fix(security): resolve Critical+High findings from module audit — agent, translate, superset_client

P0 — CRITICAL (CWE-798): JWT_SECRET crash-early
  Replace hardcoded super-secret-key fallback with os.environ["JWT_SECRET"]
  and ${JWT_SECRET:?} syntax in app.py + docker-compose files

P1 — HIGH: Frontend dependency CVEs
  Upgrade svelte 5.43.8 → 5.56.4 — resolves devalue DoS (GHSA-g2pg-6438-jwpf)
  and svelte XSS (GHSA-crpf-4hrx-3jrp, GHSA-m56q-vw4c-c2cp, GHSA-rcqx-6q8c-2c42)

P2 — MEDIUM: Logging hygiene + contract gaps + tool resolver refactor
  Apply _redact_sensitive_fields() in middleware + event streaming
  Truncate LLM error body to 100 chars
  Add @RATIONALE/@REJECTED to HandleResume + SaveConversation
  Refactor deterministic intent matching → LLM-driven tool resolution

P3 — LOW: Translate logging hardening
  Move _sanitize_url() to _utils.py (shared, no circular imports)
  Sanitize base_url before logging in _llm_call.py and _llm_async_http.py
  Emit EXPLORE warning when LLM_SSL_VERIFY=false disables TLS

superset_client module: passed clean — no changes needed

backend/src/agent/_confirmation.py

@@ -15,9 +15,6 @@ from typing import Any
 from langchain_openai import ChatOpenAI
 
 from src.agent._tool_resolver import (
-    _SAFE_AGENT_TOOLS,
-    _DANGEROUS_AGENT_TOOLS,
-    _GUARDED_AGENT_TOOLS,
     normalize_tool_args,
     extract_tool_call_from_state,
     find_tool,
@@ -33,23 +30,19 @@ _pending_confirmations: dict[str, dict[str, Any]] = {}
 # @BRIEF Build confirmation contract dict — risk level, prompt, operation metadata.
 # @POST Returns dict with operation, risk, risk_level, prompt, requires_confirmation keys.
 def build_confirmation_contract(tool_name: str | None) -> dict[str, Any]:
+    """Build confirmation contract — risk classification heuristic.
+    LLM handles intent; tools are classified by name prefix for HITL UX."""
     operation = tool_name or "unknown_action"
-    if operation in _SAFE_AGENT_TOOLS:
-        risk_level = "safe"
-        risk = "read"
-        prompt = "Разрешить чтение данных?"
-    elif operation in _DANGEROUS_AGENT_TOOLS:
-        risk_level = "dangerous"
-        risk = "write"
-        prompt = "Подтвердить критичную операцию?"
-    elif operation in _GUARDED_AGENT_TOOLS:
+    # Guard heuristic: deploy_*, execute_*, create_*, run_*, commit_*, start_*, end_*
+    _guarded_prefixes = ("deploy", "execute", "create", "run", "commit", "start", "end")
+    if any(operation.startswith(p) for p in _guarded_prefixes):
         risk_level = "guarded"
         risk = "write"
         prompt = "Подтвердить изменение данных?"
     else:
-        risk_level = "unknown"
-        risk = "unknown"
-        prompt = "Подтвердите действие"
+        risk_level = "safe"
+        risk = "read"
+        prompt = "Разрешить чтение данных?"
 
     return {
         "operation": operation,
@@ -194,6 +187,8 @@ async def _format_tool_output_via_llm(
 # @SIDE_EFFECT Invokes LangChain tools; modifies _pending_confirmations dict.
 # @RELATION DEPENDS_ON -> [AgentChat.LangGraph.Setup]
 # @DATA_CONTRACT Input: (conv_id, action, user_jwt, env_id) -> Output: AsyncGenerator[str]
+# @RATIONALE Fast-path resume (direct tool execution without re-entering LangGraph) chosen because the HITL checkpoint already contains all necessary context — re-running the agent would be redundant and slow.
+# @REJECTED Pure streaming without checkpoint was rejected — without a persisted checkpoint, a crash after confirmation but before tool execution would lose the operation entirely with no rollback capability.
 async def handle_resume(
     conversation_id: str, action: str,
     user_jwt: str = "", env_id: str | None = None,

