Agentic AI - a type of AI system that can operate autonomously, adapt in real-time, and solve complex problems with minimal human intervention - has moved beyond do-it-yourself fixes to a standardised architecture. This includes foundational platforms, orchestration layers and the use of protocols. For example, a bank is automating its customer service workflow. It needs a multi-agent framework that chops up the work into repetitive pieces, a system for coordinating agents, and a common language for agents to communicate among themselves. Furthermore, the automated workflow requires an audit trail. Each decision made by an AI agent must leave a digital footprint. Finally, the process must operate under human supervision. To be effective, human decision-making cannot be introduced only in exceptional circumstances, by which time there could be an irretrievable loss of context. Agentic AI workflows, thus, need to be made to pause at regular intervals, seeking human intervention even when digital agents can complete the task.
This is the level of complexity for a single workflow. But enterprises must move beyond isolated automation to integrate business processes to harvest the potential of agentic AI. Some of these are business-critical functions, such as credit appraisal for banks, where the level of delegation to digital agents will be low. The system complexity mounts with the range of processes intended for automation, requiring sophisticated human oversight over the technology and business outcomes. The human workforce must be exposed to a new set of gaming scenarios involving digital decision-making. The risk of an enterprise over-surrendering human agency is high.
It, thus, makes sense for enterprises to slide into agentic AI through low-skill processes and expand upwards. Yet, to protect brands, companies will have to keep an eye out for ethical incorporation of AI into the workflow. Companies will also have to keep their human employees in the loop because their feedback at every step of the agentic AI transformation is vital.
This is the level of complexity for a single workflow. But enterprises must move beyond isolated automation to integrate business processes to harvest the potential of agentic AI. Some of these are business-critical functions, such as credit appraisal for banks, where the level of delegation to digital agents will be low. The system complexity mounts with the range of processes intended for automation, requiring sophisticated human oversight over the technology and business outcomes. The human workforce must be exposed to a new set of gaming scenarios involving digital decision-making. The risk of an enterprise over-surrendering human agency is high.
It, thus, makes sense for enterprises to slide into agentic AI through low-skill processes and expand upwards. Yet, to protect brands, companies will have to keep an eye out for ethical incorporation of AI into the workflow. Companies will also have to keep their human employees in the loop because their feedback at every step of the agentic AI transformation is vital.
