AI Engineering on AI VOID

Modern AI Engineering: Core Concepts & Emerging Topics (2026)

Fri, 20 Mar 2026 00:00:00 +0000

What You Will Learn

This guide introduces the most important modern AI engineering topics as of 2026, focusing on real-world systems, architectures, and tools used in production. You will understand how AI systems are built, orchestrated, evaluated, and scaled, along with emerging trends shaping the future of software engineering.

Core AI Engineering Topics (2026)

1. Agentic AI Systems

Learn how autonomous AI agents operate, including planning, reasoning, tool usage, and multi-agent coordination in real-world workflows.

Navigating the LLM's Memory: Understanding the Context Window

Fri, 20 Mar 2026 00:00:00 +0000

Navigating the LLM’s Memory: Understanding the Context Window

Welcome back, future AI architect! In our previous chapter, we introduced the exciting field of Context Engineering – the art and science of preparing information for Large Language Models (LLMs) to achieve optimal performance. Now, it’s time to get up close and personal with the very core of an LLM’s “short-term memory”: the Context Window.

In this chapter, we’ll peel back the layers to understand what the context window truly is, why it’s so incredibly important, and how LLMs process information within its confines. We’ll explore the concept of tokens, how they relate to the context window’s size, and the practical implications this has for your AI applications. By the end, you’ll have a solid foundation for managing the data flow into your LLMs, setting the stage for more advanced context engineering techniques.

Implementing Persistent Agent State with External Storage

Sat, 23 May 2026 00:00:00 +0000

In the previous chapter, we established a basic AI agent and managed its conversational context in memory. While useful for short, single-session interactions, this approach falls short for long-running agents that need to survive restarts, process background tasks, or maintain context across multiple user sessions. Losing an agent’s state means losing its memory, its progress, and ultimately, its utility. Without persistence, a crash or planned shutdown erases all prior interactions, forcing the agent to start anew, which is unacceptable for production systems.

Mastering Basic Workflows: Events, Tasks, and Retries

Wed, 20 May 2026 00:00:00 +0000

Welcome back! In the previous chapter, we successfully set up our Trigger.dev project, getting ready to build powerful automated systems. Now, it’s time to dive into the fundamental building blocks that make Trigger.dev workflows so resilient and effective: Events, Tasks, and Retries. These three concepts are the bedrock for creating robust, automated workflows and AI agents that gracefully handle the complexities and inevitable failures of real-world production environments.

This chapter will guide you through understanding what events are, how tasks execute reliably, and how Trigger.dev automatically handles failures through intelligent retries. By the end, you’ll be able to create your first resilient workflow, capable of reacting to external signals and executing durable, fault-tolerant operations, boosting your confidence in building production-ready systems.

Orchestrating Intelligence: Patterns for Multi-Step Workflows

Fri, 20 Mar 2026 00:00:00 +0000

Introduction: Beyond Single-Shot Prompts

Welcome back, aspiring AI architect! In the previous chapters, we introduced the fundamental building blocks of AI agents: their ability to perceive, reason, and act, often augmented by powerful tools. We saw how a single agent, given a clear prompt and access to tools, can perform impressive feats. But what happens when a problem is too complex for one agent or requires a sequence of decisions and actions that aren’t purely linear?

Designing for Context Preservation and Resume Capabilities

Sat, 23 May 2026 00:00:00 +0000

In the realm of AI agents, a critical challenge arises when agents need to perform long-running tasks or maintain complex interactions over extended periods: how do they remember what happened, and how can they pick up exactly where they left off after an interruption? This chapter addresses that challenge head-on. We’ll design and implement a robust mechanism for our Google ADK agent to preserve its state and conversational context, enabling it to pause, resume, and recover from failures without losing valuable information.

Agent Operating Systems (Agent OS): The Foundation for Intelligent Behavior

Fri, 20 Mar 2026 00:00:00 +0000

Introduction: Giving AI Agents a Home

Welcome back, future AI architect! In the previous chapters, we laid the groundwork for understanding the shift towards more complex, capable AI systems. Now, we’re diving into a crucial concept that makes these advanced systems possible: Agent Operating Systems (Agent OS).

Think of an Agent OS as the brain and nervous system for your AI agents. Just as your computer needs an operating system (like Windows, macOS, or Linux) to manage its hardware, software, and resources, AI agents need a specialized operating system to manage their intelligence, interactions, and operations. Without it, individual agents would be isolated, struggling to remember things, plan effectively, or talk to each other.

