fix(agent): drop the empty assistant turn from an approved-action replay

The approved-action replay appends the sealed tool result with no assistant
prose for that round, which produced an assistant message with content "".
Anthropic's Messages API rejects a non-final assistant message with empty
content, so a resumed turn after a tool approval failed before the model saw
the result. A turn carrying neither prose nor reasoning has nothing to say to
any provider, so it is no longer appended. A round with prose, and a
reasoning-only round that DeepSeek thinking mode needs, both still append.

src/agent_loop.py

@@ -3081,10 +3081,17 @@ def _append_tool_results(
             messages.append(result_message)
     else:
         tool_output_text = "\n\n".join(tool_results)
-        msg = {"role": "assistant", "content": round_response}
+        # An approved-action replay injects the sealed tool result with no
-        if round_reasoning:
+        # assistant prose for that round, which used to append an assistant turn
-            msg["reasoning_content"] = round_reasoning
+        # whose content was "". Anthropic's Messages API rejects a non-final
-        messages.append(msg)
+        # assistant message with empty content (HTTP 400), so the resumed turn
+        # died before the model saw the result. A turn carrying neither prose nor
+        # reasoning has nothing to say to any provider, so skip it entirely.
+        if round_response.strip() or round_reasoning:
+            msg = {"role": "assistant", "content": round_response}
+            if round_reasoning:
+                msg["reasoning_content"] = round_reasoning
+            messages.append(msg)
         # Tool output (shell/python stdout, file reads, fetched pages, email
         # bodies, MCP results) is sourced from outside the server. Wrap it as
         # untrusted data so prompt-injection inside a tool result is treated as

tests/test_approved_replay_message_shape.py

@@ -0,0 +1,100 @@
+"""Regression coverage for the assistant turn an approved-action replay appends.
+
+Anthropic's Messages API rejects a non-final assistant message whose content is
+empty, so the resumed turn after a tool approval used to fail before the model
+ever saw the sealed result. The replay injects its result with no assistant
+prose for that round, which is the only path that produced such a turn.
+"""
+
+import src.llm_core as llm_core
+from src.agent_loop import _append_tool_results
+
+_RESULT = "bash: ok\nhello"
+_RECORD = {
+    "tool_name": "bash",
+    "content": "printf hello",
+    "result": {"output": "hello", "exit_code": 0},
+    "text": _RESULT,
+}
+
+
+def _replay_messages(round_response="", round_reasoning=""):
+    """Mirror the approved-action injection in stream_agent_loop."""
+    messages = [
+        {"role": "system", "content": "system preface"},
+        {"role": "user", "content": "run the command and summarise it"},
+    ]
+    _append_tool_results(
+        messages,
+        round_response,
+        [],
+        [_RESULT],
+        [_RESULT],
+        False,
+        0,
+        round_reasoning=round_reasoning,
+        tool_result_records=[_RECORD],
+    )
+    return messages
+
+
+def _empty_assistant_turns(messages):
+    return [
+        index
+        for index, message in enumerate(messages)
+        if message.get("role") == "assistant"
+        and not str(message.get("content") or "").strip()
+        and not message.get("tool_calls")
+    ]
+
+
+def test_replay_appends_no_empty_assistant_turn():
+    assert _empty_assistant_turns(_replay_messages()) == []
+
+
+def test_replay_payload_is_a_single_non_empty_user_turn_for_anthropic():
+    # Asserting the whole sequence rather than scanning a slice: once the empty
+    # spacer is gone the payload is one message, so a "no offenders in
+    # chat[:-1]" check would pass without inspecting anything.
+    sanitized = llm_core._sanitize_llm_messages(_replay_messages())
+    payload = llm_core._build_anthropic_payload(
+        "claude-sonnet-5", sanitized, 0.2, 512
+    )
+    chat = payload["messages"]
+    assert [message["role"] for message in chat] == ["user"]
+    assert all(str(message.get("content") or "").strip() for message in chat)
+
+
+def test_replay_merges_tool_output_after_the_request_with_its_fence_intact():
+    # Dropping the empty assistant turn leaves two adjacent user messages, which
+    # the sanitizer merges. Pin that shape: the tool output must still sit
+    # behind its untrusted fence and must not precede the operator's request.
+    sanitized = llm_core._sanitize_llm_messages(_replay_messages())
+    user_turns = [m for m in sanitized if m.get("role") == "user"]
+    assert len(user_turns) == 1
+    merged = user_turns[0]["content"]
+    assert merged.index("run the command") < merged.index("UNTRUSTED SOURCE DATA")
+    assert merged.index("UNTRUSTED SOURCE DATA") < merged.index("hello")
+
+
+def test_a_round_with_prose_still_appends_its_assistant_turn():
+    messages = _replay_messages(round_response="running that now")
+    assistants = [m for m in messages if m.get("role") == "assistant"]
+    assert [m["content"] for m in assistants] == ["running that now"]
+
+
+def test_reasoning_only_round_keeps_its_carrier_and_its_known_empty_content():
+    """Characterises the one empty-content turn this change deliberately leaves.
+
+    A round with reasoning and no prose still appends its carrier, and that
+    carrier's content is still "", which Anthropic would still reject. Dropping
+    it would lose the reasoning DeepSeek thinking mode requires on the next
+    request, and the approval replay never takes this path because it passes no
+    reasoning. Left alone on purpose; this test makes the gap visible instead of
+    silent, and should be updated by whoever closes it.
+    """
+    messages = _replay_messages(round_reasoning="thinking about it")
+    assistants = [m for m in messages if m.get("role") == "assistant"]
+    assert len(assistants) == 1
+    assert assistants[0].get("reasoning_content") == "thinking about it"
+    assert assistants[0]["content"] == ""