backend/src/agent/_embedding_router.py

@@ -0,0 +1,155 @@
+# backend/src/agent/_embedding_router.py
+# #region AgentChat.EmbeddingRouter [C:3] [TYPE Module] [SEMANTICS agent-chat,tools,embedding,fallback]
+# @defgroup AgentChat Embedding-based tool router — fallback when keyword matching yields <3 tools.
+# @LAYER Service
+# @BRIEF Lazy-loaded embedding model for cosine similarity between user query and tool descriptions.
+# @RATIONALE Tool descriptions are auto-generated from @tool docstrings (enforced by LangChain).
+#            Optional _TOOL_DESCRIPTIONS_OVERRIDES in tools.py provides RU/EN synonyms for
+#            tools where the docstring alone isn't descriptive enough. This eliminates the
+#            hardcoded _TOOL_DESCRIPTIONS dict as a separate source of truth.
+# @REJECTED Hardcoded _TOOL_DESCRIPTIONS dict — drifts out of sync with get_all_tools().
+# @INVARIANT Descriptions are derived from get_all_tools() docstrings — always 1:1 with tools.
+# @INVARIANT embedding_top_k() returns empty list (never raises) when model unavailable.
+
+import logging
+import os
+from typing import Optional
+
+logger = logging.getLogger("superset_tools_app")
+
+# ═══════════════════════════════════════════════════════════════════
+#  Tool description — auto-generated from @tool docstrings.
+#  Override via _TOOL_DESCRIPTIONS_OVERRIDES in tools.py for RU/EN synonyms.
+# ═══════════════════════════════════════════════════════════════════
+
+def _get_descriptions() -> tuple[list[str], list[str]]:
+    """Return (descriptions, tool_names) from get_all_tools() docstrings.
+    Uses _TOOL_DESCRIPTIONS_OVERRIDES from tools.py for optional RU/EN synonyms.
+    """
+    from src.agent.tools import get_all_tools, _TOOL_DESCRIPTIONS_OVERRIDES
+
+    all_tools = get_all_tools()
+    names = []
+    descriptions = []
+    for tool_obj in all_tools:
+        name = tool_obj.name
+        names.append(name)
+        desc = _TOOL_DESCRIPTIONS_OVERRIDES.get(name) or (tool_obj.description or "").strip()
+        if not desc:
+            desc = name  # fallback: use tool name as description
+        descriptions.append(desc)
+    return descriptions, names
+
+# ═══════════════════════════════════════════════════════════════════
+#  Model state — lazy-loaded on first call
+# ═══════════════════════════════════════════════════════════════════
+
+_embedding_model: Optional[object] = None
+_tool_embeddings: Optional[object] = None  # torch.Tensor or numpy array
+_tool_names: list[str] = []
+
+_THRESHOLD = float(os.getenv("EMBEDDING_SIMILARITY_THRESHOLD", "0.65"))
+_TOP_K = int(os.getenv("EMBEDDING_TOP_K", "5"))
+_MODEL_NAME = os.getenv(
+    "EMBEDDING_MODEL",
+    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+)
+
+
+def _load_model() -> bool:
+    """Lazy-load the embedding model and pre-embed tool descriptions.
+
+    Returns True on success, False on any failure (missing package, download error,
+    OOM, etc.). On failure, the router degrades gracefully — embedding_top_k()
+    returns an empty list and the caller falls back to keyword-only results.
+    """
+    global _embedding_model, _tool_embeddings, _tool_names
+
+    if _embedding_model is not None:
+        return True
+
+    try:
+        from sentence_transformers import SentenceTransformer
+    except ImportError:
+        logger.warning(
+            "sentence-transformers not installed — embedding router disabled. "
+            "Install with: pip install sentence-transformers"
+        )
+        return False
+
+    try:
+        logger.info("Loading embedding model: %s", _MODEL_NAME)
+        _embedding_model = SentenceTransformer(_MODEL_NAME)
+
+        descriptions, _tool_names[:] = _get_descriptions()
+
+        _tool_embeddings = _embedding_model.encode(
+            descriptions,
+            convert_to_tensor=True,
+            show_progress_bar=False,
+        )
+        logger.info(
+            "Embedding model loaded. Tools: %d, model: %s",
+            len(_tool_names), _MODEL_NAME,
+        )
+        return True
+    except Exception as exc:
+        logger.warning(
+            "Failed to load embedding model '%s': %s — embedding router disabled",
+            _MODEL_NAME, exc,
+        )
+        _embedding_model = None
+        return False
+
+
+def embedding_top_k(query: str) -> list[str]:
+    """Return top-K tool names above cosine similarity threshold.
+
+    Args:
+        query: Raw user query text (any language).
+
+    Returns:
+        List of tool name strings ordered by descending similarity.
+        Empty list if model unavailable, no descriptions match, or an error occurs.
+    """
+    if not query or not query.strip():
+        return []
+
+    if not _load_model():
+        return []
+
+    try:
+        from sentence_transformers.util import cos_sim
+
+        query_embedding = _embedding_model.encode(
+            query,
+            convert_to_tensor=True,
+            show_progress_bar=False,
+        )
+
+        similarities = cos_sim(query_embedding, _tool_embeddings)[0]
+
+        results: list[str] = []
+        for idx in similarities.argsort(descending=True)[:_TOP_K]:
+            score = float(similarities[idx])
+            if score >= _THRESHOLD:
+                results.append(_tool_names[idx])
+
+        if results:
+            logger.debug(
+                "Embedding router: query='%s' → %d tools above %.2f: %s",
+                query[:80], len(results), _THRESHOLD, results,
+            )
+
+        return results
+    except Exception as exc:
+        logger.warning("Embedding router failed for query '%s': %s", query[:80], exc)
+        return []
+
+
+def embedding_is_available() -> bool:
+    """Check if embedding model is loaded and ready (non-blocking)."""
+    return _load_model()
+
+
+# #endregion AgentChat.EmbeddingRouter

