Tag: AI agents
-
watsonx Reference Architectures for RAG, Agentic, and Regulated Workloads (IBM Gen AI Series, Part 23)
The three watsonx reference architectures that recur in real builds: enterprise RAG on watsonx.data, agentic systems on watsonx Orchestrate, and a watsonx.governance overlay for regulated work. Which to build first, what each costs, and where they break.
-
Building Agents on watsonx Orchestrate with the ADK (IBM Gen AI Series, Part 15)
How to build a native agent on watsonx Orchestrate with the ADK, wire its tools, collaborators, and knowledge, pick the right agent style, and keep multi-agent routing from wrecking your bill.
-
Gemini Enterprise and Agentspace, Enterprise Agents for Every Employee (Google Cloud Gen AI Series, Part 22)
Google Agentspace is now Gemini Enterprise, the per seat front door that puts search and a gallery of agents in front of every employee. What it includes, how a request flows, what a seat costs, and when to buy it instead of building your own.
-
Multi-Agent Systems on Vertex AI with ADK and Agent Engine (Google Cloud Gen AI Series, Part 21)
One big agent with twenty tools rots fast. Here is how to split it into a coordinator and typed sub-agents with the ADK, choose deterministic versus LLM-driven flows, connect across boundaries with A2A, and deploy to Vertex AI Agent Engine.
-
Vertex AI Agent Builder and the ADK, From Local Agent to Managed Runtime (Google Cloud Gen AI Series, Part 13)
Build an agent in code with the ADK, deploy it to the managed runtime, and see what the always-on replica really costs before your first user shows up.
-
Microsoft Copilot Studio, from First Agent to Autonomous Workflow (Azure Gen AI Series, Part 22)
Copilot Studio builds low-code agents on Microsoft 365 and Power Platform. Where building is free, where Copilot Credits start, and when to reach for pro-code instead.
-
Semantic Kernel, AutoGen, and Microsoft Agent Framework on Azure (Azure Gen AI Series, Part 21)
Semantic Kernel and AutoGen merged into Microsoft Agent Framework in 2026. Here is how the unified SDK works, what a migration costs, and where I would still wait.
-
Azure AI Foundry Agent Service, from First Agent to Production (Azure Gen AI Series, Part 13)
Foundry Agent Service is the managed runtime in Azure AI Foundry that stores an agent, runs it against a conversation thread, and calls tools. Here is how the pieces fit, when to switch to standard setup, and where it breaks.
-
Bedrock Reference Architectures for Chatbot, RAG, Agentic, and Batch (AWS Gen AI Series, Part 29)
Most AWS generative AI features are one of four shapes: chatbot, RAG, agentic, or batch. Here is how each maps to Amazon Bedrock services, what it costs, and which one to reach for first.
-
Amazon Bedrock Agents, Action Groups, and the AgentCore Shift (AWS Gen AI Series, Part 13)
How Bedrock agents turn one question into several model calls, how action groups and return of control work, what a request really costs, and why new builds now start on AgentCore.
-
AI Agents: What Actually Works, and What’s Hype (GenAI Series, Part 16)
An AI agent is a model in a loop that plans, calls tools, and observes results. What agents genuinely do well today, and why reliability, not intelligence, is the real bottleneck.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
You May Have Missed
DrJha