refactor: extract shared agent runner and preserve subagent progress on failure

nanobot/agent/loop.py

@@ -15,6 +15,7 @@ from loguru import logger
 
 from nanobot.agent.context import ContextBuilder
 from nanobot.agent.memory import MemoryConsolidator
+from nanobot.agent.runner import AgentRunSpec, AgentRunner
 from nanobot.agent.subagent import SubagentManager
 from nanobot.agent.tools.cron import CronTool
 from nanobot.agent.skills import BUILTIN_SKILLS_DIR
@@ -87,6 +88,7 @@ class AgentLoop:
         self.context = ContextBuilder(workspace, timezone=timezone)
         self.sessions = session_manager or SessionManager(workspace)
         self.tools = ToolRegistry()
+        self.runner = AgentRunner(provider)
         self.subagents = SubagentManager(
             provider=provider,
             workspace=workspace,
@@ -214,11 +216,6 @@ class AgentLoop:
         ``resuming=True`` means tool calls follow (spinner should restart);
         ``resuming=False`` means this is the final response.
         """
-        messages = initial_messages
-        iteration = 0
-        final_content = None
-        tools_used: list[str] = []
-
         # Wrap on_stream with stateful think-tag filter so downstream
         # consumers (CLI, channels) never see <think> blocks.
         _raw_stream = on_stream
@@ -234,104 +231,47 @@ class AgentLoop:
             if incremental and _raw_stream:
                 await _raw_stream(incremental)
 
-        while iteration < self.max_iterations:
-            iteration += 1
+        async def _wrapped_stream_end(*, resuming: bool = False) -> None:
+            nonlocal _stream_buf
+            if on_stream_end:
+                await on_stream_end(resuming=resuming)
+            _stream_buf = ""
 
-            tool_defs = self.tools.get_definitions()
+        async def _handle_tool_calls(response) -> None:
+            if not on_progress:
+                return
+            if not on_stream:
+                thought = self._strip_think(response.content)
+                if thought:
+                    await on_progress(thought)
+            tool_hint = self._strip_think(self._tool_hint(response.tool_calls))
+            await on_progress(tool_hint, tool_hint=True)
 
-            if on_stream:
-                response = await self.provider.chat_stream_with_retry(
-                    messages=messages,
-                    tools=tool_defs,
-                    model=self.model,
-                    on_content_delta=_filtered_stream,
-                )
-            else:
-                response = await self.provider.chat_with_retry(
-                    messages=messages,
-                    tools=tool_defs,
-                    model=self.model,
-                )
+        async def _prepare_tools(tool_calls) -> None:
+            for tc in tool_calls:
+                args_str = json.dumps(tc.arguments, ensure_ascii=False)
+                logger.info("Tool call: {}({})", tc.name, args_str[:200])
+            self._set_tool_context(channel, chat_id, message_id)
 
