To enhance engineering productivity, it's crucial to run coding agents for extended, uninterrupted periods. Advanced models like Claude Fable and GPT-5.6 are increasingly capable of autonomous, long-duration tasks. However, effective agent execution requires a supportive structure, including granting necessary permissions, enabling self-verification, and implementing agentic code reviews.

The primary benefit of long-running agents is minimizing human review time, which has become the current bottleneck in software development. By allowing agents to complete and verify their work autonomously, engineers can significantly reduce the time spent on verification, thereby increasing overall output. This approach ensures agents handle most of the implementation and validation before human oversight.

Key techniques for achieving long-running sessions involve granting agents comprehensive permissions and the ability to verify their own output. Running agents in a secure sandbox environment is recommended to prevent accidental data loss, especially considering past incidents with AI models deleting files. Backups and using version control systems like GitHub are essential safety measures.

Furthermore, agents should be instructed on how to test their implementations, such as comparing screenshots to designs or running test suites. Agentic code reviews, particularly using tools like Codex, can further reduce the need for human intervention, with options for integrated GitHub reviews or CLI-based comparisons between branches. This process can drastically speed up code deployment.