Enhancing Agent Intelligence with Tools and Multi-Step Workflows

Sat, 23 May 2026 00:00:00 +0000

Enhancing Agent Intelligence with Tools and Multi-Step Workflows

To build truly capable AI agents, mere conversational abilities are not enough. Agents must interact with the real world, access dynamic information, and perform actions beyond generating text. This is precisely where tools become indispensable. Tools are external functions or APIs that an agent can invoke to perform specific tasks, retrieve real-time data, or integrate with other systems. Imagine an agent that can not only chat about the weather but also fetch the current weather forecast for any city.

Breaking Down Information: Smart Chunking Strategies

Fri, 20 Mar 2026 00:00:00 +0000

Breaking Down Information: Smart Chunking Strategies

Welcome back, future Context Engineering expert! In our previous chapters, we’ve explored the critical concept of the LLM context window and the art of designing and structuring information to fit within it. We’ve learned that feeding the right information to an LLM is paramount for high-quality, relevant outputs.

But what happens when your source material – a massive legal document, a comprehensive research paper, or an entire codebase – far exceeds the LLM’s context window? That’s where chunking comes into play!

Real-time Agent Progress and User Control UI

Sun, 24 May 2026 00:00:00 +0000

Interacting with AI agents can often feel like giving a command to a black box. You trigger a task, wait, and eventually, an output appears. For a multi-agent system like Kanbots, this lack of transparency can lead to frustration and inefficiency. This chapter addresses that challenge by equipping our Kanbots application with real-time feedback and user controls.

By the end of this milestone, your Kanbots application will provide a dynamic interface that displays agent progress, streams logs, and allows users to pause, resume, or cancel agent tasks directly from the Kanban board. This dramatically improves the user experience, giving operators crucial insights and control over complex AI workflows.

Containerizing Your ADK Agent for Portability and Scalability

Sat, 23 May 2026 00:00:00 +0000

Packaging your AI agent into a portable, self-contained unit is a critical step towards production readiness. This chapter guides you through containerizing your Google ADK agent using Docker, transforming it from a local Python script into a deployable artifact.

By the end of this milestone, you will have a fully functional Docker image of your long-running ADK agent. This image encapsulates all its dependencies and configurations, ensuring it runs consistently across different environments, from your local machine to various cloud services. This consistency is vital for scaling, maintaining, and debugging your agent system effectively.

Dynamic Context: Prioritization & Sliding Windows for Agents

Fri, 20 Mar 2026 00:00:00 +0000

Introduction to Dynamic Context

Welcome back, fellow AI engineers! In our previous chapters, we laid the groundwork for effective context engineering. We learned how to design context, reduce its size through summarization and filtering, compress it for efficiency, and chunk it into manageable pieces. These foundational techniques are crucial, but they primarily deal with static context – information that’s prepared once and then fed to the LLM.

But what about long-running conversations, persistent agents, or applications that need to maintain a “memory” over extended periods? The fixed context window of LLMs, while growing, still presents a significant challenge. This is where dynamic context management comes into play.

Tool Marketplaces: Empowering Agents with External Abilities

Fri, 20 Mar 2026 00:00:00 +0000

Introduction to Tool Marketplaces

Welcome to Chapter 6! In our journey through advanced AI engineering, we’ve explored how AI agents are becoming the building blocks of complex systems and how orchestration engines coordinate their efforts. But what if an agent needs to do something beyond its inherent knowledge, like checking the live weather, performing a complex calculation, or interacting with a specific database? That’s where tools come into play, and Tool Marketplaces are where agents (or rather, their developers) discover and integrate these essential external abilities.

Understanding Execution Pipelines and Request Routing in MCP

Fri, 20 Mar 2026 00:00:00 +0000

Introduction

Welcome back, intrepid AI architects! In our previous chapters, we’ve explored the foundational concepts of the Model Context Protocol (MCP), from its purpose as a universal language for AI tool interaction to the intricate details of defining and registering tools using robust JSON Schemas. You’ve learned how tools declare their capabilities, making them discoverable by AI agents.

But how does an AI agent actually use a tool once it’s discovered? How does a request travel from the agent, through the MCP system, to the correct tool, and then return a meaningful response? That’s precisely what we’ll unravel in this chapter: the fascinating world of Execution Pipelines and Request Routing within MCP.

Robust Testing for Long-Running Agent Workflows

Sat, 23 May 2026 00:00:00 +0000

Building a reliable, long-running AI agent that can pause, resume, and maintain its conversational context across sessions is paramount for production systems. This chapter focuses on establishing a robust testing framework to ensure our Google ADK agent’s state persistence and recovery mechanisms function flawlessly under various conditions.