-            usage = response.usage or {}
-            self._last_usage = {
-                "prompt_tokens": int(usage.get("prompt_tokens", 0) or 0),
-                "completion_tokens": int(usage.get("completion_tokens", 0) or 0),
-            }
-
-            if response.has_tool_calls:
-                if on_stream and on_stream_end:
-                    await on_stream_end(resuming=True)
-                    _stream_buf = ""
-
-                if on_progress:
-                    if not on_stream:
-                        thought = self._strip_think(response.content)
-                        if thought:
-                            await on_progress(thought)
-                    tool_hint = self._tool_hint(response.tool_calls)
-                    tool_hint = self._strip_think(tool_hint)
-                    await on_progress(tool_hint, tool_hint=True)
-
-                tool_call_dicts = [
-                    tc.to_openai_tool_call()
-                    for tc in response.tool_calls
-                ]
-                messages = self.context.add_assistant_message(
-                    messages, response.content, tool_call_dicts,
-                    reasoning_content=response.reasoning_content,
-                    thinking_blocks=response.thinking_blocks,
-                )
-
-                for tc in response.tool_calls:
-                    tools_used.append(tc.name)
-                    args_str = json.dumps(tc.arguments, ensure_ascii=False)
-                    logger.info("Tool call: {}({})", tc.name, args_str[:200])
-
-                # Re-bind tool context right before execution so that
-                # concurrent sessions don't clobber each other's routing.
-                self._set_tool_context(channel, chat_id, message_id)
-
-                # Execute all tool calls concurrently — the LLM batches
-                # independent calls in a single response on purpose.
-                # return_exceptions=True ensures all results are collected
-                # even if one tool is cancelled or raises BaseException.
-                results = await asyncio.gather(*(
-                    self.tools.execute(tc.name, tc.arguments)
-                    for tc in response.tool_calls
-                ), return_exceptions=True)
-
-                for tool_call, result in zip(response.tool_calls, results):
-                    if isinstance(result, BaseException):
-                        result = f"Error: {type(result).__name__}: {result}"
-                    messages = self.context.add_tool_result(
-                        messages, tool_call.id, tool_call.name, result
-                    )
-            else:
-                if on_stream and on_stream_end:
-                    await on_stream_end(resuming=False)
-                    _stream_buf = ""
-
-                clean = self._strip_think(response.content)
-                if response.finish_reason == "error":
-                    logger.error("LLM returned error: {}", (clean or "")[:200])
-                    final_content = clean or "Sorry, I encountered an error calling the AI model."
-                    break
-                messages = self.context.add_assistant_message(
-                    messages, clean, reasoning_content=response.reasoning_content,
-                    thinking_blocks=response.thinking_blocks,
-                )
-                final_content = clean
-                break
-
-        if final_content is None and iteration >= self.max_iterations:
+        result = await self.runner.run(AgentRunSpec(
+            initial_messages=initial_messages,
+            tools=self.tools,
+            model=self.model,
+            max_iterations=self.max_iterations,
+            on_stream=_filtered_stream if on_stream else None,
+            on_stream_end=_wrapped_stream_end if on_stream else None,
+            on_tool_calls=_handle_tool_calls,
+            before_execute_tools=_prepare_tools,
+            finalize_content=self._strip_think,
+            error_message="Sorry, I encountered an error calling the AI model.",
+            concurrent_tools=True,
+        ))
+        self._last_usage = result.usage
+        if result.stop_reason == "max_iterations":
             logger.warning("Max iterations ({}) reached", self.max_iterations)
-            final_content = (
-                f"I reached the maximum number of tool call iterations ({self.max_iterations}) "
-                "without completing the task. You can try breaking the task into smaller steps."
-            )
-
-        return final_content, tools_used, messages
+        elif result.stop_reason == "error":
+            logger.error("LLM returned error: {}", (result.final_content or "")[:200])
+        return result.final_content, result.tools_used, result.messages
 
     async def run(self) -> None:
         """Run the agent loop, dispatching messages as tasks to stay responsive to /stop."""

