Adam Strojek

AI Adoption Leader

As an AI Adoption Leader, I specialize in guiding companies through their digital evolution, preparing them to harness the full potential of Generative AI. My role involves not only strategic planning for AI adoption but also identifying key areas for automation and building the teams capable of executing this vision.

This strategic leadership is built upon a solid foundation as an experienced Tech Leader with a deep-rooted consulting mindset. With a background in Python development stretching back to version 2.5, I possess a robust understanding of the technical and business challenges clients face. This experience is critical for choosing the right technologies to deliver practical, long-lasting solutions—a principle proven by the fact that projects I’ve led over the last decade remain in production today.

My approach is both strategic and hands-on. I have extensive experience building RAG (Retrieval-Augmented Generation) applications and in the creation and management of MCP Servers. I develop AI agents that streamline complex processes, reducing tasks to minutes within a user-friendly environment. Furthermore, I am dedicated to empowering teams by conducting specialized AI training.

My passion for technology extends to exploring modern ecosystems like Rust and WebAssembly, and I actively contribute to the open-source AI community. This commitment to continuous learning ensures I bring the most current and effective solutions to every challenge, making me an ideal partner for any organization ready to embrace its AI-powered future.

Recent

Personal Brain Agent: Current Status and Future Plans

28 November 2025·974 words·5 mins

I’m excited to introduce the Personal Brain Agent, a new AI plugin for Obsidian.md that seamlessly integrates a powerful agent into your second brain. While AI agents are a well-tested concept among software engineers, my goal with this plugin is to make this powerful technology accessible to a broader, non-technical audience. This post is a deep dive into its features, demonstrating how anyone can transform their workflow by moving beyond simple chatbots.

Personal Brain: My Journey with ACP in Obsidian

14 November 2025·1649 words·8 mins

In my previous article, From MCP to ACP - A New Standard in AI Agent Interoperability, I explored the evolving landscape of AI agent protocols. This month, I put theory into practice by integrating the Gemini-CLI agent into my favorite note-taking tool, Obsidian.md. The result is a working MVP of a plugin I’m calling Personal Brain, which seamlessly embeds an AI agent into your second brain.

From MCP to ACP: A New Standard in AI Agent Interoperability

8 October 2025·914 words·5 mins

After a recent update to the Zed editor, I noticed it now includes native support for AI agents like Gemini CLI and Claude Code. What is surprising is not the integration itself, but the collaborative effort behind it. Zed Industries, together with Google, announced the Agent Client Protocol (ACP), an open standard designed to do for AI agents what the Model Context Protocol (MCP) did for tools: enable developers to focus on building the best possible agents without getting bogged down by integration complexities.

Inspired by the Language Server Protocol (LSP) and MCP (Model Context Protocol), the protocol was jointly designed, with Zed focusing on the editor (client) and Google on the agent. The initial announcement from Zed provides a deeper dive into their vision of unbundling AI agents from IDEs.

Monitor and control your LLM token spend

29 September 2025·1419 words·7 mins

Across consultations on bringing AI into client products, the same concerns keep surfacing: unpredictable spend, lack of visibility into usage, and the risk of runaway bills from bugs or developers’ misuse. Clients ask for monitoring, budgets, alerts, and cost allocation at various levels of granularity — by project, feature, environment, region, and team — along with credible forecasts. To streamline those conversations, I decided to put the key guidance and patterns into this blog post.

Uncovering limitations of Gemini over OpenAI

10 July 2025·440 words·3 mins

Recently, while developing my AI agent library, agentai, I introduced a new CI pipeline to run examples against various models for each pull request. This process ensures that all elements of the library work correctly with each new release. I started by running these tests using GitHub Models, primarily for convenience (as they are already integrated) and to enable external contributors to use the same test suite in the same environment.