There are few things more painful in autonomous software engineering than watching your Manus AI agent enter a "Functionality Loop." You know the pattern: the agent writes a script, executes it, encounters a cryptic stderr output, attempts a minor syntax tweak, and runs it again. Ten minutes later, you have burned through $20 in API credits, the error log is identical, and the agent is confidently asserting that "Attempt #15 will definitely resolve the dependency conflict." This isn't just a hallucination issue; it is a structural flaw in how agents perceive execution environments. When Manus—or any sophisticated coding agent—deploys shell scripts or Python automation, it often lacks the temporal context of its own failures. Here is why these execution loops happen and a rigorous Python-based harness to force the agent out of them. The Anatomy of a Functionality Loop To fix the loop, we must understand the "State Persistency Gap." When Manus ex...
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