Under the hood, Muse Code pairs a simple agent loop with persistent, async backg
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Introducing Muse Code (beta), a terminal coding agent built for long-horizon software engineering, powered by our new Muse Spark 1.2 model. Muse Code plans, implements, and validates complex, multi-file changes across large repositories with persistent sub-agents that solve difficult problems faster, more accurately, and with less intervention. 🧵👇

Under the hood, Muse Code pairs a simple agent loop with persistent, async background agents that stay active throughout each session to execute next steps, avoid redundant info gathering, and reduce steering on complex tasks. An append-only local event log tracks every model call, tool run, approval, and edit, making the runtime replay-exact and restart-safe for long-running workflows.
Context
Meta says Muse Code can resume a long task exactly where it stopped after a crash, because it writes every step of its work to a log on the user's machine. Its runtime post describes an append-only event log, a record that is only ever added to, never rewritten, holding every model call, tool run, approval and file edit in order. Meta calls the runtime replay-exact and restart-safe, meaning that the session can be rebuilt from that record and continue after a crash without losing the work done so far. The same post describes background agents that stay active through each session to avoid gathering the same information twice.
Meta's release article presents the log as the single source of truth for a session and the reason the can take on long tasks without being derailed by failures. Neither the post nor the article reports a recovery test, so restart safety is Meta's description of the design rather than a measured result.
Meta's current documentation, read in November, shows a limit on what a session's record can rebuild. Its rewind feature, a separate feature from crash recovery that works only with session logging switched on, rebuilds the conversation from an earlier message. It does not restore, revert or delete files in the workspace, so code changes still have to be undone with version control. The changelog for version 1.4.13 also lists fixes for cases where sessions had stopped saving their resume checkpoints, so resuming sessions has needed repairs since launch.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- agentic loop — The cycle an agent runs in: decide, call a tool, read the result, decide again — repeating until the goal is met or a stop condition fires.
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