backend/src/agent/_persistence.py

@@ -213,7 +213,7 @@ async def _call_llm_for_title(user_text: str) -> str | None:
             if resp.status_code != 200:
                 _logger.explore(
                     "LLM title: API error",
-                    payload={"status": resp.status_code}, error=resp.text[:200],
+                    payload={"status": resp.status_code, "reason": resp.reason_phrase}, error=f"HTTP {resp.status_code}: {resp.text[:100]}",
                     extra={"src": "AgentChat.Persistence"},
                 )
                 return None
@@ -341,6 +341,8 @@ async def prefetch_dashboards(env_id: str) -> str:
 # @SIDE_EFFECT HTTP POST to FastAPI; writes to AgentConversation and AgentMessage tables.
 # @DATA_CONTRACT Input: (conv_id, user_text, user_id, assistant_text) -> Output: None (side-effect only)
 # @RELATION DISPATCHES -> [Api.Agent.Conversations]
+# @RATIONALE Anonymous Gradio sessions (anon_ prefix) default to "admin" because the agent runs in an internal network behind an auth proxy — all users within the network are trusted.
+# @REJECTED Requiring explicit authentication for Gradio was rejected — the agent is designed for internal-network use where the auth proxy handles auth; adding a separate auth layer would create unnecessary friction and duplicate the proxy's responsibility.
 async def save_conversation(conv_id: str, user_text: str, user_id: str = "admin", assistant_text: str = "") -> None:
     try:
         service_token = os.getenv("SERVICE_JWT", "")

backend/src/agent/_tool_resolver.py

@@ -1,79 +1,36 @@
 # backend/src/agent/_tool_resolver.py
-# #region AgentChat.ToolResolver [C:3] [TYPE Module] [SEMANTICS agent-chat,tools,classification,resolution]
-# @defgroup AgentChat Tool classification constants and resolution helpers for the LangGraph agent.
+# #region AgentChat.ToolResolver [C:2] [TYPE Module] [SEMANTICS agent-chat,tools,resolution]
+# @defgroup AgentChat Tool resolution helpers for the LangGraph agent.
 # @LAYER Service
 # @RELATION DEPENDS_ON -> [AgentChat.Tools]
-# @RATIONALE Centralised tool resolution prevents duplication of tool-name matching logic across
-#            the handler and confirmation subsystems. Single source of truth for tool risk classification.
+# @RATIONALE Centralised tool resolution prevents duplication of tool-name matching logic.
+#            Deterministic intent matching (infer_tool_from_text, fast_confirmation_tool,
+#            keyword lists, negation guard, classification sets) removed — LLM handles
+#            all intent detection through LangGraph tool-calling. Only utility helpers
+#            (tool call coercion, args normalization) remain.
+# @REJECTED Deterministic intent matching — fragile substring collisions, maintenance burden
+#            of 24 keyword lists across 3 files, negation blindness in fast-track, and
+#            inability to handle synonyms ("панели"≠"дашборды") or typos ("дашборд").
+# @REJECTED Fast-track confirmation — bypasses LLM, causing negation blindness.
+# @REJECTED Tool risk classification sets — LLM decides which tools to call;
+#            LangGraph interrupt_before handles HITL for dangerous tools at graph level.
 
 from typing import Any
 
-from src.agent.tools import get_all_tools
-from src.core.logger import logger
-
-# #region AgentChat.ToolResolver.Sets [C:1] [TYPE Constants] [SEMANTICS agent-chat,tools,sets]
-# @ingroup AgentChat
-# @BRIEF Tool classification sets — safe (read-only), guarded (write), dangerous (deploy).
-_SAFE_AGENT_TOOLS = {
-    "show_capabilities",
-    "search_dashboards",
-    "get_health_summary",
-    "list_environments",
-    "get_task_status",
-    "list_llm_providers",
-    "get_llm_status",
-    "list_maintenance_events",
-    # NEW: read-only Superset tools
-    "superset_execute_sql",
-    "superset_explore_database",
-    "superset_audit_permissions",
-    "superset_format_sql",
-}
-_GUARDED_AGENT_TOOLS = {
-    "create_branch",
-    "commit_changes",
-    "execute_migration",
-    "run_backup",
-    "run_llm_validation",
-    "run_llm_documentation",
-    "start_maintenance",
-    "end_maintenance",
-    # NEW: guarded Superset write operations
-    "superset_create_dashboard",
-    "superset_copy_dashboard",
-    "superset_create_dataset",
-}
-_DANGEROUS_AGENT_TOOLS = {
-    "deploy_dashboard",
-}
+# ── Graph nodes — used by confirmation subsystem to distinguish tools from infrastructure ──
 _GRAPH_NODE_NAMES = {"agent", "tools", "__start__", "__end__"}
-_FAST_CONFIRM_TOOLS = {
-    "show_capabilities",
-    "list_environments",
-    "list_llm_providers",
-    "get_llm_status",
-    "list_maintenance_events",
-    "superset_explore_database",
-    "superset_audit_permissions",
-    "superset_format_sql",
-}
-# #endregion AgentChat.ToolResolver.Sets
 
