Illustration for the Sazko Solutions article on n8n and AI-orchestrated workflow automation
Posted on Author Mahesh Kumar Categories AI, App Development, Software

n8n and the Rise of AI-Orchestrated Workflows

As of August 2026, several of the newest large models are being marketed specifically for managing long-running projects and collaborating with developers. Workflow orchestration tools are where a lot of that ambition meets reality. Workflow automation tools have existed for years, but something changed once AI models got good enough to sit inside the workflow rather than just around it. n8n is a useful example: an open-source, node-based automation tool that was already popular for connecting APIs and services, and has become a common substrate for AI-orchestrated workflows precisely because its workflows are structured, inspectable, and easy for both humans and models to reason about.

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Illustration for the Sazko Solutions article on AI-assisted coding in brownfield versus greenfield software projects
Posted on Author Mahesh Kumar Categories AI, App Development, Software

AI-Assisted Coding: Brownfield vs. Greenfield Projects

July 2026 brought another wave of coding-focused model releases promising near-frontier capability at lower cost. They’re genuinely good — and they make the gap between greenfield and brownfield work more visible, not less. Most conversations about AI-assisted coding default to the easy case: a brand-new codebase, a clean slate, and a coding agent that scaffolds an app in minutes. That’s greenfield work, and it’s genuinely where tools like GitHub Copilot, Cursor, and Claude Code look most impressive. The harder and far more common reality for most engineering teams is brownfield: an existing system with years of undocumented decisions, tangled dependencies, and business logic nobody fully remembers the reason for.

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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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