feat: concurrent tool execution with ThreadPoolExecutor

When the model returns multiple tool calls in a single response, they are
now executed concurrently using a thread pool instead of sequentially.
This significantly reduces wall-clock time when multiple independent tools
are batched (e.g. parallel web_search, read_file, terminal calls).

Architecture:
- _execute_tool_calls() dispatches to sequential or concurrent path
- Single tool calls and batches containing 'clarify' use sequential path
- Multiple non-interactive tools use ThreadPoolExecutor (max 8 workers)
- Results are collected and appended to messages in original order
- _invoke_tool() extracted as shared tool invocation helper

Safety:
- Pre-flight interrupt check skips all tools if interrupted
- Per-tool exception handling: one failure doesn't crash the batch
- Result truncation (100k char limit) applied per tool
- Budget pressure injection after all tools complete
- Checkpoints taken before file-mutating tools
- CLI spinner shows batch progress, then per-tool completion messages

Tests: 10 new tests covering dispatch logic, ordering, error handling,
interrupt behavior, truncation, and _invoke_tool routing.

tests/tools/test_interrupt.py

@@ -91,8 +91,11 @@ class TestPreToolCheck:
         agent._persist_session = MagicMock()
 
         # Import and call the method
+        import types
         from run_agent import AIAgent
-        # Bind the real method to our mock
+        # Bind the real methods to our mock so dispatch works correctly
+        agent._execute_tool_calls_sequential = types.MethodType(AIAgent._execute_tool_calls_sequential, agent)
+        agent._execute_tool_calls_concurrent = types.MethodType(AIAgent._execute_tool_calls_concurrent, agent)
         AIAgent._execute_tool_calls(agent, assistant_msg, messages, "default")
 
         # All 3 should be skipped