diff --git a/nanobot/agent/runner.py b/nanobot/agent/runner.py
index e92d864f2..5cb7b4f0e 100644
--- a/nanobot/agent/runner.py
+++ b/nanobot/agent/runner.py
@@ -134,6 +134,36 @@ class AgentRunner:
                 continue
             messages.append(injection)
 
+    async def _try_drain_injections(
+        self,
+        spec: AgentRunSpec,
+        messages: list[dict[str, Any]],
+        assistant_message: dict[str, Any] | None,
+        injection_cycles: int,
+        *,
+        phase: str = "after error",
+    ) -> tuple[bool, int]:
+        """Drain pending injections. Returns (should_continue, updated_cycles).
+
+        If injections are found and we haven't exceeded _MAX_INJECTION_CYCLES,
+        append them to *messages* and return (True, cycles+1) so the caller
+        continues the iteration loop.  Otherwise return (False, cycles).
+        """
+        if injection_cycles >= _MAX_INJECTION_CYCLES:
+            return False, injection_cycles
+        injections = await self._drain_injections(spec)
+        if not injections:
+            return False, injection_cycles
+        injection_cycles += 1
+        if assistant_message is not None:
+            messages.append(assistant_message)
+        self._append_injected_messages(messages, injections)
+        logger.info(
+            "Injected {} follow-up message(s) {} ({}/{})",
+            len(injections), phase, injection_cycles, _MAX_INJECTION_CYCLES,
+        )
+        return True, injection_cycles
+
     async def _drain_injections(self, spec: AgentRunSpec) -> list[dict[str, Any]]:
         """Drain pending user messages via the injection callback.
 
@@ -287,6 +317,13 @@ class AgentRunner:
                     context.error = error
                     context.stop_reason = stop_reason
                     await hook.after_iteration(context)
+                    should_continue, injection_cycles = await self._try_drain_injections(
+                        spec, messages, None, injection_cycles,
+                        phase="after tool error",
+                    )
+                    if should_continue:
+                        had_injections = True
+                        continue
                     break
                 await self._emit_checkpoint(
                     spec,
@@ -379,36 +416,31 @@ class AgentRunner:
             # Check for mid-turn injections BEFORE signaling stream end.
             # If injections are found we keep the stream alive (resuming=True)
             # so streaming channels don't prematurely finalize the card.
-            _injected_after_final = False
-            if injection_cycles < _MAX_INJECTION_CYCLES:
-                injections = await self._drain_injections(spec)
-                if injections:
-                    had_injections = True
-                    injection_cycles += 1
-                    _injected_after_final = True
-                    if assistant_message is not None:
-                        messages.append(assistant_message)
-                        await self._emit_checkpoint(
-                            spec,
-                            {
-                                "phase": "final_response",
-                                "iteration": iteration,
-                                "model": spec.model,
-                                "assistant_message": assistant_message,
-                                "completed_tool_results": [],
-                                "pending_tool_calls": [],
-                            },
-                        )
-                    self._append_injected_messages(messages, injections)
-                    logger.info(
-                        "Injected {} follow-up message(s) after final response ({}/{})",
-                        len(injections), injection_cycles, _MAX_INJECTION_CYCLES,
+            should_continue, injection_cycles = await self._try_drain_injections(
+                spec, messages, assistant_message, injection_cycles,
+                phase="after final response",
+            )
+            if should_continue:
+                had_injections = True
+                # Emit checkpoint for the assistant message that was appended
+                # by _try_drain_injections, then keep the stream alive.
+                if assistant_message is not None:
+                    await self._emit_checkpoint(
+                        spec,
+                        {
+                            "phase": "final_response",
+                            "iteration": iteration,
+                            "model": spec.model,
+                            "assistant_message": assistant_message,
+                            "completed_tool_results": [],
+                            "pending_tool_calls": [],
+                        },
                     )
 
             if hook.wants_streaming():
-                await hook.on_stream_end(context, resuming=_injected_after_final)
+                await hook.on_stream_end(context, resuming=should_continue)
 
