Illustration for the Sazko Solutions article on agentic AI adoption in the enterprise
Posted on Author Mahesh Kumar Categories AI, Digital, Software

Agentic AI in the Enterprise: From Chatbots to Systems That Act

Google’s Cloud Next in April 2026 was billed as the arrival of the “agentic era,” complete with an enterprise agent platform and chips designed for it. The word “agent” gets applied loosely enough right now that it’s worth being precise. A chatbot takes an input and returns an output. An agent does something meaningfully different: it holds a goal, plans a sequence of steps to reach it, calls tools or other systems along the way, observes the results, and decides what to do next — often without a human approving each step. That loop, not the underlying language model, is what “agentic AI” actually refers to.

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Sazko Solutions article on choosing an AI model during the March 2026 wave of frontier releases
Posted on Author Mahesh Kumar Categories AI, Consultancy, Software

Five Frontier Models in One Month: Choosing When the Ground Keeps Moving

March 2026 saw five significant model launches in a matter of weeks — new releases from Mistral, DeepSeek, OpenAI, Google and xAI, with one open-source model topping reasoning benchmarks within days of release. If you’re responsible for choosing which model your product runs on, that pace is the actual problem to solve, not any individual model.

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Illustration for the Sazko Solutions article explaining the Model Context Protocol (MCP) and agentic AI
Posted on Author Mahesh Kumar Categories AI, App Development, Software

What MCP Actually Is, and Why It’s Becoming AI’s Common Plumbing

By March 2026, the Model Context Protocol had reportedly crossed tens of millions of installs, and NVIDIA’s GTC keynote was full of enterprise agents running in production. Every AI agent eventually needs the same thing: a reliable way to reach outside its own context window — into a database, a ticketing system, a filesystem, an internal API — and act on what it finds. Before the Model Context Protocol (MCP), teams solved this the same way they solved every integration problem: a bespoke adapter per tool, per model, per vendor. MCP’s contribution is boring in the best possible way — it standardizes that connector so it only has to be built once.

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