A recent Gartner study found that essentially all AI agent platforms are designed to narrowly focus on tasks without any awareness of strategy context. The Gartner study is explained in a video, https://www.gartner.com/en/webinar/844664/1853733-saaspocalypse-234b-of-enterprise-apps-spending-will-be-exposed-to-agentic-arbitrage
They explain that AI working on isolated tasks is far less effective than AI that is aware of strategic goals. They refer to that task-centric approach as the “amnesia zone” because the agents do not benefit from what is being learned across the organization.
According to Gartner, “We mapped 1000 agentic pilot deployments … 90% – 900 – of these deployments are … trapped in the amnesia zone. They have no direct or persistent context.”
Just as people benefit from knowing the full context of their work and how it fits into a larger picture, AI agents can benefit from that type of visibility as well. This is because performing a task involves countless small decisions, and if one is unaware of the higher goals and strategies, those small decisions can easily drift against the higher goals and strategies, even if the task is done according to spec. In the aggregate of hundreds of tasks, this can create a very large impedance for the organization’s intended direction.
ThroughlineTM is currently the only enterprise-level platform that provides end-to-end line-of-sight from executive strategy down through operational team work and exposes that to AI agents (as well as human workers) so that the agents can act intelligently and maintain alignment, instead of operating in the amnesia zone. And it can do this at scale: Throughline is designed for the complex situations that large organizations have.
Not only that, but the agents can spot misalignment and alert appropriate people. The agents can even identify ways to optimize work and speed things up. People can also assign agents to specific work and give them goals. You can substitute your own agents for certain tasks and select your preferred AI model provider(s). And you can specify which models are to be used for specific kinds of task.
Throughline also solves the agent-run-amok problem by providing a robust governance layer which ensures that agents don’t do things that you don’t want them to do. A recent incident in which an Alibaba AI agent covertly created cryptocurrency accounts and started its own trading operation is a glaring example of how desperately AI agents need robust governance at the platform level. Throughline’s governance layer is also plug-replaceable for those who want to substitute their own policy or governance system.
With Throughline, you can rest at ease knowing that your AI agents adhere to centrally defined policies and criteria. Those are also tailorable for different parts of the organization because different divisions often have different risks and risk tolerance.
Throughline is not yet on a Gartner chart because it is currently in a beta phase with a handful of customers. However, it will soon be released for general use, so now is the time to find out how it might benefit your organization by enabling your AI agents to operate with awareness of (1) what the objectives and strategies are, (2) what others are doing in other teams and other parts of the organization, and (3) how gaps can be filled and plans adjusted to optimize your execution.
