Orchestration and Execution: How JONI Approaches the Agent Layer

The distance between a model that produces correct output and a system that completes a task has turned out to be larger than most deployments anticipated.

A Workday survey of 3,200 employees across North America, Europe and Asia found that while 85 percent reported AI saving them between one and seven hours a week, roughly 37 percent of that saved time was consumed correcting, clarifying or rewriting low-quality output. Only 14 percent said they consistently achieved net-positive outcomes, and the heaviest users lost the most, with highly engaged employees giving up an estimated 1.5 weeks a year to rework. Workday characterised the cause as structural rather than behavioural, noting that AI has largely been layered onto roles never redesigned to accommodate it.

The organisational picture is consistent. IBM's 2025 study of chief executives found roughly a quarter of AI initiatives had met their expected return, and Gartner has projected that more than 40 percent of agentic AI projects will be cancelled by 2027, citing cost, unclear business value, and what the firm terms agent washing, the relabelling of existing automation as agentic systems.

The failure modes are reasonably well understood. Multi-step reliability degrades multiplicatively, so a pipeline of seven steps each succeeding 90 percent of the time completes less than half the time. Context does not persist between sessions. And most systems described as agentic terminate at output generation, leaving the final actions, provisioning, publishing, transacting, to a human operator.

JONI is one of several platforms attempting to address the third of these directly, positioning itself as an orchestration and execution layer above the foundation models rather than as a model provider.

Architecture

Routing

The company has stated an intention to publish comparative model performance data on a recurring basis.