Discover how Microsoft’s two key frameworks, Semantic Kernel and AutoGen, provide a powerful stack for creating sophisticated multi-agent AI systems. This article explores how to build specialized agents with Semantic Kernel and orchestrate their collaboration with AutoGen, while also comparing this approach to leading alternatives like CrewAI and LangGraph.

I recently finished reading “Unlocking Data with Generative AI and RAG” by Keith Bourne, and it provides a comprehensive overview of building systems that can leverage large language models (LLMs) with your own private or new data. This post summarizes my key takeaways from the book.

The core idea is that the key technical components of a Retrieval-Augmented Generation (RAG) system include the embedding model that creates your embeddings, the vector store, the vector search mechanism, and the LLM itself.

Enhancing Codehub application with agentic capabilities can transform it from a static reporting tool into an intelligent engineering insights platform that proactively helps teams improve their performance and productivity.

See also Agentic Features for Codehub: Engineering Metrics Intelligence (part 1 of 2)

Integrating agentic features into Codehub app is an excellent way to enhance its value proposition, moving it from a data visualization tool to an intelligent assistant for engineers and managers. In this article I’m brainstorming (with AI) several agentic features, categorized by the problems they solve and the personas they target.

Agentic applications represent a significant leap in AI, moving beyond simple chatbots to systems that can autonomously reason, plan, and execute multi-step tasks to achieve a defined goal. They are designed to operate with minimal human intervention, continuously learning and adapting over time.

I recently embarked on an interesting experiment with GitHub Copilot Agent that has given me some valuable insights into the evolving landscape of software development. I thought it would be worth sharing my experience and observations.

Discover how Microsoft’s two key frameworks, Semantic Kernel and AutoGen, provide a powerful stack for creating sophisticated multi-agent AI systems. This article explores how to build specialized agents with Semantic Kernel and orchestrate their collaboration with AutoGen, while also comparing this approach to leading alternatives like CrewAI and LangGraph.

This Week’s Challenges:

• Is APL the Future of Programming? A Look at its Practical Value

I recently finished reading “Unlocking Data with Generative AI and RAG” by Keith Bourne, and it provides a comprehensive overview of building systems that can leverage large language models (LLMs) with your own private or new data. This post summarizes my key takeaways from the book.

The core idea is that the key technical components of a Retrieval-Augmented Generation (RAG) system include the embedding model that creates your embeddings, the vector store, the vector search mechanism, and the LLM itself.

This Week’s Challenges:

• From Prompt Engineering to Language Engineering: The Story of APL

Engineering managers operate at the intersection of numerous information streams, leading to significant context-switching overhead and potential loss of critical information. This post outlines the design for a context-aware AI assistant, orchestrated by the Gemini CLI, to solve this problem. We will explore two solutions: a short-term workaround using a command-line bridge and the recommended long-term solution involving a dedicated local server.

The life of an engineering manager is a constant exercise in context switching. Between processing a flood of emails, keeping up with Slack channels, managing Jira tickets, reading Confluence pages, and preparing for one-on-one meetings with notes in OneNote, the mental overhead can be staggering. The core challenge isn’t just managing tasks; it’s about maintaining and retrieving context across a dozen different platforms, often in parallel.

This Week’s Challenges:

• Achieving Operational Excellence through Observability

Enhancing Codehub application with agentic capabilities can transform it from a static reporting tool into an intelligent engineering insights platform that proactively helps teams improve their performance and productivity.

See also Agentic Features for Codehub: Engineering Metrics Intelligence (part 1 of 2)

Integrating agentic features into Codehub app is an excellent way to enhance its value proposition, moving it from a data visualization tool to an intelligent assistant for engineers and managers. In this article I’m brainstorming (with AI) several agentic features, categorized by the problems they solve and the personas they target.

Agentic applications represent a significant leap in AI, moving beyond simple chatbots to systems that can autonomously reason, plan, and execute multi-step tasks to achieve a defined goal. They are designed to operate with minimal human intervention, continuously learning and adapting over time.

