Implement self-healing system Phase 1: Error capture and logging

- Add SelfHealingSystem with error observation infrastructure
- Capture errors with full context: type, message, stack trace, intent, inputs
- Log to MEMORY.md with deduplication (max 3 attempts per error signature)
- Integrate error capture in agent, tools, runtime, and scheduler
- Non-invasive: preserves all existing error handling behavior
- Foundation for future diagnosis and auto-fixing capabilities

Phase 1 of 4-phase rollout - observation only, no auto-fixing yet.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

agent.py

@@ -7,6 +7,7 @@ from heartbeat import Heartbeat
 from hooks import HooksSystem
 from llm_interface import LLMInterface
 from memory_system import MemorySystem
+from self_healing import SelfHealingSystem
 from tools import TOOL_DEFINITIONS, execute_tool
 
 # Maximum number of recent messages to include in LLM context
@@ -31,6 +32,7 @@ class Agent:
         self.hooks = HooksSystem()
         self.conversation_history: List[dict] = []
         self._chat_lock = threading.Lock()
+        self.healing_system = SelfHealingSystem(self.memory, self)
 
         self.memory.sync()
         self.hooks.trigger("agent", "startup", {"workspace_dir": workspace_dir})
@@ -188,6 +190,16 @@ class Agent:
             except Exception as e:
                 error_msg = f"LLM API error: {e}"
                 print(f"[Agent] {error_msg}")
+                self.healing_system.capture_error(
+                    error=e,
+                    component="agent.py:_chat_inner",
+                    intent="Calling LLM API for chat response",
+                    context={
+                        "model": self.llm.model,
+                        "message_preview": user_message[:100],
+                        "iteration": iteration,
+                    },
+                )
                 return f"Sorry, I encountered an error communicating with the AI model. Please try again."
 
             # Check stop reason
@@ -245,7 +257,7 @@ class Agent:
                 # Execute tools and build tool result message
                 tool_results = []
                 for tool_use in tool_uses:
-                    result = execute_tool(tool_use.name, tool_use.input)
+                    result = execute_tool(tool_use.name, tool_use.input, healing_system=self.healing_system)
                     # Truncate large tool outputs to prevent token explosion
                     if len(result) > 5000:
                         result = result[:5000] + "\n... (output truncated)"