Machine Learning, Generative AI & Agentic AI

Three layers of modern AI

Artificial Intelligence spans multiple approaches. In practice, most solutions fall into three overlapping areas: Machine Learning, Generative AI, and Agentic AI.

1) Machine Learning (ML)

Machine Learning focuses on building models that learn patterns from data to make predictions or decisions. Typical tasks include classification, regression, forecasting, and anomaly detection.

• Supervised learning: learn from labeled examples (e.g., spam detection).
• Unsupervised learning: find structure in unlabeled data (e.g., clustering customers).
• Reinforcement learning: learn through rewards (e.g., game-playing, robotics).

2) Generative AI

Generative AI creates new content—text, images, audio, or code—based on patterns learned from large datasets. Many systems use foundation models and are often accessed via prompting.

• Language generation: chatbots, Q&A, summarization, drafting.
• Multimodal generation: text-to-image, image-to-text, video-related workflows.
• Code generation: assist with scaffolding, refactoring suggestions, and examples.

3) Agentic AI

Agentic AI goes beyond generating responses. It uses an agent that can plan, use tools (APIs, search, databases), and iterate toward a goal—often with memory and task decomposition.

• Planning: break a goal into steps.
• Tool use: call external systems to get facts or take actions.
• Iteration: check results and refine outputs.

Want to apply these ideas to your business processes? We can help design end-to-end solutions—data strategy, model selection, integration, and responsible AI practices.