This Week’s Challenges:

• From Kafka events to Tableau reports: A 3-Step Funnel to a Cost-Effective Analytics Architecture

I recently embarked on an interesting experiment with GitHub Copilot Agent that has given me some valuable insights into the evolving landscape of software development. I thought it would be worth sharing my experience and observations.

This Week’s Challenges:

• FY26Q1 planning - Q1 Game Plan (Management)
• Engineering excellence metrics (Engineering)

This Week’s Challenges:

• Finalize FY26Q1 planning (Management)
• Engineering excellence metrics (Engineering)

This Week’s Challenges:

• Making Engineering Excellence Measurable: Sharing My Codehub Project

This Week’s Challenges:

• Building the Codehub: A Journey into GitHub Data and Static Site Generation (Engineering)

This Week’s Challenges:

• Diving into Static Site Generation with Hugo (Engineering)

I recently finished reading “Unlocking Data with Generative AI and RAG” by Keith Bourne, and it provides a comprehensive overview of building systems that can leverage large language models (LLMs) with your own private or new data. This post summarizes my key takeaways from the book.

The core idea is that the key technical components of a Retrieval-Augmented Generation (RAG) system include the embedding model that creates your embeddings, the vector store, the vector search mechanism, and the LLM itself.

Systems thinking is essential for addressing complex challenges: understanding system dynamics is crucial for developing effective and sustainable solutions to environmental, social, and economic problems.

Engineering managers operate at the intersection of numerous information streams, leading to significant context-switching overhead and potential loss of critical information. This post outlines the design for a context-aware AI assistant, orchestrated by the Gemini CLI, to solve this problem. We will explore two solutions: a short-term workaround using a command-line bridge and the recommended long-term solution involving a dedicated local server.

The life of an engineering manager is a constant exercise in context switching. Between processing a flood of emails, keeping up with Slack channels, managing Jira tickets, reading Confluence pages, and preparing for one-on-one meetings with notes in OneNote, the mental overhead can be staggering. The core challenge isn’t just managing tasks; it’s about maintaining and retrieving context across a dozen different platforms, often in parallel.

This Week’s Challenges:

• Promotion nominations (Management)

This Week’s Challenges:

• Setting individual goals for FY26 (Management)

This Week’s Challenges:

• Engineering strategy for FY26 (Management)

This Week’s Challenges:

• Annual Performance Review (Management)

This Week’s Challenges:

• FY25Q4 retrospective (Management)

This Week’s Challenges:

• FY26Q1 planning - Q1 Game Plan (Management)
• Engineering excellence metrics (Engineering)

This Week’s Challenges:

• Finalize FY26Q1 planning (Management)
• Engineering excellence metrics (Engineering)

Engineering Team Planning Process - LOE Estimations

This document outlines the process followed by the engineering team for planning and estimating work for the next quarter of the fiscal year.

This Week’s Challenges:

• Planning FY26Q1 - Level-of-Effort (LOE) estimations

Quaterly Planning Process

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1. PRD reviews
2. Capacity Planning
3. Level-of-Effort Estimations
4. Prioritization and commitment (see Week 4 of the year 2025)
5. Build a game plan (see Week 5 of the year 2025)
6. Present next quarter to the team

See also:

1. Quarterly retrospective meeting in Week 6 of the year 2025

Quaterly Team Capacity Planning

This formal process provides a structured approach to team capacity planning, enabling more accurate resource allocation, improved project predictability, and enhanced team performance.

This Week’s Challenges:

• Navigating Team Capacity Planning (Management)

This Week’s Challenges:

• From Kafka events to Tableau reports: A 3-Step Funnel to a Cost-Effective Analytics Architecture

Hugo is one of the most popular open-source static site generators. With its amazing speed and flexibility, Hugo makes building websites fun again.

And this is how this web site was built - Hugo + Ananke.