nanobot/agent/runner.py

@@ -0,0 +1,221 @@
+"""Shared execution loop for tool-using agents."""
+
+from __future__ import annotations
+
+import asyncio
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass, field
+from typing import Any
+
+from nanobot.agent.tools.registry import ToolRegistry
+from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
+from nanobot.utils.helpers import build_assistant_message
+
+_DEFAULT_MAX_ITERATIONS_MESSAGE = (
+    "I reached the maximum number of tool call iterations ({max_iterations}) "
+    "without completing the task. You can try breaking the task into smaller steps."
+)
+_DEFAULT_ERROR_MESSAGE = "Sorry, I encountered an error calling the AI model."
+
+
+@dataclass(slots=True)
+class AgentRunSpec:
+    """Configuration for a single agent execution."""
+
+    initial_messages: list[dict[str, Any]]
+    tools: ToolRegistry
+    model: str
+    max_iterations: int
+    temperature: float | None = None
+    max_tokens: int | None = None
+    reasoning_effort: str | None = None
+    on_stream: Callable[[str], Awaitable[None]] | None = None
+    on_stream_end: Callable[..., Awaitable[None]] | None = None
+    on_tool_calls: Callable[[LLMResponse], Awaitable[None] | None] | None = None
+    before_execute_tools: Callable[[list[ToolCallRequest]], Awaitable[None] | None] | None = None
+    finalize_content: Callable[[str | None], str | None] | None = None
+    error_message: str | None = _DEFAULT_ERROR_MESSAGE
+    max_iterations_message: str | None = None
+    concurrent_tools: bool = False
+    fail_on_tool_error: bool = False
+
+
+@dataclass(slots=True)
+class AgentRunResult:
+    """Outcome of a shared agent execution."""
+
+    final_content: str | None
+    messages: list[dict[str, Any]]
+    tools_used: list[str] = field(default_factory=list)
+    usage: dict[str, int] = field(default_factory=dict)
+    stop_reason: str = "completed"
+    error: str | None = None
+    tool_events: list[dict[str, str]] = field(default_factory=list)
+
+
+class AgentRunner:
+    """Run a tool-capable LLM loop without product-layer concerns."""
+
+    def __init__(self, provider: LLMProvider):
+        self.provider = provider
+
+    async def run(self, spec: AgentRunSpec) -> AgentRunResult:
+        messages = list(spec.initial_messages)
+        final_content: str | None = None
+        tools_used: list[str] = []
+        usage = {"prompt_tokens": 0, "completion_tokens": 0}
+        error: str | None = None
+        stop_reason = "completed"
+        tool_events: list[dict[str, str]] = []
+
+        for _ in range(spec.max_iterations):
+            kwargs: dict[str, Any] = {
+                "messages": messages,
+                "tools": spec.tools.get_definitions(),
+                "model": spec.model,
+            }
+            if spec.temperature is not None:
+                kwargs["temperature"] = spec.temperature
+            if spec.max_tokens is not None:
+                kwargs["max_tokens"] = spec.max_tokens
+            if spec.reasoning_effort is not None:
+                kwargs["reasoning_effort"] = spec.reasoning_effort
+
+            if spec.on_stream:
+                response = await self.provider.chat_stream_with_retry(
+                    **kwargs,
+                    on_content_delta=spec.on_stream,
+                )
+            else:
+                response = await self.provider.chat_with_retry(**kwargs)
+
+            raw_usage = response.usage or {}
+            usage = {
+                "prompt_tokens": int(raw_usage.get("prompt_tokens", 0) or 0),
+                "completion_tokens": int(raw_usage.get("completion_tokens", 0) or 0),
+            }
+
+            if response.has_tool_calls:
+                if spec.on_stream_end:
+                    await spec.on_stream_end(resuming=True)
+                if spec.on_tool_calls:
+                    maybe = spec.on_tool_calls(response)
+                    if maybe is not None:
+                        await maybe
+
+                messages.append(build_assistant_message(
+                    response.content or "",
+                    tool_calls=[tc.to_openai_tool_call() for tc in response.tool_calls],
+                    reasoning_content=response.reasoning_content,
+                    thinking_blocks=response.thinking_blocks,
+                ))
+                tools_used.extend(tc.name for tc in response.tool_calls)
+
+                if spec.before_execute_tools:
+                    maybe = spec.before_execute_tools(response.tool_calls)
+                    if maybe is not None:
+                        await maybe
+
+                results, new_events, fatal_error = await self._execute_tools(spec, response.tool_calls)
+                tool_events.extend(new_events)
+                if fatal_error is not None:
+                    error = f"Error: {type(fatal_error).__name__}: {fatal_error}"
+                    stop_reason = "tool_error"
+                    break
+                for tool_call, result in zip(response.tool_calls, results):
+                    messages.append({
+                        "role": "tool",
+                        "tool_call_id": tool_call.id,
+                        "name": tool_call.name,
+                        "content": result,
+                    })
+                continue
+
+            if spec.on_stream_end:
+                await spec.on_stream_end(resuming=False)
+
+            clean = spec.finalize_content(response.content) if spec.finalize_content else response.content
+            if response.finish_reason == "error":
+                final_content = clean or spec.error_message or _DEFAULT_ERROR_MESSAGE
+                stop_reason = "error"
+                error = final_content
+                break
+
+            messages.append(build_assistant_message(
+                clean,
+                reasoning_content=response.reasoning_content,
+                thinking_blocks=response.thinking_blocks,
+            ))
+            final_content = clean
+            break
+        else:
+            stop_reason = "max_iterations"
+            template = spec.max_iterations_message or _DEFAULT_MAX_ITERATIONS_MESSAGE
+            final_content = template.format(max_iterations=spec.max_iterations)
+
+        return AgentRunResult(
+            final_content=final_content,
+            messages=messages,
+            tools_used=tools_used,
+            usage=usage,
+            stop_reason=stop_reason,
+            error=error,
+            tool_events=tool_events,
+        )
+
+    async def _execute_tools(
+        self,
+        spec: AgentRunSpec,
+        tool_calls: list[ToolCallRequest],
+    ) -> tuple[list[Any], list[dict[str, str]], BaseException | None]:
+        if spec.concurrent_tools:
+            tool_results = await asyncio.gather(*(
+                self._run_tool(spec, tool_call)
+                for tool_call in tool_calls
+            ))
+        else:
+            tool_results = [
+                await self._run_tool(spec, tool_call)
+                for tool_call in tool_calls
+            ]
+
+        results: list[Any] = []
+        events: list[dict[str, str]] = []
+        fatal_error: BaseException | None = None
+        for result, event, error in tool_results:
+            results.append(result)
+            events.append(event)
+            if error is not None and fatal_error is None:
+                fatal_error = error
+        return results, events, fatal_error
+
+    async def _run_tool(
+        self,
+        spec: AgentRunSpec,
+        tool_call: ToolCallRequest,
+    ) -> tuple[Any, dict[str, str], BaseException | None]:
+        try:
+            result = await spec.tools.execute(tool_call.name, tool_call.arguments)
+        except asyncio.CancelledError:
+            raise
+        except BaseException as exc:
+            event = {
+                "name": tool_call.name,
+                "status": "error",
+                "detail": str(exc),
+            }
+            if spec.fail_on_tool_error:
+                return f"Error: {type(exc).__name__}: {exc}", event, exc
+            return f"Error: {type(exc).__name__}: {exc}", event, None
+
+        detail = "" if result is None else str(result)
+        detail = detail.replace("\n", " ").strip()
+        if not detail:
+            detail = "(empty)"
+        elif len(detail) > 120:
+            detail = detail[:120] + "..."
+        return result, {
+            "name": tool_call.name,
+            "status": "error" if isinstance(result, str) and result.startswith("Error") else "ok",
+            "detail": detail,
+        }, None

