fix(agent): stop reporting broken streams as output-length truncation (#36705)

A stream that drops mid-response after tokens are delivered (peer-closed
connection, stale-stream reconnect) is converted into a synthetic
finish_reason="length" stub. The conversation loop treated that network
stall as a max-output-tokens truncation: when the dropped content was a
tool call it retried exactly once, then hard-failed with "Response
truncated due to output length limit" — even on large-output models that
never hit any cap (e.g. Opus).

- Tool-call truncation now retries up to 3 times (was 1) with a
  progressive max_tokens boost, and is stub-aware: a PARTIAL_STREAM_STUB_ID
  stall prints "Stream interrupted mid tool-call — retrying (n/3)" instead
  of the false "model hit max output tokens", and the give-up message
  distinguishes a network drop from a real truncation.
- Length-continuation retries preserve the original request's output cap
  as a floor, so a high provider/model default isn't silently downshifted
  to 8K/12K on retry.
- Added _requested_output_cap_from_api_kwargs() helper.

Tests: stub-stall mid-tool-call recovery within 3 retries; continuation
preserves a large provider-default output cap.

Fixes #26425. Salvages the substance of #26427 (cap floor) and #9525
(retry bump), adapted to the post-refactor conversation_loop.py which
handles all three api_modes uniformly.

Co-authored-by: LeonSGP43 <cine.dreamer.one@gmail.com>
Co-authored-by: ygd58 <ygd58@users.noreply.github.com>

run_agent.py

@@ -1206,6 +1206,21 @@ class AIAgent:
             return {"max_completion_tokens": value}
         return {"max_tokens": value}
 
+    @staticmethod
+    def _requested_output_cap_from_api_kwargs(api_kwargs: Any) -> Optional[int]:
+        """Extract the outgoing response token cap from a prepared request."""
+        if not isinstance(api_kwargs, dict):
+            return None
+        for key in ("max_output_tokens", "max_completion_tokens", "max_tokens"):
+            raw = api_kwargs.get(key)
+            try:
+                value = int(raw)
+            except (TypeError, ValueError):
+                continue
+            if value > 0:
+                return value
+        return None
+
     def _has_content_after_think_block(self, content: str) -> bool:
         """
         Check if content has actual text after any reasoning/thinking blocks.