What is the optimal strategy for managing long-term memory persistence across agent conversation threads?

To prevent AI agents from entering infinite tool-calling loops in production:

1. Set Hard Step Caps & Timeouts: Always enforce a maximum step limit (e.g. `max_steps = 5`) in your execution loop.
2. Detect Duplicate Tool Calls: Hash previous tool names and arguments in memory. If an agent calls the exact same tool twice with identical args, break execution immediately.

```python
class SafeRunner:
def init(self, max_steps: int = 5):
self.max_steps = max_steps

def run(self, agent, prompt: str):
steps, history = 0, set()
while steps < self.max_steps:
steps += 1
action = agent.step(prompt)
key = (action.tool_name, str(action.tool_args))
if key in history:
return "Loop detected. Halting execution."
history.add(key)
if action.is_done:
return action.result
return "Step limit exceeded."
```

3. Pass Exception Details Back: If JSON parsing fails, feed the exact validation error back to the model in the next prompt turn so it self-corrects.