Tag: MCP
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What Is Agentic AI Architecture? The Six Layers That Actually Decide Outcomes
Agentic AI architecture is the design of the system around the model: the layers that decide what an agent is asked to do, what it may reach, what it is forbidden to do, how far it may act without a person, and how any of that is proved afterwards. The model is a component in…
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27 Key Agentic AI Terms, What They Mean and Where They Came From
This is a glossary of 27 key agentic AI terms — what each one means, where it came from, and one authoritative source to read for each. The vocabulary of agentic AI has moved faster than any enterprise glossary I have seen in twenty years of consulting. Terms that were lab jargon eighteen months ago…
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Agentic AI Model Harness: The Layer That Decides Agent Outcomes
What Is an AI Model Harness? An AI model harness is everything wrapped around a raw language model that turns it into a working system — the system prompts, tool interfaces, context and memory management, the agent loop, orchestration, guardrails, permissions, sandboxing, and observability. The model weights are not the harness. And in 2026, the…
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What Is Agentic AI Technology? A Practitioner’s Field Map of the Enterprise Stack
“Agentic AI technology is the software stack that lets an enterprise hand a system an outcome to achieve rather than a script to execute” © Dr. Harish Kotadia, 2026 — the model, memory, tools, identity, guardrails, and observability that turn a goal into governed, auditable action. Instructions in, results out — that was IT. Intent…
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Agentic AI: The hidden cost of NOT doing it RIGHT
Most enterprises are still running linear LLM chains and calling it “AI transformation.” Here’s what that’s actually costing you: Fact 1: Organizations running agentic workflows report up to 35% gains in operational efficiency and materially faster task execution. Your competitors aren’t waiting. Fact 2: Gartner says 40% of enterprise apps will embed task-specific AI agents…