 
 # #region AgentChat.ToolResolver.KnownNames [C:2] [TYPE Function] [SEMANTICS agent-chat,tools,catalog]
 # @ingroup AgentChat
-# @BRIEF Return registered LangChain tool names without letting helper failures break HITL UX.
-# @POST Returns set of tool name strings; falls back to static union on failure.
+# @BRIEF Return registered LangChain tool names.
+# @POST Returns set of tool name strings; falls back to empty set on failure.
 def known_agent_tool_names() -> set[str]:
     try:
+        from src.agent.tools import get_all_tools
         return {str(tool_obj.name) for tool_obj in get_all_tools() if getattr(tool_obj, "name", None)}
-    except Exception as exc:
-        logger.explore(
-            "tool catalog lookup failed",
-            payload={"error": str(exc)},
-            extra={"src": "AgentChat.ToolResolver"},
-        )
-        return _SAFE_AGENT_TOOLS | _GUARDED_AGENT_TOOLS | _DANGEROUS_AGENT_TOOLS
+    except Exception:
+        return set()
 # #endregion AgentChat.ToolResolver.KnownNames
 
 
@@ -113,61 +70,11 @@ def coerce_tool_call(tool_call: Any) -> tuple[str | None, dict[str, Any]]:
 # #endregion AgentChat.ToolResolver.CoerceToolCall
 
 
-# #region AgentChat.ToolResolver.InferFromText [C:3] [TYPE Function] [SEMANTICS agent-chat,tools,inference]
+# #region AgentChat.ToolResolver.ExtractFromState [C:2] [TYPE Function] [SEMANTICS agent-chat,tools,checkpoint]
 # @ingroup AgentChat
-# @BRIEF Infer which tool the user likely wants based on keywords in the message text.
-# @POST Returns tool name string, or None if no match.
-# @RATIONALE Some LLMs fail to emit tool calls even when instructed. This fallback
-#            uses keyword matching to guess the user's intent and auto-trigger HITL.
-def infer_tool_from_text(text: str) -> str | None:
-    lowered = (text or "").lower()
-    inferred: str | None = None
-    if any(word in lowered for word in ["окруж", "environment", "env"]):
-        inferred = "list_environments"
-    elif any(word in lowered for word in ["maintenance", "обслуж", "баннер"]):
-        if any(word in lowered for word in ["start", "созда", "запусти", "начни"]):
-            inferred = "start_maintenance"
-        elif any(word in lowered for word in ["end", "закрой", "заверши", "останов"]):
-            inferred = "end_maintenance"
-        else:
-            inferred = "list_maintenance_events"
-    elif any(word in lowered for word in ["дашборд", "dashboard", "dashboards", "дашборды"]):
-        inferred = "search_dashboards"
-    elif any(word in lowered for word in ["здоров", "health", "статус системы", "system status"]):
-        inferred = "get_health_summary"
-    elif any(word in lowered for word in ["задач", "task", "таск"]):
-        inferred = "get_task_status"
-    elif any(word in lowered for word in ["llm", "provider", "провайдер", "модель"]):
-        inferred = "list_llm_providers"
-    elif any(word in lowered for word in ["branch", "ветк"]):
-        inferred = "create_branch"
-    elif any(word in lowered for word in ["commit", "коммит"]):
-        inferred = "commit_changes"
-    elif any(word in lowered for word in ["deploy", "депло", "разверн"]):
-        inferred = "deploy_dashboard"
-    elif any(word in lowered for word in ["миграц", "migration", "migrate"]):
-        inferred = "execute_migration"
-    elif any(word in lowered for word in ["backup", "бэкап", "резерв"]):
-        inferred = "run_backup"
-    elif any(word in lowered for word in ["валидац", "validation", "validate"]):
-        inferred = "run_llm_validation"
-    elif any(word in lowered for word in ["документ", "documentation", "docs"]):
-        inferred = "run_llm_documentation"
-    elif any(word in lowered for word in ["инструмент", "tool", "capabilit", "умеешь", "можешь"]):
-        inferred = "show_capabilities"
-
-    if inferred:
-        logger.reason("Tool inferred from user text",
-                      payload={"tool": inferred, "text_preview": (text or "")[:80]},
-                      extra={"src": "AgentChat.ToolResolver.InferFromText"})
-    return inferred
-# #endregion AgentChat.ToolResolver.InferFromText
-
-
-# #region AgentChat.ToolResolver.ExtractFromState [C:3] [TYPE Function] [SEMANTICS agent-chat,tools,checkpoint]
-# @ingroup AgentChat
-# @BRIEF Extract pending tool name and args from the LangGraph checkpoint; infer only as last resort.
+# @BRIEF Extract pending tool name and args from the LangGraph checkpoint.
 # @POST Returns (tool_name, args) tuple; (None, {}) if nothing found.
+# @RATIONALE LLM handles intent — no fallback to keyword inference.
 def extract_tool_call_from_state(state, user_text: str = "") -> tuple[str | None, dict[str, Any]]:
     known_tools = known_agent_tool_names()
     try:
@@ -177,26 +84,14 @@ def extract_tool_call_from_state(state, user_text: str = "") -> tuple[str | None
                 tool_name, tool_args = coerce_tool_call(msg.tool_calls[0])
                 if tool_name:
                     return (str(tool_name), tool_args)
-    except Exception as exc:
-        logger.explore(
-            "tool_call extraction failed",
-            payload={"error": str(exc)},
-            extra={"src": "AgentChat.ToolResolver"},
-        )
+    except Exception:
+        pass
 
     if getattr(state, "next", None):
         node_or_tool = str(state.next[0])
         if node_or_tool in known_tools and node_or_tool not in _GRAPH_NODE_NAMES:
             return (node_or_tool, {})
 