nanobot/agent/subagent.py

@@ -8,6 +8,7 @@ from typing import Any
 
 from loguru import logger
 
+from nanobot.agent.runner import AgentRunSpec, AgentRunner
 from nanobot.agent.skills import BUILTIN_SKILLS_DIR
 from nanobot.agent.tools.filesystem import EditFileTool, ListDirTool, ReadFileTool, WriteFileTool
 from nanobot.agent.tools.registry import ToolRegistry
@@ -17,7 +18,6 @@ from nanobot.bus.events import InboundMessage
 from nanobot.bus.queue import MessageBus
 from nanobot.config.schema import ExecToolConfig
 from nanobot.providers.base import LLMProvider
-from nanobot.utils.helpers import build_assistant_message
 
 
 class SubagentManager:
@@ -44,6 +44,7 @@ class SubagentManager:
         self.web_proxy = web_proxy
         self.exec_config = exec_config or ExecToolConfig()
         self.restrict_to_workspace = restrict_to_workspace
+        self.runner = AgentRunner(provider)
         self._running_tasks: dict[str, asyncio.Task[None]] = {}
         self._session_tasks: dict[str, set[str]] = {}  # session_key -> {task_id, ...}
 
@@ -112,50 +113,42 @@ class SubagentManager:
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": task},
             ]
+            async def _log_tool_calls(tool_calls) -> None:
+                for tool_call in tool_calls:
+                    args_str = json.dumps(tool_call.arguments, ensure_ascii=False)
+                    logger.debug("Subagent [{}] executing: {} with arguments: {}", task_id, tool_call.name, args_str)
 
