Getting AI agents to act is easy. Getting them to act in ways you can audit, roll back, or explain to a regulator is the hard part.
A paper out of the arXiv cs.AI preprint server proposes a classification framework for what the authors call "agentic orchestration" — the design choices that determine how much autonomy an LLM-based agent gets versus how much the surrounding process infrastructure keeps it on a leash. The researchers identify key properties along which any implementation can be evaluated: task specificity, traceability, tractability, autonomy, reactivity, and correctness assurance. They also define quantitative metrics and run them against several real implementations built around a predictive light-sensing scenario, giving the framework a concrete test rather than leaving it at pure theory.
This matters because the enterprise AI space is currently selling autonomy as an unambiguous virtue. The actual engineering challenge is the opposite — figuring out when to hand control back to a deterministic process layer so that something auditable happens. A framework that lets teams compare options on tractability versus autonomy rather than vibes is the kind of tooling that separates a production deployment from a demo.
The timing is pointed. As vendors race to brand everything an "agentic" product, most buyers lack vocabulary to ask what that actually means for oversight. This paper won't ship in anyone's product box, but the properties it names — traceability, correctness assurance — are exactly the questions procurement teams and regulators will eventually start requiring answers to.