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>
tools/ is a Hexagonal inner layer that must never depend on LLM
concerns (ADR 0002), but describe_screen_semantic.py imported
semantic.enricher, which pulls in the Anthropic client by default.
Relocated the wrapper to runtime/, which is where LLM-dependent code
is allowed to live; updated the tool registry and all test imports
accordingly. No behavior change.
openspec: semantic-scene capability, archived change semantic-scene-runtime
WorldModel routed all state through a single mutable _current_task_id,
so concurrent tasks sharing one instance could corrupt each other's
state. start_task() now returns a TaskWorldView handle scoped to that
task; TaskRunner.run() threads it through as a local variable instead
of reading self.world_model implicitly. Also extracts _start_world_view/
_record_step_result as reusable TaskRunner methods for composed runners.
openspec: world-model capability, archived change world-model-runtime
q792602257
