feat(agent-runtime): add LLM-driven AI Planner with dual-provider tool calling

Replaces the stub Planner's fixed describe_screen/[] behavior with a real
decision-maker: AIPlanner uses native tool/function calling (Anthropic or
OpenAI, pluggable via AI_PLANNER_PROVIDER) to select exactly one grounded
action per turn, with an explicit finish_task(success, reason) tool for
completion/failure instead of an ambiguous "no tool call" signal. Default
disabled (AI_PLANNER_ENABLED=false) and additive; TaskRunner falls back to
the existing stub Planner unchanged when disabled.

Amends CONSTITUTION.md's Perception Boundary with one narrow exception:
only the AI Planner may receive the current step's raw screenshot bytes
alongside Scene, for vision-grounded coordinate grounding. Also fixes a
latent gap in TaskRunner.run(): observe/plan exceptions are now caught per
iteration and turned into a failed task with a failure_reason, instead of
propagating uncaught.

openspec change: ai-planner-runtime.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

docs/CONSTITUTION.md

@@ -21,7 +21,13 @@ driver such as `WDADriver`.
 
 ## Perception Boundary
 
-`Scene` is the only perception artifact the LLM sees. It is produced through
+`Scene` is the only perception artifact the LLM sees, with one narrow,
+explicit exception: the runtime-layer AI `Planner` (and only that Planner)
+may additionally receive the raw screenshot bytes for the current step,
+alongside `Scene`, to support vision-grounded decision-making. No other
+layer — `api`, `tools`, `perception`, `storage`, or any other LLM consumer —
+may receive raw screenshot bytes; every other perception consumer still
+receives `Scene` only. `Scene` itself is still produced exclusively through
 `PerceptionProvider`, not by direct calls to OCR, UI tree parsing, or
 `scene_builder` from runtime and API layers.