We're deploying an agent assistant for internal use in a FedRAMP Moderate environment. The use case requires the agent to have a large context window for processing lengthy documents within a single session. However, our compliance team has flagged persistent chat history as a data retention risk we can't accept.

The core problem: we need the *context window* for processing power per session, but we cannot allow the system to *store* or *log* the chat history beyond the immediate session lifecycle. Think single, long, ephemeral analysis session.

Has anyone architected around this? I'm thinking about a two-pronged approach:

1. **Session Isolation:** Ensuring every new chat session spins up a fresh, isolated runtime with no access to previous sessions' data.
2. **Memory Control:** Configuring the agent's underlying system to explicitly not write conversation history to any durable storage (logs, databases). The context lives only in the session's working memory.

For our PoC, we're looking at a wrapper that manages the agent's memory object. We're forcing a hard reset between user sessions.

```python
# Simplified concept - instantiate a fresh agent for each session
def get_ephemeral_agent():
# Load fresh model, clean context
agent = AssistantAgent(llm_config={"context_window": 128k})
# Ensure no memory persistence hooks are active
agent.memory.persistence = False
return agent

# Session ends, agent object is dereferenced and garbage collected.
```

Is this naive? How are you handling the boundary between necessary runtime context and prohibited data retention? Specifically interested in any logging pitfalls you've encountered with common agent frameworks.