The model that writes convincing code still cannot get past a login screen on its own. Why agents fail on ordinary obstacles, and how the harness around the model - tools, limits and verification - makes them reliable.
A Hindi question fails where the identical English one succeeds — and the LLM is not the problem. Why multilingual and multimodal retrieval breaks, and how to build a pipeline that does not.
Generative AI waits for your next prompt. Agentic AI takes a goal and runs the whole workflow — planning, reasoning, remembering, and escalating to you only when it must. Here is how that machinery actually works.
Your AI can reason brilliantly but can't touch your Gmail, Slack, or database. Here's why the Model Context Protocol is being called AI's 'USB-C moment' — and how it turns isolated language models into agents that actually get work done.
Retrieval-Augmented Generation grounds an LLM in your own current, proprietary data. This is the complete production blueprint on AWS — S3 ingestion, Bedrock embeddings, OpenSearch Serverless vector storage, and retrieve-and-generate through API Gateway.