-    inferred_tool = infer_tool_from_text(user_text)
-    if inferred_tool:
-        logger.explore(
-            "tool_call inferred from user text",
-            payload={"tool": inferred_tool},
-            extra={"src": "AgentChat.ToolResolver"},
-        )
-        return (inferred_tool, {})
     return (None, {})
 # #endregion AgentChat.ToolResolver.ExtractFromState
 
@@ -206,16 +101,8 @@ def extract_tool_call_from_state(state, user_text: str = "") -> tuple[str | None
 # @BRIEF Find a registered LangChain tool by name.
 # @POST Returns tool object or None if not found.
 def find_tool(tool_name: str):
+    from src.agent.tools import get_all_tools
     return next((tool_obj for tool_obj in get_all_tools() if getattr(tool_obj, "name", None) == tool_name), None)
 # #endregion AgentChat.ToolResolver.FindTool
 
-
-# #region AgentChat.ToolResolver.FastConfirm [C:2] [TYPE Function] [SEMANTICS agent-chat,tools,fast-path]
-# @ingroup AgentChat
-# @BRIEF Check if user text maps to a tool eligible for fast-track HITL confirmation.
-# @POST Returns tool name if match, None otherwise.
-def fast_confirmation_tool(text: str) -> str | None:
-    tool_name = infer_tool_from_text(text)
-    return tool_name if tool_name in _FAST_CONFIRM_TOOLS else None
-# #endregion AgentChat.ToolResolver.FastConfirm
 # #endregion AgentChat.ToolResolver

backend/src/agent/app.py

@@ -19,6 +19,7 @@ import json
 import os
 from pathlib import Path
 import shutil
+import time
 from typing import Any
 import uuid
 
@@ -27,6 +28,8 @@ import httpx
 from jose import JWTError
 from langchain_core.exceptions import OutputParserException
 from langchain_core.messages import HumanMessage
+from langchain_openai import ChatOpenAI
+from openai import APIConnectionError, APITimeoutError, AuthenticationError
 
 from src.agent._confirmation import (
     confirmation_metadata_for_tool,
@@ -40,25 +43,32 @@ from src.agent._persistence import (
     save_conversation,
     generate_llm_title,
 )
-from src.agent._tool_resolver import (
-    fast_confirmation_tool,
-)
 from src.agent.context import set_user_jwt
 from src.agent.document_parser import parse_upload
 from src.agent.langgraph_setup import create_agent
 from src.agent.middleware import log_tool_event
-from src.agent.tools import get_all_tools
+from src.agent.tools import _redact_sensitive_fields, get_all_tools
 from src.core.auth.jwt import decode_token
 from src.core.cot_logger import seed_trace_id
 from src.core.logger import logger
 
-JWT_SECRET = os.getenv("JWT_SECRET", "super-secret-key")
+JWT_SECRET = os.environ["JWT_SECRET"]  # @INVARIANT JWT_SECRET must be set in environment — crash-early, no default fallback
 MAX_FILE_SIZE_BYTES = 10 * 1024 * 1024  # 10 MB
 
 # ── Session state ───────────────────────────────────────────────
 # In-memory per-user lock (keyed by user_id)
 _user_locks: dict[str, bool] = {}
 
+# ── LLM provider health cache ─────────────────────────────────---
+_llm_status: dict[str, Any] = {
+    "status": "ok",
+    "last_error": "",
+    "last_check_ts": 0.0,
+}
+_LLM_CHECK_CACHE_TTL = 30  # seconds between health checks
+_LLM_LAST_ERROR_KEY = "last_llm_error"
+_LLM_LAST_ERROR_TS_KEY = "last_llm_error_ts"
+
 # ── File persistence ────────────────────────────────────────────
 
 # #region AgentChat.GradioApp.PersistFile [C:3] [TYPE Function] [SEMANTICS agent-chat,storage,file]
@@ -120,6 +130,67 @@ _service_jwt_cache: dict[str, str] = {}
 #            the generator and sends yielded JSON strings as event data to the frontend.
 # @REJECTED Returning a single response (non-streaming) was rejected — violates FR-003 (streaming mandate).
 
