Great topic.
We've moved past simple automation and into the era of orchestration, as you said.
But I believe there's another layer to this shift: the move from Horizontal AI Agents to Vertical AI Agents.
Horizontal agents (like general-purpose chatbots or assistants) are powerful, but they lack deep, contextual understanding of a specific team's unique workflow. They can answer general questions, but they can't manage a complex design review process from end to end.
At my company, Markhub, we're betting on the power of Vertical AI. We're building an AI teammate, MAKi, that is not a generalist. It is an expert in one thing: the chaotic workflow of turning scattered team conversations into structured, actionable work.
It's designed to understand the specific context of a project—the feedback on a PDF, the follow-up questions in a chat, and the resulting tasks on a Kanban board. It doesn't just automate tasks; it orchestrates the entire feedback and execution lifecycle.
My belief is that the most successful enterprise AI won't be a single, all knowing agent. It will be a network of specialized, vertical agents like ours, each mastering a specific, high-value business process.
RPA has worked out a lot of the plumbing for orchestration such as data-sharing, administrative control, access control, integration, reporting, and compliance. It seems a shorter trip to put agents inside RPA than to replace RPA with agents. This observation mostly applies to lots of orchestration at scale in an enteprise. How would vertical AI be monitored, controlled, and managed at scale without building the same sort of stuff that RPA has created?
The big shift will be from orchestration to choreography.
For most work processes beyong the ubiquitous 'vacation request' demo orchestration was never viable as a model due to its inherent inflexibility aligned with rose coloured, sunny day snenarios clashing with the real fluidity and complexity of the actual work floor.
Great topic. We've moved past simple automation and into the era of orchestration, as you said.
But I believe there's another layer to this shift: the move from Horizontal AI Agents to Vertical AI Agents.
Horizontal agents (like general-purpose chatbots or assistants) are powerful, but they lack deep, contextual understanding of a specific team's unique workflow. They can answer general questions, but they can't manage a complex design review process from end to end.
At my company, Markhub, we're betting on the power of Vertical AI. We're building an AI teammate, MAKi, that is not a generalist. It is an expert in one thing: the chaotic workflow of turning scattered team conversations into structured, actionable work.
It's designed to understand the specific context of a project—the feedback on a PDF, the follow-up questions in a chat, and the resulting tasks on a Kanban board. It doesn't just automate tasks; it orchestrates the entire feedback and execution lifecycle.
My belief is that the most successful enterprise AI won't be a single, all knowing agent. It will be a network of specialized, vertical agents like ours, each mastering a specific, high-value business process.
RPA has worked out a lot of the plumbing for orchestration such as data-sharing, administrative control, access control, integration, reporting, and compliance. It seems a shorter trip to put agents inside RPA than to replace RPA with agents. This observation mostly applies to lots of orchestration at scale in an enteprise. How would vertical AI be monitored, controlled, and managed at scale without building the same sort of stuff that RPA has created?
The big shift will be from orchestration to choreography.
For most work processes beyong the ubiquitous 'vacation request' demo orchestration was never viable as a model due to its inherent inflexibility aligned with rose coloured, sunny day snenarios clashing with the real fluidity and complexity of the actual work floor.