What is Agentic AI?
AI systems that can independently plan multi-step tasks, make decisions, use tools, and take actions to achieve defined ...
AI systems that can independently plan multi-step tasks, make decisions, use tools, and take actions to achieve defined goals with minimal human intervention. Agentic AI moves beyond simple question-and-answer interactions to handle complex workflows end to end.
Agentic AI represents the next evolution beyond conversational AI. Rather than responding to a single prompt with a single output, an agentic system breaks a complex goal into subtasks, executes them in sequence, checks its own work, and adapts its approach based on results. This is the difference between asking AI to draft one email and asking it to manage an entire onboarding workflow.
For regulated mid-market businesses, agentic AI is relevant because many high-value processes involve multiple sequential steps that currently require human coordination. Client onboarding in financial services, for example, involves identity verification, risk assessment, suitability checking, document generation, and compliance logging. An agentic system can orchestrate these steps, pulling data from each stage to inform the next, and escalating to a human only when it encounters something outside its defined parameters.
The governance implications of agentic AI are more significant than for simpler AI tools. When an AI system is making a sequence of decisions and taking actions autonomously, you need clear boundaries on what it can and cannot do, robust logging of every step it takes, and well-defined escalation criteria. For FCA-regulated firms, this means being able to demonstrate that the agent operates within approved parameters and that its decision chain is fully auditable.
Most mid-market firms are not yet deploying fully autonomous agents for client-facing processes, and that caution is appropriate. The current sweet spot is what might be called supervised agents: systems that automate the execution of multi-step workflows but require human approval at defined checkpoints. This gives you the efficiency benefit of automation with the control that regulators expect.
The firms best positioned to adopt agentic AI are those that have already mapped their processes clearly, established AI governance frameworks, and built confidence with simpler AI tools. Agentic AI is not a starting point. It is where you arrive once the foundations are solid.
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