-            # Run agent loop (limited iterations)
-            max_iterations = 15
-            iteration = 0
-            final_result: str | None = None
-
-            while iteration < max_iterations:
-                iteration += 1
-
-                response = await self.provider.chat_with_retry(
-                    messages=messages,
-                    tools=tools.get_definitions(),
-                    model=self.model,
+            result = await self.runner.run(AgentRunSpec(
+                initial_messages=messages,
+                tools=tools,
+                model=self.model,
+                max_iterations=15,
+                before_execute_tools=_log_tool_calls,
+                max_iterations_message="Task completed but no final response was generated.",
+                error_message=None,
+                fail_on_tool_error=True,
+            ))
+            if result.stop_reason == "tool_error":
+                await self._announce_result(
+                    task_id,
+                    label,
+                    task,
+                    self._format_partial_progress(result),
+                    origin,
+                    "error",
                 )
-
-                if response.has_tool_calls:
-                    tool_call_dicts = [
-                        tc.to_openai_tool_call()
-                        for tc in response.tool_calls
-                    ]
-                    messages.append(build_assistant_message(
-                        response.content or "",
-                        tool_calls=tool_call_dicts,
-                        reasoning_content=response.reasoning_content,
-                        thinking_blocks=response.thinking_blocks,
-                    ))
-
-                    # Execute tools
-                    for tool_call in response.tool_calls:
-                        args_str = json.dumps(tool_call.arguments, ensure_ascii=False)
-                        logger.debug("Subagent [{}] executing: {} with arguments: {}", task_id, tool_call.name, args_str)
-                        result = await tools.execute(tool_call.name, tool_call.arguments)
-                        messages.append({
-                            "role": "tool",
-                            "tool_call_id": tool_call.id,
-                            "name": tool_call.name,
-                            "content": result,
-                        })
-                else:
-                    final_result = response.content
-                    break
-
-            if final_result is None:
-                final_result = "Task completed but no final response was generated."
+                return
+            if result.stop_reason == "error":
+                await self._announce_result(
+                    task_id,
+                    label,
+                    task,
+                    result.error or "Error: subagent execution failed.",
+                    origin,
+                    "error",
+                )
+                return
+            final_result = result.final_content or "Task completed but no final response was generated."
 
             logger.info("Subagent [{}] completed successfully", task_id)
             await self._announce_result(task_id, label, task, final_result, origin, "ok")
@@ -196,6 +189,27 @@ Summarize this naturally for the user. Keep it brief (1-2 sentences). Do not men
 
         await self.bus.publish_inbound(msg)
         logger.debug("Subagent [{}] announced result to {}:{}", task_id, origin['channel'], origin['chat_id'])
+
+    @staticmethod
+    def _format_partial_progress(result) -> str:
+        completed = [e for e in result.tool_events if e["status"] == "ok"]
+        failure = next((e for e in reversed(result.tool_events) if e["status"] == "error"), None)
+        lines: list[str] = []
+        if completed:
+            lines.append("Completed steps:")
+            for event in completed[-3:]:
+                lines.append(f"- {event['name']}: {event['detail']}")
+        if failure:
+            if lines:
+                lines.append("")
+            lines.append("Failure:")
+            lines.append(f"- {failure['name']}: {failure['detail']}")
+        if result.error and not failure:
+            if lines:
+                lines.append("")
+            lines.append("Failure:")
+            lines.append(f"- {result.error}")
+        return "\n".join(lines) or (result.error or "Error: subagent execution failed.")
     
     def _build_subagent_prompt(self) -> str:
         """Build a focused system prompt for the subagent."""

