The missing layer between your AI agents and you

The three patterns developers settle for Pattern 1: Terminal babysitting python my_agent.py >> agent.log 2>&1 & tail -f agent.log You run the agent. You watch the logs. When it crashes, you restart it. This works fine for one-shot batch jobs: scraping, processing, tasks where the agent runs to completion and you check the output file. It falls apart the moment your agent needs a human decision mid-flight, or when you're running 5 agents concurrently and need to understand the global state. Pattern 2: Polling (REST API) # Check status curl http://localhost:8000/status # {"status": "running", "task": "scraping page 42/100", "errors": 0} # Ask it something curl -X POST http://localhost:8000/query \ -d '{"question": "what have you found so far?"}' Better. At least there's a programmatic dialogue. But the power dynamic is one-way: the agent can only respond to requests. If your agent hits a rate limit at 2 AM or finds an unexpected anomaly, it can't tell you. You'd have to write a separate