 — Measure Continuously with OpenTelemetry and AgenticOps
+
+Export telemetry automatically and add workflows that keep finding token waste over time.
+
+### Enable OTLP export
+
+Add workflow-level OpenTelemetry export so each run emits token and phase data to your observability backend:
+
+```yaml
+observability:
+  otlp:
+    endpoint: ${{ secrets.GH_AW_OTEL_ENDPOINT }}
+    headers: ${{ secrets.GH_AW_OTEL_HEADERS }}
+```
+
+Setup, agent, and conclusion spans carry token usage attributes. See [Frontmatter syntax](syntax-agentic.md#agentic-workflow-specific-fields).
+
+### Add AgenticOps token workflows
+
+- `copilot-token-audit` — scheduled audit of token usage across workflows
+- `copilot-token-optimizer` — scheduled follow-up that identifies one expensive workflow and proposes concrete savings
+
+Loop: export OTEL → summarize usage → open optimization issues → re-measure. See `.github/workflows/` for examples.
+
+---
+
+## Technique 8 — Learn from Harness Execution Experience
+
+Treat the agent harness as six separate control surfaces rather than one prompt:
+
+| Dimension | gh-aw control surface |
+|---|---|
+| Context assembly | Prompt structure, imports, DataOps, and context compression |
+| Tool interaction | Tool selection, `gh-proxy`, `cli-proxy`, permissions, and result filtering |
+| Generation control | Engine and model selection, `max-turns`, and `timeout-minutes` |
+| Orchestration | Deterministic steps, sub-agents, planning, execution, and refinement |
+| Memory management | `cache-memory`, `repo-memory`, summaries, and stale-context removal |
+| Output processing | Safe outputs, schema validation, fallbacks, and `noop` behavior |
+
+Start with the smallest known-good harness. Per experiment, record a compact entry (task features, config change, outcome quality, AIC/token cost, diagnosed failure dimension), distill repeated diagnoses into reusable patterns, and retrieve only relevant cases later instead of re-searching broadly. Select changes **correctness first**: maximize the quality metric, then minimize AIC among equivalent-quality variants so a cheap but degraded result cannot win.
+
+Prioritize this for long-horizon, tool-heavy workflows with measurable headroom; keep retrieved experience compact so prompt caching offsets its input-token overhead. Based on [MemoHarness](https://arxiv.org/pdf/2607.14159) — treat its gains as directional (small held-out set, unablated components, cache-dependent cost advantage).

.github/aw/token-optimization.md

@@ -358,48 +358,9 @@ Keep each batch idempotent, skip items already fixed, and report the processed s
 
 ---
 
-## Technique 7 — Measure Continuously with OpenTelemetry and AgenticOps
+## Techniques 7–8 — Observability and Harness Learning
 
-Export telemetry automatically and add workflows that keep finding token waste over time.
-
-### Enable OTLP export
-
-Add workflow-level OpenTelemetry export so each run emits token and phase data to your observability backend:
-
-```yaml
-observability:
-  otlp:
-    endpoint: ${{ secrets.GH_AW_OTEL_ENDPOINT }}
-    headers: ${{ secrets.GH_AW_OTEL_HEADERS }}
-```
-
-Setup, agent, and conclusion spans carry token usage attributes. See [Frontmatter syntax](syntax-agentic.md#agentic-workflow-specific-fields).
-
-### Add AgenticOps token workflows
-
-- `copilot-token-audit` — scheduled audit of token usage across workflows
-- `copilot-token-optimizer` — scheduled follow-up that identifies one expensive workflow and proposes concrete savings
-
-Loop: export OTEL → summarize usage → open optimization issues → re-measure. See `.github/workflows/` for examples.
-
----
-
-## Technique 8 — Learn from Harness Execution Experience
-
-Treat the agent harness as six separate control surfaces rather than one prompt:
-
-| Dimension | gh-aw control surface |
-|---|---|
-| Context assembly | Prompt structure, imports, DataOps, and context compression |
-| Tool interaction | Tool selection, `gh-proxy`, `cli-proxy`, permissions, and result filtering |
-| Generation control | Engine and model selection, `max-turns`, and `timeout-minutes` |
-| Orchestration | Deterministic steps, sub-agents, planning, execution, and refinement |
-| Memory management | `cache-memory`, `repo-memory`, summaries, and stale-context removal |
-| Output processing | Safe outputs, schema validation, fallbacks, and `noop` behavior |
-
-Start with the smallest known-good harness. Per experiment, record a compact entry (task features, config change, outcome quality, AIC/token cost, diagnosed failure dimension), distill repeated diagnoses into reusable patterns, and retrieve only relevant cases later instead of re-searching broadly. Select changes **correctness first**: maximize the quality metric, then minimize AIC among equivalent-quality variants so a cheap but degraded result cannot win.
-
-Prioritize this for long-horizon, tool-heavy workflows with measurable headroom; keep retrieved experience compact so prompt caching offsets its input-token overhead. Based on [MemoHarness](https://arxiv.org/pdf/2607.14159) — treat its gains as directional (small held-out set, unablated components, cache-dependent cost advantage).
+See [token-optimization-observability.md](token-optimization-observability.md) for OpenTelemetry export, AgenticOps token workflows, and learning from harness execution experience.
 
 ---
 
@@ -413,11 +374,11 @@ See [token-optimization-caching-budgets.md](token-optimization-caching-budgets.m
 
 | Topic | File |
 |---|---|
+| OpenTelemetry export, AgenticOps, harness-experience learning | [token-optimization-observability.md](token-optimization-observability.md) |
 | Prompt caching, AI-credit guardrails, bounded file reads | [token-optimization-caching-budgets.md](token-optimization-caching-budgets.md) |
 | Inline sub-agents syntax | [subagents.md](subagents.md) |
 | A/B experiments | [experiments.md](experiments.md) |
 | Persistent memory | [memory.md](memory.md) |
-| Experience-guided harness optimization | [MemoHarness](https://arxiv.org/pdf/2607.14159) |
 | DataOps pattern | [DataOps guide](https://github.com/github/gh-aw/blob/main/docs/src/content/docs/patterns/data-ops.md) |
 | Audit command reference | [cli-commands.md](cli-commands.md) |
 | Frontmatter syntax | [syntax.md](syntax.md) |