The agentic AI era is here — and Claude by Anthropic is at the center of it.
What excites me most right now:
🔹 Model Context Protocol (MCP) — standardizing how AI agents talk to tools and data sources, enabling true multi-agent orchestration
🔹 Extended Thinking — Claude reasoning through complex, multi-step problems before responding
🔹 Prompt engineering & tool use — designing robust agentic workflows with RAG, memory, and structured outputs
🔹 AI Safety & Responsible Scaling Policy (RSP) — Anthropic’s commitment to ensuring frontier models remain trustworthy as capabilities grow
Whether you’re building on Claude Sonnet, deploying via Amazon Bedrock, or exploring LLMOps for production grade AI, the patterns we architect today will define enterprise AI for the next decade.
We used to build AI like this:
User → App → Cloud API → Response
Call the model. Get a string back. Done.
It worked for demos. It doesn’t work for enterprise.
Here’s what the shift actually looks like:
The new pattern puts Claude at the center of a loop — not a pipeline.
Instead of returning a response, Claude now:
Reasons — Extended Thinking lets it work through multi-step problems before acting
Uses tools — via Model Context Protocol (MCP), it calls APIs, queries databases, runs code, searches the web
Accesses memory — RAG, conversation history, and structured context travel with every request
Self-verifies — it checks its own output, retries if needed, and refines before delivering
Completes tasks — not just answers questions
This is what “agentic AI” actually means in practice.
Why does this matter for enterprise architects?
Because the old mental model — “LLM as a smarter autocomplete” — breaks the moment you need the system to do something, not just say something.
Processing a loan application. Analyzing a utility outage. Drafting and filing a contract. These aren’t Q&A tasks. They’re multi-step workflows that require reasoning, tool use, and memory working together.
Claude on AWS Bedrock (Agents + Knowledge Bases + Guardrails) and Google Vertex AI (Agent Builder + Grounding + Vector Search) now give you the managed infrastructure to run this loop at enterprise scale — with IAM, VPC, compliance, and audit trails built in.
The model is no longer the product. The loop is the product.
Dr. Harish Kotadia, Ph.D. is an Enterprise AI Architect with 20+ years of Fortune 100 consulting experience, specializing in agentic AI systems built on Anthropic Claude, AWS Bedrock, and Google Vertex AI.
© Dr. Harish Kotadia, 2026. All Rights Reserved.