Developers working with long-running AI agents often hit a familiar wall where the model begins to lose the plot. After a few dozen turns of refactoring, the agent starts hallucinating existing functions or stubbornly clinging to a flawed architectural decision made ten prompts ago. This phenomenon, known as context drift, turns a productive session into a battle against the model's own memory, forcing the human developer to manually reset the chat or prune the history to get the AI back on track.
The Architecture of Iterative Refinement
retry-now enters the ecosystem as an autonomous loop agent specifically engineered to eliminate this cognitive decay. Built as a CLI tool powered by the Bun JavaScript runtime, the agent operates on a strict binary cycle of ANALYZE and IMPROVE. During the ANALYZE phase, the agent scans the codebase to identify specific candidates for optimization, requiring concrete evidence in the form of file paths and line numbers before proposing a change. Once a candidate is identified, the IMPROVE phase takes over, applying the change and immediately subjecting it to a gauntlet of verification tools, including tests, linters, and performance benchmarks.
If a proposed change fails any of these verification steps, retry-now triggers an immediate rollback to the last known stable state. This loop continues autonomously, with the agent iterating through the codebase until no further improvements can be identified. To ensure maximum compatibility with existing developer workflows, retry-now is designed to work alongside other prominent AI coding tools, including opencode, Codex CLI, and Claude Code.
Breaking the Cycle of AI Tunnel Vision
What separates retry-now from standard autonomous agents is its radical approach to memory. While the industry trend has been to expand context windows to millions of tokens, retry-now intentionally destroys its context. By generating a completely new context session for every single iteration, the agent avoids the trap of previous judgments. It does not remember the failed attempts of the last loop; it only sees the current state of the code and the objective benchmarks.
This design transforms the agent from a conversational partner into a deterministic optimization engine. By stripping away the baggage of the session history, the tool prevents the AI from falling into a loop of repeating the same mistake or attempting to justify a poor decision simply because it was mentioned earlier in the thread. The result is a focused pursuit of extreme performance optimization and the reduction of memory footprints, treating code improvement as a mathematical convergence rather than a creative dialogue.
This shift toward stateless, iterative loops suggests a future where AI agents act less like assistants and more like autonomous compilers for performance tuning.
