fix: correct task scheduling and simplify agent internals

Fix queue cancellation, concurrency limits and worker status updates.

Use direct Explorer imports, split the Diagnoser loop into helpers and remove unused tool registration and prompt-builder code.

Correct model caching, endpoint selection and retry handling.

artemis/services/llm.py

@@ -21,7 +21,6 @@ thought stream recording, and role-based dynamic dispatching.
 import asyncio
 from collections.abc import Awaitable, Callable, Coroutine
 from contextvars import ContextVar, Token
-from dataclasses import replace
 import functools
 import logging
 from pathlib import Path
@@ -110,6 +109,9 @@ _ENDPOINT_BREAKER = CircuitBreaker(threshold=3, cooldown_seconds=30.0)
 # remembered so subsequent calls go straight to non-streaming invocation.
 _NON_STREAMING_ENDPOINTS: set[str] = set()
 
+# Total retry limit when failures switch categories within one recovery cycle.
+_MAX_TOTAL_RETRY_ATTEMPTS = 8
+
 
 def _get_current_data_engine():
     if _CURRENT_DATA_ENGINE is not None:
@@ -442,6 +444,13 @@ async def _run_with_recovery[T](
                     policy = retry_policy_for(failure.category)
                     if attempt >= policy.max_attempts:
                         break
+                    total_attempts = sum(attempts.values())
+                    if total_attempts >= _MAX_TOTAL_RETRY_ATTEMPTS:
+                        llm_logger.warning(
+                            "LLM retry total across failure categories reached"
+                            f" {total_attempts}; treating as exhausted."
+                        )
+                        break
                     delay = policy.delay_for(attempt)
                     _record_llm_retry(
                         str(e),
@@ -753,10 +762,17 @@ class RobustChatModelWrapper:
 
 
 def _is_stream_unsupported_error(error: BaseException) -> bool:
-    """Detect 'this endpoint/model cannot stream' as opposed to a transient failure."""
-    if isinstance(error, (NotImplementedError, AttributeError, TypeError)):
+    """Detect 'this endpoint/model cannot stream' as opposed to a transient failure.
+
+    A positive here permanently disables streaming for the endpoint in this
+    process, so a bare AttributeError/TypeError from unrelated code must not
+    qualify — the message has to actually implicate streaming.
+    """
+    if isinstance(error, NotImplementedError):
         return True
     message = str(error).lower()
+    if isinstance(error, (AttributeError, TypeError)):
+        return "stream" in message
     return "stream" in message and any(
         marker in message
         for marker in ("not support", "unsupported", "not implemented", "not available")
@@ -1106,7 +1122,7 @@ def get_llm(
     """Resolves and instantiates the appropriate LLM wrapper for the given agent role."""
     endpoint = _resolve_endpoint(ctx, str(name), is_utils=is_utils, use_fallback=use_fallback)
     if temperature is not None:
-        endpoint = replace(endpoint, temperature=temperature)
+        endpoint = endpoint.model_copy(update={"temperature": temperature})
     raw_model = ModelFactory.get_model(endpoint)
 
     handler = DataEngineCallbackHandler(ctx)