By the end of this milestone, you will have implemented unit, integration, and end-to-end tests. These tests will validate the agent’s ability to save and load its state, preserve conversation history, and correctly resume complex workflows after an interruption. This rigorous testing is crucial for delivering an AI agent that users can trust not to “forget” their interactions.

Supercharging Development: VS Code and MCP Workflows

Sun, 17 May 2026 00:00:00 +0000

Supercharging Development: VS Code and MCP Workflows

Welcome back, AI agent architects! In the previous chapters, we laid the groundwork for building and running your first AIPacks, exploring the core architecture and how to integrate various AI models. You’ve likely felt the power of agentic workflows, but perhaps also the challenges of observing and debugging them. How do you peer inside an agent’s mind to understand its decisions? How can you make your development process smoother and more integrated?

Semantic Kernel: Skills, Planners, and Enterprise AI Integration

Fri, 20 Mar 2026 00:00:00 +0000

Semantic Kernel: Skills, Planners, and Enterprise AI Integration

Welcome back, AI explorers! In our journey through modern AI agent frameworks, we’ve seen how LangGraph builds state machines, AutoGen fosters conversational agents, and CrewAI empowers role-playing teams. Now, it’s time to dive into a framework designed with enterprise integration and modularity at its core: Semantic Kernel (SK).

Semantic Kernel, spearheaded by Microsoft, offers a powerful SDK for integrating Large Language Models (LLMs) with conventional programming languages like Python and C#. It helps you build intelligent applications by weaving together AI capabilities (like natural language understanding and generation) with existing business logic and external services. Think of it as a sophisticated toolkit that allows your code to think and act more intelligently by leveraging LLMs, without completely reinventing your application architecture.

Deploying and Monitoring Your Production ADK Agent on Google Cloud

Sat, 23 May 2026 00:00:00 +0000

This chapter marks a critical transition: moving your sophisticated, context-aware ADK agent from a local development environment to a production-grade cloud platform. We’ll focus on deploying the containerized agent built in the previous chapter to Google Cloud Run, a fully managed serverless platform. Beyond deployment, we’ll establish essential operational capabilities, including secure secret management, robust logging, and foundational monitoring.

By the end of this chapter, you will have a live, accessible ADK agent running on Google Cloud, capable of persisting its state and conversational context, ready to serve users reliably. This milestone is about making your agent resilient, scalable, and observable in a real-world environment.

AI-Native IDEs: Supercharging Your Development Workflow

Fri, 20 Mar 2026 00:00:00 +0000

Introduction

Welcome to Chapter 8! So far in our journey, we’ve explored the fascinating worlds of AI workflow languages, agent operating systems, and AI orchestration engines. We’ve seen how these components empower AI systems to tackle increasingly complex tasks. But what about the developers building these sophisticated systems? How can AI empower us to be more productive, write better code, and manage intricate projects with greater ease?

Enter AI-Native IDEs. These aren’t just IDEs with a few AI plugins; they are integrated development environments fundamentally redesigned to embed AI capabilities at their core. Imagine an IDE that doesn’t just autocomplete your code but truly understands your intent, helps debug complex multi-agent interactions, and even assists with project planning and refactoring. This chapter will dive deep into what AI-Native IDEs are, their core features, how they work, and how they are poised to revolutionize the software development workflow for AI engineers and beyond.

Context Control and Large Codebases: Managing Agent Memory

Sun, 17 May 2026 00:00:00 +0000

Introduction: The Agent’s Memory Challenge

Imagine trying to have a productive conversation with someone who constantly forgets what you just said or only remembers a tiny fragment of your shared history. Frustrating, right? This is the core challenge AI agents face: managing their “memory” or, more technically, their context. For an AI agent to perform complex tasks, especially within a sprawling project like a large codebase, it needs to access and process relevant information efficiently without getting overwhelmed.

Hands-On Project: Building a Collaborative AI Assistant

Fri, 20 Mar 2026 00:00:00 +0000

Hands-On Project: Building a Collaborative AI Assistant

Welcome to a truly exciting chapter where we turn theory into practice! In our previous discussions, we’ve explored the foundational concepts of AI workflow languages, agent operating systems, and orchestration engines. Now, it’s time to get our hands dirty and build a simplified, yet insightful, collaborative AI assistant that brings these ideas to life.

In this chapter, you’ll embark on a hands-on journey to create a system where multiple AI agents work together to achieve a complex goal: researching a specific topic and generating a concise summary. This project will solidify your understanding of multi-agent collaboration, tool integration, and basic orchestration, preparing you for more advanced frameworks like OpenFang and ChatDev. Get ready to write some code and see your agents in action!