-            if _injected_after_final:
+            if should_continue:
                 await hook.after_iteration(context)
                 continue
 
@@ -421,6 +453,13 @@ class AgentRunner:
                 context.error = error
                 context.stop_reason = stop_reason
                 await hook.after_iteration(context)
+                should_continue, injection_cycles = await self._try_drain_injections(
+                    spec, messages, None, injection_cycles,
+                    phase="after LLM error",
+                )
+                if should_continue:
+                    had_injections = True
+                    continue
                 break
             if is_blank_text(clean):
                 final_content = EMPTY_FINAL_RESPONSE_MESSAGE
@@ -431,6 +470,13 @@ class AgentRunner:
                 context.error = error
                 context.stop_reason = stop_reason
                 await hook.after_iteration(context)
+                should_continue, injection_cycles = await self._try_drain_injections(
+                    spec, messages, None, injection_cycles,
+                    phase="after empty response",
+                )
+                if should_continue:
+                    had_injections = True
+                    continue
                 break
 
             messages.append(assistant_message or build_assistant_message(
@@ -467,6 +513,15 @@ class AgentRunner:
                     max_iterations=spec.max_iterations,
                 )
             self._append_final_message(messages, final_content)
+            # Drain any remaining injections so they are appended to the
+            # conversation history instead of being re-published as
+            # independent inbound messages by _dispatch's finally block.
+            # We ignore should_continue here because the for-loop has already
+            # exhausted all iterations.
+            _, injection_cycles = await self._try_drain_injections(
+                spec, messages, None, injection_cycles,
+                phase="after max_iterations",
+            )
 
