As a concept, Agentic AI combines language models, custom code, data and APIs to create intelligent workflows capable of solving business problems.
It represents a shift towards more autonomous decision-making systems. Here an agent is a piece of code capable of perceiving its environment. It could be done through sight, sound or text. The decision is based on such inputs.
It could be applied to simple code generation on WhatsApp to complex functions such as SCM- supply chain management or customer engagement enhancement.
An agent takes the initiative and makes decisions to solve problems autonomously.
AI agents will be the next big thing. Zuckerberg said recently there could be more AI agents in the world than humans. Google too is fiddling with AI agents. Kyndryl is a tech player who is going big on this. It is a spin-off from IBM in 2021.
Generative AI is being used in production. Agentic AI will bring about a significant shift in how things work. Agentic AI is used when there is mixed problem solving.
The queries are directed to the most suitable agent. One agent might resort to RAG. Another accesses real-time data. The former uses internal data and the latter external data (through APIs). What results is a collaborative workflow. Multiple agents work on different aspects of a problem based on a query. Agentic AI is the future.
Agentic AI is designed to run specific functions within an organization without human intervention. Agentic AI technology is gaining traction as businesses look to automate business workflows. It also augments the output of human workers.
Some organization try to build Agentic AI alone. Most of them fail. They then turn to outside firms to build these agents for them. Or else they use embedded agents from their vendors.
Building agents is a complex process. Organization may lack this expertise in-house.
Agentic AI facilitates the use of generative AI from basic tasks to more complex actions.
The architecture is convoluted. It requires multiple models — RAG, advanced data architecture and specialized expertise.
It is a nascent field. In a couple of years, it is likely to mature.
There are some open-source models too. These models could be linked to turn them into agents. They will then perform their assigned tasks without human prompts. Building on open-source model is more efficient than creating AI agents from scratch.
There should be MLOPs plan in the organization. It was aspirational technology a few years back. It is now being realized.
Though these agents run autonomously, building successful Agent AIs requires human supervision.
Many in-house projects spiral out of control in terms of cost and complexity.
Some companies train their own Agent AIs, but many lack this expertise. In addition, there are maintenance costs in future. It is complex and resource intensive.
When outsourced from suppliers, we get ready-made agents which have been tested and refined by thousands of users. This process is faster too.
In an agentic AI system, there comes the management of robust memory management.
Building Agentic AI from scratch involves designing data structures, implementing search algorithms and fine-tuning its ability to interpret and prioritizing information. It calls for ML, NLP and data engineering expertise.
By outsourcing, we are leveraging the experience and expertise of others who have navigated such systems.