Advanced Agent Architectures and Design Patterns

Fri, 20 Mar 2026 00:00:00 +0000

Introduction to Advanced Agent Architectures

Welcome to Chapter 10! In our previous chapters, we’ve explored the fundamentals of AI agents, their ability to use tools, and how basic workflows can be constructed. We’ve seen how a single LLM, augmented with external tools, can tackle impressive tasks. However, as the complexity of our AI applications grows, relying on a single, monolithic agent or simple sequential chains often hits limits. We need ways to manage state, coordinate complex behaviors, and build systems that are robust, scalable, and truly intelligent.

Ensuring Reliability: Testing, Evaluation, and Observability for Agents

Fri, 20 Mar 2026 00:00:00 +0000

Introduction to Agent Reliability

Welcome back, intrepid AI engineers! In the previous chapters, we’ve explored the exciting landscape of AI workflow languages, agent operating systems, orchestration engines, and the tools that empower them. You’ve learned how to design sophisticated multi-agent systems that can tackle complex problems. But as with any advanced software system, building it is only half the battle. The other, equally crucial half is ensuring it works reliably, predictably, and safely.

Chapter 11: Cost, Latency & Optimization for AI Solutions

Fri, 16 Jan 2026 00:00:00 +0000

Chapter 11: Cost, Latency & Optimization for AI Solutions

Welcome back, future Applied AI Engineer! In our journey so far, we’ve built intelligent agents, leveraged RAG for informed responses, and orchestrated complex workflows. You’re becoming adept at making AI do things. But now, it’s time to shift our focus from “can it work?” to “can it work efficiently and affordably?” This chapter is all about transforming your powerful AI prototypes into production-ready solutions that are both fast and cost-effective.

Systems Thinking, Tradeoffs, and Architecting for AI/Agentic Workflows

Fri, 15 May 2026 00:00:00 +0000

In the journey from a simple application to a complex distributed system, we’ve explored many patterns and practices. Yet, the most powerful tool in an engineer’s arsenal isn’t a specific technology or framework—it’s a way of thinking. This chapter brings it all together, focusing on systems thinking, the art of navigating architectural tradeoffs, and how these timeless principles are more critical than ever when building the next generation of AI and agentic workflows.

Chapter 18: Experimentation, Tracking & Debugging Model Behavior

Sat, 17 Jan 2026 00:00:00 +0000

Introduction to Experimentation, Tracking & Debugging

Welcome to Chapter 18! As you’ve progressed through building increasingly complex machine learning models, you’ve likely encountered a common challenge: keeping track of what works, what doesn’t, and why. Developing sophisticated AI/ML systems isn’t a linear process; it’s an iterative cycle of trying ideas, training models, evaluating performance, and refining your approach. Without a structured way to manage this chaos, you can quickly get lost in a sea of forgotten hyperparameters, untracked metrics, and unreproducible results.

The AI Systems Engineer's Playbook: Mastering Production AI in 2026

Sat, 11 Apr 2026 00:00:00 +0000

Introduction: The AI Systems Engineer’s Imperative in 2026

Welcome to 2026! The landscape of Artificial Intelligence has evolved dramatically. We’ve moved beyond the hype of experimental models to a world where AI is deeply embedded in critical business operations. As an AI Systems Engineer, your role is no longer just about training models; it’s about building, deploying, and maintaining robust, scalable, and reliable AI systems that deliver real-world value.

This shift demands a comprehensive understanding of the entire machine learning lifecycle, from data ingestion to live system monitoring. This guide, drawing from real-world production experience, will equip you with the insights and best practices needed to thrive in this demanding, yet incredibly rewarding, field. We’ll explore the latest trends, tackle common production challenges, and outline the essential skills for mastering AI systems engineering in 2026.

Emerging AI Engineering: Agents, Orchestration, and AI-Native Systems

Fri, 20 Mar 2026 00:00:00 +0000

Welcome! This guide is designed to help you understand and navigate the rapidly evolving landscape of advanced AI engineering. We’re moving beyond building individual machine learning models to creating complex, collaborative AI systems. If you’re an AI engineer, developer, or a technical professional looking to grasp the future of AI development, you’re in the right place.

What is Emerging AI Engineering About?

At its heart, this field is about building intelligent systems that can perform complex tasks autonomously, often by combining the strengths of multiple specialized AI components. Think of it as moving from having a single smart tool to building an entire workshop where different intelligent tools collaborate seamlessly.