In the context of generative AI, agents serve as autonomous intermediaries, handling tasks like data extraction, interpretation, decision-making, and even query formulation. Unlike traditional automation, agents are intelligent—they can adapt, learn, and make decisions based on the context of the data they process.

How Agents Revolutionize Generative AI:

Agents are set to transform generative AI by introducing an autonomous layer that can interpret the human query and plan the tasks for downstream agents, interact with both structured and unstructured data, bridging the gap between raw data and human-intelligible insights. With agents, users don’t need deep technical knowledge to harness the power of AI. Agents can interpret complex instructions, interact with databases, and generate valuable outputs without needing to understand the underlying code or architecture

AI Agents – Understanding Unstructured Data:

AI Agents specialize in understanding and interpreting natural Language. Execute basic AI task like plotting trends, developing predictive models etc.

Text-to-Query Agents – Simplifying Interaction with Structured Data:

Text-to-Query Agents enable non-technical users to interact with structured datasets (like databases) using natural language instead of technical query languages. These agents convert human-readable questions into machine-readable queries and retrieve the desired information from structured sources.

Why Agents Matter in Generative AI:

Agents introduce an intelligent, user-friendly layer between complex datasets and the people who need to interact with them. Whether it’s extracting insights from unstructured data or querying structured data.