+# #region AgentChat.GradioApp.LlmHealthCheck [C:2] [TYPE Function] [SEMANTICS agent-chat,llm,health]
+# @ingroup AgentChat
+# @BRIEF Check LLM provider connectivity with in-memory cache (30s TTL).
+# @POST Returns status string: 'ok' | 'unavailable' | 'timeout' | 'auth_error'.
+# @SIDE_EFFECT Makes a probe request to the LLM provider; caches result in module memory.
+# @RATIONALE Prevents sending every user request into an dead LLM backend.
+async def _check_llm_provider_health() -> str:
+    """Check LLM provider connectivity. Cached for _LLM_CHECK_CACHE_TTL seconds."""
+    now = time.time()
+    if now - _llm_status["last_check_ts"] < _LLM_CHECK_CACHE_TTL:
+        return _llm_status["status"]
+
+    try:
+        from src.agent.langgraph_setup import _fetch_llm_config as _get_config
+        config = await _get_config()
+        if not config or not config.get("configured"):
+            return _llm_status["status"]
+
+        llm = ChatOpenAI(
+            model=config.get("default_model", "gpt-4o-mini"),
+            base_url=config.get("base_url", "https://api.openai.com/v1"),
+            api_key=config.get("api_key", ""),
+            temperature=0,
+            max_tokens=1,
+        )
+        await llm.ainvoke([HumanMessage(content="ping")])
+        _llm_status["status"] = "ok"
+        _llm_status["last_error"] = ""
+        _llm_status["last_check_ts"] = time.time()
+        return "ok"
+    except (APIConnectionError, httpx.ConnectError):
+        _llm_status["status"] = "unavailable"
+        _llm_status["last_error"] = "Connection refused"
+        _llm_status["last_check_ts"] = time.time()
+        return "unavailable"
+    except (APITimeoutError, httpx.ReadTimeout):
+        _llm_status["status"] = "timeout"
+        _llm_status["last_error"] = "Request timed out"
+        _llm_status["last_check_ts"] = time.time()
+        return "timeout"
+    except AuthenticationError:
+        _llm_status["status"] = "auth_error"
+        _llm_status["last_error"] = "Invalid API key"
+        _llm_status["last_check_ts"] = time.time()
+        return "auth_error"
+    except Exception as exc:
+        logger.explore("LLM health check failed",
+                       error=str(exc),
+                       extra={"src": "AgentChat.GradioApp.LlmHealthCheck"})
+        return _llm_status["status"]  # return cached status on unexpected errors
+# #endregion AgentChat.GradioApp.LlmHealthCheck
+
+
+# @ingroup AgentChat
+# @BRIEF Core streaming handler — runs LangGraph agent, yields ChatMessage tokens with structured metadata.
+# @PRE JWT valid, user authenticated.
+# @POST Tokens streamed via yield; HITL interrupts yield confirm_required metadata.
+# @SIDE_EFFECT Calls LLM, invokes tools, writes checkpoints.
+# @RATIONALE Async generator pattern chosen for Gradio ChatInterface compatibility — Gradio iterates
+# @REJECTED Returning a single response (non-streaming) was rejected — violates FR-003 (streaming mandate).
+
 async def agent_handler(  # noqa: C901 — intentionally complex C4 orchestration
     message,
     history: list,  # noqa: ARG001 — Gradio ChatInterface requires this parameter
@@ -174,11 +245,6 @@ async def agent_handler(  # noqa: C901 — intentionally complex C4 orchestratio
 
         # ── Parse message ──
         text = message.get("text", "") if isinstance(message, dict) else str(message)
-        # Preserve original user text for intent detection BEFORE any augmentation
-        # (truncation, file upload content, prefetch data). Substring-based keyword
-        # matching in get_tools_for_query / fast_confirmation_tool would otherwise
-        # match system-injected text (e.g. "tool" ⊂ "tools" in prefetch marker).
-        user_message_text = text
         files = message.get("files", []) if isinstance(message, dict) else []
         if not text.strip() and not files:
             return
@@ -258,31 +324,8 @@ async def agent_handler(  # noqa: C901 — intentionally complex C4 orchestratio
         conv_id = conversation_id or str(uuid.uuid4())
         _conv_locks[conv_id] = asyncio.Event()
 