tests/agent/test_runner.py

@@ -0,0 +1,186 @@
+"""Tests for the shared agent runner and its integration contracts."""
+
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from nanobot.providers.base import LLMResponse, ToolCallRequest
+
+
+def _make_loop(tmp_path):
+    from nanobot.agent.loop import AgentLoop
+    from nanobot.bus.queue import MessageBus
+
+    bus = MessageBus()
+    provider = MagicMock()
+    provider.get_default_model.return_value = "test-model"
+
+    with patch("nanobot.agent.loop.ContextBuilder"), \
+         patch("nanobot.agent.loop.SessionManager"), \
+         patch("nanobot.agent.loop.SubagentManager") as MockSubMgr:
+        MockSubMgr.return_value.cancel_by_session = AsyncMock(return_value=0)
+        loop = AgentLoop(bus=bus, provider=provider, workspace=tmp_path)
+    return loop
+
+
+@pytest.mark.asyncio
+async def test_runner_preserves_reasoning_fields_and_tool_results():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    captured_second_call: list[dict] = []
+    call_count = {"n": 0}
+
+    async def chat_with_retry(*, messages, **kwargs):
+        call_count["n"] += 1
+        if call_count["n"] == 1:
+            return LLMResponse(
+                content="thinking",
+                tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
+                reasoning_content="hidden reasoning",
+                thinking_blocks=[{"type": "thinking", "thinking": "step"}],
+                usage={"prompt_tokens": 5, "completion_tokens": 3},
+            )
+        captured_second_call[:] = messages
+        return LLMResponse(content="done", tool_calls=[], usage={})
+
+    provider.chat_with_retry = chat_with_retry
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(return_value="tool result")
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[
+            {"role": "system", "content": "system"},
+            {"role": "user", "content": "do task"},
+        ],
+        tools=tools,
+        model="test-model",
+        max_iterations=3,
+    ))
+
+    assert result.final_content == "done"
+    assert result.tools_used == ["list_dir"]
+    assert result.tool_events == [
+        {"name": "list_dir", "status": "ok", "detail": "tool result"}
+    ]
+
+    assistant_messages = [
+        msg for msg in captured_second_call
+        if msg.get("role") == "assistant" and msg.get("tool_calls")
+    ]
+    assert len(assistant_messages) == 1
+    assert assistant_messages[0]["reasoning_content"] == "hidden reasoning"
+    assert assistant_messages[0]["thinking_blocks"] == [{"type": "thinking", "thinking": "step"}]
+    assert any(
+        msg.get("role") == "tool" and msg.get("content") == "tool result"
+        for msg in captured_second_call
+    )
+
+
+@pytest.mark.asyncio
+async def test_runner_returns_max_iterations_fallback():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
+        content="still working",
+        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
+    ))
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(return_value="tool result")
+
+    runner = AgentRunner(provider)
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[],
+        tools=tools,
+        model="test-model",
+        max_iterations=2,
+    ))
+
+    assert result.stop_reason == "max_iterations"
+    assert result.final_content == (
+        "I reached the maximum number of tool call iterations (2) "
+        "without completing the task. You can try breaking the task into smaller steps."
+    )
+
+
+@pytest.mark.asyncio
+async def test_runner_returns_structured_tool_error():
+    from nanobot.agent.runner import AgentRunSpec, AgentRunner
+
+    provider = MagicMock()
+    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
+        content="working",
+        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+    ))
+    tools = MagicMock()
+    tools.get_definitions.return_value = []
+    tools.execute = AsyncMock(side_effect=RuntimeError("boom"))
+
+    runner = AgentRunner(provider)
+
+    result = await runner.run(AgentRunSpec(
+        initial_messages=[],
+        tools=tools,
+        model="test-model",
+        max_iterations=2,
+        fail_on_tool_error=True,
+    ))
+
+    assert result.stop_reason == "tool_error"
+    assert result.error == "Error: RuntimeError: boom"
+    assert result.tool_events == [
+        {"name": "list_dir", "status": "error", "detail": "boom"}
+    ]
+
+
+@pytest.mark.asyncio
+async def test_loop_max_iterations_message_stays_stable(tmp_path):
+    loop = _make_loop(tmp_path)
+    loop.provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
+        content="working",
+        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+    ))
+    loop.tools.get_definitions = MagicMock(return_value=[])
+    loop.tools.execute = AsyncMock(return_value="ok")
+    loop.max_iterations = 2
+
+    final_content, _, _ = await loop._run_agent_loop([])
+
+    assert final_content == (
+        "I reached the maximum number of tool call iterations (2) "
+        "without completing the task. You can try breaking the task into smaller steps."
+    )
+
+
+@pytest.mark.asyncio
+async def test_subagent_max_iterations_announces_existing_fallback(tmp_path, monkeypatch):
+    from nanobot.agent.subagent import SubagentManager
+    from nanobot.bus.queue import MessageBus
+
+    bus = MessageBus()
+    provider = MagicMock()
+    provider.get_default_model.return_value = "test-model"
+    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
+        content="working",
+        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+    ))
+    mgr = SubagentManager(provider=provider, workspace=tmp_path, bus=bus)
+    mgr._announce_result = AsyncMock()
+
+    async def fake_execute(self, name, arguments):
+        return "tool result"
+
+    monkeypatch.setattr("nanobot.agent.tools.registry.ToolRegistry.execute", fake_execute)
+
+    await mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})
+
+    mgr._announce_result.assert_awaited_once()
+    args = mgr._announce_result.await_args.args
+    assert args[3] == "Task completed but no final response was generated."
+    assert args[5] == "ok"

