feat: add agentic video analysis and clarify action results

Report device actions as dispatched and preserve full failure details. Route
Flash through McpActionExecutor and use ToolFailure across helper tools and
agent loops instead of inferring failure from response text.

Require model-provided descriptions for coordinate targets. Keep observed
element metadata separate from those descriptions in safety checks, visual
summaries, history, and frontend labels.

Add Gemini Interactions API video analysis with configurable processing mode,
chunk size, and timeouts. Centralize Google provider capabilities and token
usage normalization, and update google-genai to 2.22.0.

Preserve system instructions on follow-up interactions, replan pending chunks
when falling back to static processing, and reject unanswered native video
and audio segments instead of caching placeholder results. Keep helper-tool
failure status intact through history tools and committee calls.

Validation: 1,762 Python unit tests passed (3 skipped), 40 frontend formatter
tests passed, and the production UI build completed. Ruff lint, formatting,
the quality ratchet, and git diff checks passed.

artemis/llm/google/usage.py

@@ -0,0 +1,148 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Token-usage normalization across every response shape ARTEMIS sees.
+
+Google alone reports usage under three different vocabularies:
+
+* LangChain ``usage_metadata`` - ``input_tokens`` / ``output_tokens`` /
+  ``input_token_details.cache_read`` (OpenAI-style names appear via
+  ``response_metadata.token_usage``).
+* ``generate_content`` ``usage_metadata`` - ``prompt_token_count`` /
+  ``candidates_token_count`` / ``cached_content_token_count`` /
+  ``thoughts_token_count`` / ``tool_use_prompt_token_count``.
+* Interactions API ``usage`` - ``total_input_tokens`` /
+  ``total_output_tokens`` / ``total_cached_tokens`` / ``total_thought_tokens``
+  / ``total_tool_use_tokens``.
+
+:func:`normalize_usage` folds all of them into one dict so meters, traces and
+the UI read a single set of keys.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+_PROMPT_KEYS = ("prompt_tokens", "input_tokens", "prompt_token_count", "total_input_tokens")
+_COMPLETION_KEYS = (
+    "completion_tokens",
+    "output_tokens",
+    "candidates_token_count",
+    "total_output_tokens",
+)
+_TOTAL_KEYS = ("total_tokens", "total_token_count")
+_CACHED_KEYS = (
+    "cached_tokens",
+    "cached_content_token_count",
+    "cachedContentTokenCount",
+    "total_cached_tokens",
+)
+_THOUGHT_KEYS = (
+    "thought_tokens",
+    "thoughts_token_count",
+    "reasoning_tokens",
+    "total_thought_tokens",
+)
+_TOOL_USE_KEYS = ("tool_use_tokens", "tool_use_prompt_token_count", "total_tool_use_tokens")
+
+NORMALIZED_USAGE_KEYS = (
+    "prompt_tokens",
+    "completion_tokens",
+    "total_tokens",
+    "cached_tokens",
+    "thought_tokens",
+    "tool_use_tokens",
+)
+
+
+def _int(value: Any) -> int:
+    if isinstance(value, bool):
+        return 0
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return 0
+
+
+def _read(raw: Any, key: str) -> Any:
+    if isinstance(raw, dict):
+        return raw.get(key)
+    return getattr(raw, key, None)
+
+
+def _first(raw: Any, keys: tuple[str, ...]) -> int:
+    for key in keys:
+        value = _int(_read(raw, key))
+        if value:
+            return value
+    return 0
+
+
+def normalize_usage(raw: Any) -> dict[str, int] | None:
+    """Normalizes a usage payload (dict or object) into the shared key set.
+
+    Returns ``None`` when the payload carries no usable token counts, so
+    callers can distinguish "no usage reported" from an all-zero report.
+    """
+    if raw is None:
+        return None
+
+    prompt = _first(raw, _PROMPT_KEYS)
+    completion = _first(raw, _COMPLETION_KEYS)
+    total = _first(raw, _TOTAL_KEYS) or (prompt + completion)
+
+    cached = 0
+    details = _read(raw, "input_token_details")
+    if details is not None:
+        cached = _int(_read(details, "cache_read"))
+    if not cached:
+        cached = _first(raw, _CACHED_KEYS)
+
+    thought = 0
+    output_details = _read(raw, "output_token_details")
+    if output_details is not None:
+        thought = _int(_read(output_details, "reasoning"))
+    if not thought:
+        thought = _first(raw, _THOUGHT_KEYS)
+
+    tool_use = _first(raw, _TOOL_USE_KEYS)
+
+    if total <= 0:
+        return None
+    return {
+        "prompt_tokens": prompt,
+        "completion_tokens": completion,
+        "total_tokens": total,
+        "cached_tokens": cached,
+        "thought_tokens": thought,
+        "tool_use_tokens": tool_use,
+    }
+
+
+def usage_from_message(message: Any) -> dict[str, int] | None:
+    """Extracts normalized usage from a LangChain message, best-effort.
+
+    Prefers LangChain's ``usage_metadata``; falls back to the raw provider
+    payload stored under ``response_metadata`` (``usage_metadata`` for Google,
+    ``token_usage`` for OpenAI-compatible providers).
+    """
+    raw = getattr(message, "usage_metadata", None)
+    if isinstance(raw, dict) and raw:
+        return normalize_usage(raw)
+    meta = getattr(message, "response_metadata", None)
+    if isinstance(meta, dict):
+        candidate = meta.get("usage_metadata") or meta.get("token_usage")
+        if isinstance(candidate, dict) and candidate:
+            return normalize_usage(candidate)
+    return None