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 hybrid to fully deterministic workflows, together with an evidence-based promotion mechanism that converts repeatedly validated agent behaviors into cheaper and more reproducible deterministic workflows, while automatically demoting workflows that regress. Evaluated on a production cloud networking AIOps system processing tens of thousands of incidents per month, the approach increased deterministic execution from 0% to 45% over eight months, reduced per-incident agent costs by more than 70% despite doubling incident volume, and improved safety through greater reproducibility and auditability. The paper also presents the execution taxonomy, promotion and demotion criteria, trace extraction methodology, economic model, safety considerations, and discusses limitations and threats to validity.
OrchBench is established as an efficient and interpretable benchmark for comparing and diagnosing multi-agent orchestration plans, finding that preserving task-critical information is more important than simply increasing the number of agents, and the benefits of parallelism diminish as coordination failures accumulate...
This work presents MetaRoute-Bench, an open, inspectable framework for comparing meta-decision policies under a shared execution model, and releases task generation, policies, traces, tests, and analysis artifacts to support live-system validation.
Natan Vidra, Alina Kapanova, Arun Kanhai et al.· 0 citations
The results suggest that scientific-agent capability depends not only on the base model but on whether an architecture can be formed to match the task's cognitive structure, as well as establishing results on regret, planning invalidation, amortization, subtree interfaces, serializability, and verification.
Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures. However, constructing high-quality agentic workflows remains largely manual and requires substantial domain e...
TRIAGE, a three-level routing framework that reduces token consumption by reusing historical execution trajectories, and proposes an automatic Skill extraction mechanism that distills high-frequency trajectory patterns into deterministic Skills, creating a positive feedback loop of the more you use it, the more efficie...
The more typical feature of agentic AI systems is dynamic, multistep workflows where autonomous components plan, reason, and communicate with external tools and data sources in a series of iterations. Such flexibility increases capability but also brings nondeterminism which is inherent and where the same inputs can re...
Ankur Gupta, Karan Gupta, Divyakumar Deepak Savla et al.· International Conference on...· 0 citations
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