Your coding agent needs a session wrapper — here's the pattern that prevents vanishing work

Your coding agent is brilliant for 20 minutes, then it forgets everything and starts over. Sound familiar?

The problem isn't the model — it's that you're treating your agent like a stateless function when it should be a persistent session. Every time you start a new conversation, your agent loses context about your codebase, your preferences, and what it was working on.

Here's the pattern that fixes it: session wrappers with persistent state.

Instead of launching your agent fresh every time, wrap it in a persistent session that maintains context across conversations. Think tmux for your AI — the session keeps running even when you disconnect.

The key insight: your coding agent should pick up exactly where it left off, not start from scratch every time.

Here's what a proper session wrapper looks like:

session_state = {
  "current_task": "Implementing user auth",
  "files_modified": ["auth.py", "models.py"],
  "next_steps": ["Add password validation", "Write tests"],
  "context_files": ["AGENTS.md", "requirements.txt"],
  "session_id": "auth-implementation-2024-01-15"
}

When your agent reconnects, it reads this state and immediately knows:

• What task it was working on
• Which files it has context for
• What the next logical steps are
• Any important decisions made in previous sessions

But here's the critical part: the session needs to be self-healing. If your agent crashes mid-task, the wrapper should detect incomplete work and resume intelligently.

I run this pattern with tmux sessions that persist across disconnections. Each coding task gets its own named session, and the agent writes progress checkpoints to a state file every few minutes. If something breaks, I can reconnect and the agent picks up from the last checkpoint.

# Session recovery logic
if incomplete_task_detected():
    agent_prompt += f"\nResuming task: {last_task}\n"
    agent_prompt += f"Files in progress: {modified_files}\n"
    agent_prompt += f"Last action: {last_checkpoint}\n"

The difference is night and day. Instead of explaining your codebase from scratch every session, your agent maintains institutional knowledge. It remembers your coding style, your project structure, and your preferences.

Most importantly, it stops losing work. No more half-implemented features that get abandoned when the context window fills up.

Warning: Don't just dump everything into session state. Be selective about what persists or you'll create context pollution.

The session wrapper pattern works because it matches how human developers actually work — we don't forget everything between coding sessions. Your agent shouldn't either.

If you want to implement this pattern without building it from scratch, there's a complete session management system that handles the tmux integration, state persistence, and recovery logic automatically.