Reimagining Automation

AI agents are emerging as game changers in the movement towards automation. In the past three years, since the advent of ChatGPT, generative AI tools have advanced by leaps and bounds. However, attention is now shifting to AI agents capable of thinking, acting and collaborating autonomously.

There is a rapid journey towards automation from chatbots to retrieval-augmented-generation (RGA) to automatic multi-agent AI. By 2028, there would be 33 per cent enterprise software applications as against 1 per cent in 2024.

Agentic workflows would expand the set of tasks that AI can do. Organizations will move from predefined processes to dynamic intelligent workflows.

Traditional Automation: Limitations

Traditional automation tools are rigid. Costs are high. We have dealt with RPA or robotic process automation. The workflows struggle with processes which are not clear. Or else, these rely on unstructured data. The systems are brittle and need vendor attention when processes change.

Chatbots do reason and generate content but lack the capability of autonomous execution.

They do require human element when there are complex tasks.

Automation has to go beyond predefined processes to dynamic intelligent workflows.

Vertical AI Agents

In evolution, we are shifting to vertical AI agents. These are smarter and proactive. They accomplish task across domains. They improve over a period of time. They memorize the activities and sense your intent and recognize patterns in your behaviour.

SaaS models do optimize existing workflows. Vertical AI agents reimagine them entirely. They eliminate the operational teams (because of autonomous execution). Reimagining workflows brings new capabilities hitherto unexisting. They give a competitive advantage.

Transition from RPA to Multi-agent AI

Multi-agent AI systems are capable of autonomous decision making. By 2028, this is going to be prevalent to the extent of 15 per cent of day-to-day decisions. Agents will be true collaborators, changing enterprise workflows and systems.

There would multi-modal systems of record to get actionable insights. Complex tasks will be broken into manageable components. Tooling will change — there could be AI Agent Studio. Co-workers will be freed up for strategic tasks. Agents will have better memory, advanced coordination capabilities and advanced reasoning.

Agents will move from doing jobs to managing workflows to doing the entire jobs. Then arises the challenge of their accuracy. An AI agent executes a single task with 85 per cent overall accuracy. When it starts doing two tasks, the accuracy becomes 72 per cent (0.85 x0.85). As the combination keeps growing, the accuracy drops further. Is it then acceptable? We have to optimize to an accuracy level of 90-100 per cent.

We, therefore, require robust evaluation frameworks. There should be continuous feedback. There should be automated optimization tools.

Ai agents will stay as our co-workers. They will transform the enterprise operations. They will unlock unprecedented efficiency. It is time to ACT. Are you ready?