long-running-agent

Maintaining AI agent state across multiple context windows requires persistent session management that tracks progress and handles handoffs. Using an initializer agent to set up artifacts like progress files enables work to resume seamlessly on multi-hour coding projects.

Structuring session handoffs for multi-hour AI coding projects involves centralizing context using artifacts like progress tracking files and feature lists. A repeatable session startup sequence inspects the repository, reviews progress, and initializes the environment before selecting the next feature.

Setting up an AI agent workflow to track incremental coding progress between sessions involves running a two-agent workflow: initialize the environment with an initializer agent, then proceed with a coding agent that reads progress, tests, and commits changes between sessions.

Using a single AI agent to both initialize the environment and perform incremental coding work is not recommended. A two-agent workflow separates environment setup and progress tracking from the actual coding agent, ensuring persistent state and clean handoffs across multiple sessions.

To resume a long-running AI coding task in a new session, follow a repeatable session startup sequence: inspect the repository path, review the progress text file, read the feature list JSON, check recent git logs, run the initialization script, execute a basic test, and select the next feature.