The rise of AI agents—autonomous software that observes, reasons, plans, and acts independently—marks a paradigm shift from predictive analytics to operational execution. Globally, the AI agents market surged from around $5-8B in 2024-25 to projections exceeding $50B by 2030, driven by machine learning and multi-agent systems.
AI agents employ a layered architecture: perception (data ingestion via APIs), reasoning (ML models like transformers for anomaly detection), planning (orchestration layers coordinating multi-agents), and action (API calls for blocks/approvals), all under 100ms latency. Multi-agent systems distribute tasks—e.g., one agent for transaction velocity checks, another for device fingerprinting—using parallel processing and shared context stores for scalability. Feedback loops capture human overrides as signals, enabling continuous learning without full retrains, ensuring adaptability to evolving threats.
For those interested in exploring the practical implementation of AI agents, documentation and resources are available through major technology providers. For example, you can learn how to build AI agents using tools and documentation on the Google AI developer site or by exploring the Microsoft Azure AI documentation.
☘️ Prof. Sudesh Kumar
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- Excerpt from my Guest Lecture for High School Students
