Uvi estimates its context layer costs $0.10 to $0.16 an answer against up to $1.41 for in-house agents

Monday morning, a team switches on a new agent. According to Uvi, that agent knows nothing at all about the company that deployed it ^[13]. It has to rebuild the business context from scratch, and the company pays the model provider for every token spent doing so ^[14].

The pitch is an eightfold cut in agent running costs. What Uvi measured is cost per answer. It ran its approach and company-built agents on the same model and said the in-house agents cost about eight times as much ^[15]. Uvi estimated the cost per answer at $0.75 to $1.41 for those agents and $0.10 to $0.16 for itself ^[16].

Take the ranges at face value. The low ends differ by a factor of 7.5 ^[9] and the high ends by about 8.8 ^[10], so "about eight" sits inside both. Pair the extremes the other way and the gap runs from 4.7 to 14.1 ^[11]. In dollars the saving is 59 cents to $1.31 an answer ^[12], so the annual total depends on how many answers an agent produces.

Both figures come from Uvi's own testing ^[15][17]. The reports do not say which model was used, how many tasks were run, or what an agent's yearly answer volume looks like.

Gilad Salinger, Uvi's chief executive, locates the waste in repetition. He said that "even when an agent has already completed a task, it would fail the same task the next time, because it never retained how the work actually gets done" ^[6]. He calls the fix a "context reasoning layer" that each company owns ^[8]. In plain terms it is a record of what was decided and why, assembled from tools such as Slack and Jira ^[2]. In our view the saving should be largest where many agents repeat similar work and smallest where each agent does something new.

The investor view is supportive. Gwen Cheni, who runs the Venture IQ Fund at CT Innovations, said: "Every enterprise we talk to has already spent a year and a real budget trying to build context-at-scale, and most are still very far away" ^[5]. CT Innovations led the round ^[7].

We would pilot a shared context layer only where two measurements point the same way: how often agents repeat the same task across deployments, and what share of each answer's token bill goes to gathering context. Where both are high, a shared layer has something to remove, and the pilot is worth the integration work of connecting Slack, Jira and Confluence and trusting a new system with the record of who decided what. Where repetition is high and context is a small share of the bill, the waste is elsewhere. Where context is a large share but every task is new, there is little to reuse. Where both are low, there is nothing to cut.

We would run the pilot as Uvi's own comparison, on the task your agents repeat most, on your own model, with the vendor's fee counted in its per-answer cost. If the measured gap on that task is close to 1x, the eightfold claim does not describe your deployment.