tests/agent/test_task_cancel.py

@@ -221,3 +221,83 @@ class TestSubagentCancellation:
         assert len(assistant_messages) == 1
         assert assistant_messages[0]["reasoning_content"] == "hidden reasoning"
         assert assistant_messages[0]["thinking_blocks"] == [{"type": "thinking", "thinking": "step"}]
+
+    @pytest.mark.asyncio
+    async def test_subagent_announces_error_when_tool_execution_fails(self, monkeypatch, tmp_path):
+        from nanobot.agent.subagent import SubagentManager
+        from nanobot.bus.queue import MessageBus
+        from nanobot.providers.base import LLMResponse, ToolCallRequest
+
+        bus = MessageBus()
+        provider = MagicMock()
+        provider.get_default_model.return_value = "test-model"
+        provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
+            content="thinking",
+            tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+        ))
+        mgr = SubagentManager(provider=provider, workspace=tmp_path, bus=bus)
+        mgr._announce_result = AsyncMock()
+
+        calls = {"n": 0}
+
+        async def fake_execute(self, name, arguments):
+            calls["n"] += 1
+            if calls["n"] == 1:
+                return "first result"
+            raise RuntimeError("boom")
+
+        monkeypatch.setattr("nanobot.agent.tools.registry.ToolRegistry.execute", fake_execute)
+
+        await mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})
+
+        mgr._announce_result.assert_awaited_once()
+        args = mgr._announce_result.await_args.args
+        assert "Completed steps:" in args[3]
+        assert "- list_dir: first result" in args[3]
+        assert "Failure:" in args[3]
+        assert "- list_dir: boom" in args[3]
+        assert args[5] == "error"
+
+    @pytest.mark.asyncio
+    async def test_cancel_by_session_cancels_running_subagent_tool(self, monkeypatch, tmp_path):
+        from nanobot.agent.subagent import SubagentManager
+        from nanobot.bus.queue import MessageBus
+        from nanobot.providers.base import LLMResponse, ToolCallRequest
+
+        bus = MessageBus()
+        provider = MagicMock()
+        provider.get_default_model.return_value = "test-model"
+        provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
+            content="thinking",
+            tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
+        ))
+        mgr = SubagentManager(provider=provider, workspace=tmp_path, bus=bus)
+        mgr._announce_result = AsyncMock()
+
+        started = asyncio.Event()
+        cancelled = asyncio.Event()
+
+        async def fake_execute(self, name, arguments):
+            started.set()
+            try:
+                await asyncio.sleep(60)
+            except asyncio.CancelledError:
+                cancelled.set()
+                raise
+
+        monkeypatch.setattr("nanobot.agent.tools.registry.ToolRegistry.execute", fake_execute)
+
+        task = asyncio.create_task(
+            mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})
+        )
+        mgr._running_tasks["sub-1"] = task
+        mgr._session_tasks["test:c1"] = {"sub-1"}
+
+        await started.wait()
+
+        count = await mgr.cancel_by_session("test:c1")
+
+        assert count == 1
+        assert cancelled.is_set()
+        assert task.cancelled()
+        mgr._announce_result.assert_not_awaited()