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>

runtime/planner.py

@@ -26,6 +26,7 @@ class Planner:
         scene: Scene,
         context: TaskContext,
         world: "WorldState | None" = None,
+        screenshot: bytes | None = None,
     ) -> list[PlannedStep]:
         if context.step_results:
             return []