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>

tests/test_planner_config.py

@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+from runtime.planner_config import (
+    DEFAULT_MODEL_BY_PROVIDER,
+    DEFAULT_PROVIDER,
+    DEFAULT_TIMEOUT_SECONDS,
+    PlannerConfig,
+    load_config,
+)
+
+_NO_RELEVANT_VARS = {"UNRELATED": "1"}
+
+
+def test_load_config_defaults_when_unset() -> None:
+    config = load_config(_NO_RELEVANT_VARS)
+
+    assert config == PlannerConfig(
+        enabled=False,
+        provider=DEFAULT_PROVIDER,
+        model="",
+        timeout=DEFAULT_TIMEOUT_SECONDS,
+    )
+    assert config.resolved_model() == DEFAULT_MODEL_BY_PROVIDER[DEFAULT_PROVIDER]
+
+
+def test_load_config_parses_enabled_truthy_values() -> None:
+    for value in ["1", "true", "True", "yes", "on", "enabled"]:
+        assert load_config({"AI_PLANNER_ENABLED": value}).enabled is True
+
+
+def test_load_config_parses_enabled_falsy_values() -> None:
+    for value in ["0", "false", "no", "off", ""]:
+        assert load_config({"AI_PLANNER_ENABLED": value}).enabled is False
+
+
+def test_load_config_selects_provider_and_resolves_default_model() -> None:
+    config = load_config({"AI_PLANNER_PROVIDER": "openai"})
+
+    assert config.provider == "openai"
+    assert config.resolved_model() == "gpt-5.6"
+
+
+def test_load_config_anthropic_default_model() -> None:
+    config = load_config({"AI_PLANNER_PROVIDER": "anthropic"})
+
+    assert config.resolved_model() == "claude-sonnet-5"
+
+
+def test_load_config_falls_back_to_default_provider_when_unsupported() -> None:
+    config = load_config({"AI_PLANNER_PROVIDER": "not-a-real-provider"})
+
+    assert config.provider == DEFAULT_PROVIDER
+
+
+def test_load_config_model_override_wins_regardless_of_provider() -> None:
+    config = load_config(
+        {"AI_PLANNER_PROVIDER": "openai", "AI_PLANNER_MODEL": "custom-model"}
+    )
+
+    assert config.resolved_model() == "custom-model"
+
+
+def test_load_config_parses_valid_timeout() -> None:
+    config = load_config({"AI_PLANNER_TIMEOUT_SECONDS": "12.5"})
+
+    assert config.timeout == 12.5
+
+
+def test_load_config_falls_back_to_default_timeout_when_invalid_or_non_positive() -> None:
+    for value in ["not-a-number", "0", "-5"]:
+        config = load_config({"AI_PLANNER_TIMEOUT_SECONDS": value, **_NO_RELEVANT_VARS})
+        assert config.timeout == DEFAULT_TIMEOUT_SECONDS