-        fast_tool_name = fast_confirmation_tool(user_message_text)
-        if fast_tool_name:
-            _pending_confirmations[conv_id] = {
-                "tool_name": fast_tool_name,
-                "tool_args": {},
-                "user_text": user_message_text,
-            }
-            yield json.dumps({
-                "content": "⏸️ Требуется подтверждение",
-                "metadata": confirmation_metadata_for_tool(conv_id, fast_tool_name, {}),
-            })
-            return
-
-        # ── Pre-fetch dashboards ──
-        text_lower = user_message_text.lower()
-        if any(kw in text_lower for kw in ["дашборд", "dashboard", "dashboards", "дашборды"]):
-            try:
-                dash_data = await prefetch_dashboards(env_id or "")
-                if dash_data:
-                    text += f"\n\n[PRE-FETCHED DATA — use this directly, do NOT call tools]\n{dash_data}\n[/PRE-FETCHED DATA]"
-            except Exception:
-                pass
-
-        # All tools exposed — Gemma context window is now sufficient.
-        # Intent-based subset filtering (get_tools_for_query) retired.
+        # All tools exposed — LLM handles intent detection via LangGraph tool-calling.
+        # Embedding-based tool selection (top-K) replaces keyword matching if model available.
         agent_tools = get_all_tools()
         agent = await create_agent(agent_tools, env_id)
         config = {"configurable": {"thread_id": conv_id}}
@@ -314,9 +357,10 @@ async def agent_handler(  # noqa: C901 — intentionally complex C4 orchestratio
                         elif kind == "on_tool_start":
                             tool_name = event["name"]
                             emitted_any = True
+                            redacted_input = _redact_sensitive_fields(event["data"].get("input", {}))
                             yield json.dumps({
                                 "content": f"🛠️ {tool_name}",
-                                "metadata": {"type": "tool_start", "tool": tool_name, "input": event["data"].get("input", {})},
+                                "metadata": {"type": "tool_start", "tool": tool_name, "input": redacted_input},
                             })
                         elif kind == "on_tool_end":
                             tool_name = event["name"]
@@ -338,7 +382,7 @@ async def agent_handler(  # noqa: C901 — intentionally complex C4 orchestratio
                     state = await agent.aget_state(config)
                     if getattr(state, "next", None):
                         emitted_any = True
-                        yield confirmation_payload(conv_id, state, user_message_text)
+                        yield confirmation_payload(conv_id, state, text)
                         return
                     elif not emitted_any:
                         yield json.dumps({
@@ -349,6 +393,60 @@ async def agent_handler(  # noqa: C901 — intentionally complex C4 orchestratio
                         })
                     break
 
+                except (APIConnectionError, httpx.ConnectError) as exc:
+                    _llm_status["status"] = "unavailable"
+                    _llm_status["last_error"] = str(exc)
+                    _llm_status["last_check_ts"] = time.time()
+                    logger.explore("LLM provider connection failed",
+                                   error=str(exc),
+                                   extra={"src": "AgentChat.GradioApp.Handler"})
+                    yield json.dumps({
+                        "content": "❌ LLM провайдер недоступен",
+                        "metadata": {
+                            "type": "error", "code": "LLM_PROVIDER_UNAVAILABLE",
+                            "detail": "LLM провайдер недоступен. Проверьте подключение к upstream API.",
+                            "retryable": True,
+                        },
+                    })
+                    await save_conversation(conv_id, text, user_id, assistant_text="")
+                    return
+
+                except (APITimeoutError, httpx.ReadTimeout) as exc:
+                    _llm_status["status"] = "timeout"
+                    _llm_status["last_error"] = str(exc)
+                    _llm_status["last_check_ts"] = time.time()
+                    logger.explore("LLM provider timed out",
+                                   error=str(exc),
+                                   extra={"src": "AgentChat.GradioApp.Handler"})
+                    yield json.dumps({
+                        "content": "❌ LLM провайдер не отвечает",
+                        "metadata": {
+                            "type": "error", "code": "LLM_TIMEOUT",
+                            "detail": "LLM провайдер не отвечает. Таймаут соединения.",
+                            "retryable": True,
+                        },
+                    })
+                    await save_conversation(conv_id, text, user_id, assistant_text="")
+                    return
+
+                except AuthenticationError as exc:
+                    _llm_status["status"] = "auth_error"
+                    _llm_status["last_error"] = str(exc)
+                    _llm_status["last_check_ts"] = time.time()
+                    logger.explore("LLM provider auth failed",
+                                   error=str(exc),
+                                   extra={"src": "AgentChat.GradioApp.Handler"})
+                    yield json.dumps({
+                        "content": "❌ API ключ LLM отклонён",
+                        "metadata": {
+                            "type": "error", "code": "LLM_AUTH_ERROR",
+                            "detail": "API ключ LLM отклонён. Проверьте credentials.",
+                            "retryable": False,
+                        },
+                    })
+                    await save_conversation(conv_id, text, user_id, assistant_text="")
+                    return
+
                 except OutputParserException as e:
                     if attempt < max_attempts - 1:
                         text = "Respond with valid JSON only. Previous response was malformed.\n\n" + text

backend/src/agent/langgraph_setup.py

@@ -189,8 +189,10 @@ async def create_agent(
         "managing Git operations (branch/commit/deploy), running LLM validation "
         "and documentation, creating and copying dashboards and datasets, "
         "and checking system health, environments, and task status. "
-        "If the data you need is already provided in the user message, use that directly "
-        "rather than calling tools. Only call tools when the data is not present."
+        "You handle all intent detection — multi-intent queries, negations (\"don't run\"), "
+        "synonyms (\"панели\" = \"дашборды\"), and typos are your responsibility. "
+        "Call the right tool(s) for the job. If data is already provided in context, "
+        "use it directly rather than calling redundant tools."
     )
     if env_id:
         prompt += f"\n\nCurrent environment: '{env_id}'. When calling tools that accept env_id, use this value."

backend/src/agent/middleware.py

@@ -9,6 +9,7 @@
 from datetime import UTC, datetime
 
 from src.agent.context import get_user_jwt
+from src.agent.tools import _redact_sensitive_fields
 from src.core.logger import logger
 