         return AgentRunResult(
             final_content=final_content,
diff --git a/tests/agent/test_runner.py b/tests/agent/test_runner.py
index a62457aa8..4a943165c 100644
--- a/tests/agent/test_runner.py
+++ b/tests/agent/test_runner.py
@@ -2410,3 +2410,236 @@ async def test_dispatch_republishes_leftover_queue_messages(tmp_path):
     contents = [m.content for m in msgs]
     assert "leftover-1" in contents
     assert "leftover-2" in contents
+
+
+@pytest.mark.asyncio
+async def test_drain_injections_on_fatal_tool_error():
+    """Pending injections should be drained even when a fatal tool error occurs."""
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+    from nanobot.bus.events import InboundMessage
+
+    provider = MagicMock()
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        if call_count["n"] == 1:
+            return LLMResponse(
+                content="",
+                tool_calls=[ToolCallRequest(id="c1", name="exec", arguments={"cmd": "bad"})],
+                usage={},
+            )
+        # Second call: respond normally to the injected follow-up
+        return LLMResponse(content="reply to follow-up", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(side_effect=RuntimeError("tool exploded"))
+
+    injection_queue = asyncio.Queue()
+
+    async def inject_cb():
+        items = []
+        while not injection_queue.empty():
+            items.append(await injection_queue.get())
+        return items
+
+    await injection_queue.put(
+        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="follow-up after error")
+    )
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[{"role": "user", "content": "hello"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=5,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        fail_on_tool_error=True,
+        injection_callback=inject_cb,
+    ))
+
+    assert result.had_injections is True
+    assert result.final_content == "reply to follow-up"
+    # The injection should be in the messages history
+    injected = [
+        m for m in result.messages
+        if m.get("role") == "user" and m.get("content") == "follow-up after error"
+    ]
+    assert len(injected) == 1
+
+
+@pytest.mark.asyncio
+async def test_drain_injections_on_llm_error():
+    """Pending injections should be drained when the LLM returns an error finish_reason."""
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+    from nanobot.bus.events import InboundMessage
+
+    provider = MagicMock()
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        if call_count["n"] == 1:
+            return LLMResponse(
+                content=None,
+                tool_calls=[],
+                finish_reason="error",
+                usage={},
+            )
+        # Second call: respond normally to the injected follow-up
+        return LLMResponse(content="recovered answer", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+
+    injection_queue = asyncio.Queue()
+
+    async def inject_cb():
+        items = []
+        while not injection_queue.empty():
+            items.append(await injection_queue.get())
+        return items
+
+    await injection_queue.put(
+        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="follow-up after LLM error")
+    )
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[
+            {"role": "user", "content": "hello"},
+            {"role": "assistant", "content": "previous response"},
+            {"role": "user", "content": "trigger error"},
+        ],
+        tools=tools,
+        model="test-model",
+        max_iterations=5,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        injection_callback=inject_cb,
+    ))
+
+    assert result.had_injections is True
+    assert result.final_content == "recovered answer"
+    injected = [
+        m for m in result.messages
+        if m.get("role") == "user" and "follow-up after LLM error" in str(m.get("content", ""))
+    ]
+    assert len(injected) == 1
+
+
+@pytest.mark.asyncio
+async def test_drain_injections_on_empty_final_response():
+    """Pending injections should be drained when the runner exits due to empty response."""
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner, _MAX_EMPTY_RETRIES
+    from nanobot.bus.events import InboundMessage
+
+    provider = MagicMock()
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        if call_count["n"] <= _MAX_EMPTY_RETRIES + 1:
+            return LLMResponse(content="", tool_calls=[], usage={})
+        # After retries exhausted + injection drain, respond normally
+        return LLMResponse(content="answer after empty", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+
+    injection_queue = asyncio.Queue()
+
+    async def inject_cb():
+        items = []
+        while not injection_queue.empty():
+            items.append(await injection_queue.get())
+        return items
+
+    await injection_queue.put(
+        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="follow-up after empty")
+    )
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[
+            {"role": "user", "content": "hello"},
+            {"role": "assistant", "content": "previous response"},
+            {"role": "user", "content": "trigger empty"},
+        ],
+        tools=tools,
+        model="test-model",
+        max_iterations=10,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        injection_callback=inject_cb,
+    ))
+
+    assert result.had_injections is True
+    assert result.final_content == "answer after empty"
+    injected = [
+        m for m in result.messages
+        if m.get("role") == "user" and "follow-up after empty" in str(m.get("content", ""))
+    ]
+    assert len(injected) == 1
+
+
+@pytest.mark.asyncio
+async def test_drain_injections_on_max_iterations():
+    """Pending injections should be drained when the runner hits max_iterations.
+
+    Unlike other error paths, max_iterations cannot continue the loop, so
+    injections are appended to messages but not processed by the LLM.
+    The key point is they are consumed from the queue to prevent re-publish.
+    """
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+    from nanobot.bus.events import InboundMessage
+
+    provider = MagicMock()
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        return LLMResponse(
+            content="",
+            tool_calls=[ToolCallRequest(id=f"c{call_count['n']}", name="read_file", arguments={"path": "x"})],
+            usage={},
+        )
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(return_value="file content")
+
+    injection_queue = asyncio.Queue()
+
+    async def inject_cb():
+        items = []
+        while not injection_queue.empty():
+            items.append(await injection_queue.get())
+        return items
+
+    await injection_queue.put(
+        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="follow-up after max iters")
+    )
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[{"role": "user", "content": "hello"}],
+        tools=tools,
+        model="test-model",
+        max_iterations=2,
+        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
+        injection_callback=inject_cb,
+    ))
+
+    assert result.stop_reason == "max_iterations"
+    # The injection was consumed from the queue (preventing re-publish)
+    assert injection_queue.empty()
+    # The injection message is appended to conversation history
+    injected = [
+        m for m in result.messages
+        if m.get("role") == "user" and m.get("content") == "follow-up after max iters"
+    ]
+    assert len(injected) == 1