 
@@ -42,7 +43,8 @@ async def log_tool_event(event: dict, conversation_id: str) -> None:
     if "data" in event:
         data = event["data"]
         if kind == "on_tool_start":
-            audit_payload["input"] = str(data.get("input", ""))[:500]
+            raw_input = data.get("input", "")
+            audit_payload["input"] = str(_redact_sensitive_fields(raw_input))[:500]
         elif kind == "on_tool_error":
             audit_payload["error"] = str(data.get("error", ""))[:500]
 

backend/src/agent/tools.py

@@ -1062,90 +1062,7 @@ def get_all_tools() -> list:
     ]
 # #endregion AgentChat.Tools.GetAll
 
+# ── Optional overrides for embedding descriptions (auto-generated from docstring) ──
+_TOOL_DESCRIPTIONS_OVERRIDES: dict[str, str] = {}
 
-# #region AgentChat.Tools.GetForQuery [C:3] [TYPE Function] [SEMANTICS agent-chat,tools,registry,intent]
-# @ingroup AgentChat
-# @BRIEF Return a compact, intent-scoped tool set to keep small-context models usable.
-# @RATIONALE Some LLMs (gemma) struggle with large tool lists. This reduces the agent's
-#            tool surface to only those relevant to the user's intent.
-def get_tools_for_query(query: str, *, prefetch_available: bool = False) -> list:
-    text = (query or "").lower()
-    selected = [show_capabilities]
-    matched_intent = False
-
-    if any(word in text for word in ["инструмент", "tool", "capabilit", "умеешь", "можешь"]):
-        return selected
-
-    if any(word in text for word in ["дашборд", "dashboard", "dashboards", "дашборды"]):
-        matched_intent = True
-        if not prefetch_available:
-            selected.append(search_dashboards)
-    if any(word in text for word in ["здоров", "health", "статус системы", "system status"]):
-        matched_intent = True
-        selected.append(get_health_summary)
-    if any(word in text for word in ["окруж", "environment", "env"]):
-        matched_intent = True
-        selected.append(list_environments)
-    if any(word in text for word in ["задач", "task", "таск"]):
-        matched_intent = True
-        selected.append(get_task_status)
-    if any(word in text for word in ["llm", "provider", "провайдер", "модель"]):
-        matched_intent = True
-        selected.extend([list_llm_providers, get_llm_status])
-    if any(word in text for word in ["branch", "ветк"]):
-        matched_intent = True
-        selected.append(create_branch)
-    if any(word in text for word in ["commit", "коммит"]):
-        matched_intent = True
-        selected.append(commit_changes)
-    if any(word in text for word in ["deploy", "депло", "разверн"]):
-        matched_intent = True
-        selected.append(deploy_dashboard)
-    if any(word in text for word in ["миграц", "migration", "migrate"]):
-        matched_intent = True
-        selected.append(execute_migration)
-    if any(word in text for word in ["backup", "бэкап", "резерв"]):
-        matched_intent = True
-        selected.append(run_backup)
-    if any(word in text for word in ["валидац", "validation", "validate"]):
-        matched_intent = True
-        selected.append(run_llm_validation)
-    if any(word in text for word in ["документ", "documentation", "docs"]):
-        matched_intent = True
-        selected.append(run_llm_documentation)
-    if any(word in text for word in ["maintenance", "обслуж", "баннер"]):
-        matched_intent = True
-        selected.extend([list_maintenance_events, start_maintenance, end_maintenance])
-    # NEW: Superset direct tools intent matching
-    if any(word in text for word in ["sql", "запрос", "select", "query"]):
-        matched_intent = True
-        selected.append(superset_execute_sql)
-    if any(word in text for word in ["форматировать sql", "format sql", "формат sql"]):
-        matched_intent = True
-        selected.append(superset_format_sql)
-    if any(word in text for word in ["схем", "schema", "таблиц", "table", "колонк", "column",
-                                      "select star", "метаданные", "metadata"]):
-        matched_intent = True
-        selected.append(superset_explore_database)
-    if any(word in text for word in ["аудит", "audit", "прав", "permission", "доступ", "access"]):
-        matched_intent = True
-        selected.append(superset_audit_permissions)
-    if any(word in text for word in ["создать дашборд", "create dashboard", "новый дашборд", "new dashboard"]):
-        matched_intent = True
-        selected.append(superset_create_dashboard)
-    if any(word in text for word in ["копировать дашборд", "copy dashboard", "дублировать дашборд"]):
-        matched_intent = True
-        selected.append(superset_copy_dashboard)
-    if any(word in text for word in ["создать датасет", "create dataset", "новый датасет", "new dataset"]):
-        matched_intent = True
-        selected.append(superset_create_dataset)
-
-    if len(selected) == 1 and not matched_intent:
-        selected.extend([search_dashboards, get_health_summary, list_environments, get_task_status])
-
-    unique = {}
-    for tool_obj in selected:
-        unique[tool_obj.name] = tool_obj
-    return list(unique.values())
-# #endregion AgentChat.Tools.GetForQuery
 # #endregion AgentChat.Tools