Agents

Master AI engineering with 30 essential AI systems engineering patterns. From RAG and LLM gateways to multi-agent orchestration and flow engineering, discover the definitive guide to robust LLM design patterns that bridge the gap between traditional software architecture and modern AI development.

Hi, welcome to my website! This is where I share my thoughts on things that interest me, mostly tech, philosophy, history, and math.

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If you’ve built an agent, you know that the delta between “it works on my machine” and “it works in production” can be huge. Traditional software assumes you mostly know the inputs and can define the outputs. Agents give you neither: users can say literally anything, and the space

The best agent products aren't the most flexible, they're the most opinionated. Learn why agents need fewer knobs, not more, and how to design around model intelligence spikes.

A step-by-step practical guide on building AI agents using Gemini 3 Pro, covering tool integration, context management, and best practices for creating effective and reliable agents.

A brain dump of all the ways I've been using Claude Code.

This blog has been co-authored by Hanfei Sun, Vertex AI Agent Engine, Software Engineer, and Huang Xia, Vertex AI Agent Engine, Software Engineer. TL;DR: Vertex AI Agent Engine now integrates with the Live API to enable real-time, bidirectional streaming agents. This allows for low-latency, human-like conversations using text and audio. This post demonstrates how to quickly build a streaming agent with the Agent Development Kit (ADK), leveraging a fully managed, serverless platform that hand...

Setting up the coding agent rust project with Gemini API

As AI agents powered by Large Language Models (LLMs) become increasingly versatile and capable of addressing a broad spectrum of tasks, ensuring their security has become a critical challenge. Among the most pressing threats are prompt injection attacks, which exploit the agent's resilience on natural language inputs -- an especially dangerous threat when agents are granted tool access or handle sensitive information. In this work, we propose a set of principled design patterns for building AI agents with provable resistance to prompt injection. We systematically analyze these patterns, discuss their trade-offs in terms of utility and security, and illustrate their real-world applicability through a series of case studies.

We envision a new era of AI, termed agentic organization, where agents solve complex problems by working collaboratively and concurrently, enabling outcomes beyond individual intelligence. To...

A practical guide to Agent Engineering: the intersection of software, systems and security engineering.

Design robust multi-agent AI systems in Google Cloud.

Agentic Design Patterns A Hands-On Guide to Building Intelligent Systems, Antonio Gulli Table of Contents - total 424 pages = 1+2+1+1+4+9+103+61+34+114+74+5+4 11 Dedication, 1 page Acknowledgment, 2 pages [final, last read done] Foreword, 1 page [final, last read done] A Thought Leader's ...

Practical lessons from building production agentic systems

AI Engineer at ANZ Bank working on intelligent systems, LLM optimization, and scalable ML platforms.

This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design...

The crucial layers of infrastructure that make up a production grade Agentic AI system.

I rewrote my "tech writer" agent in 7 frameworks: Agno, Autogen, Google ADK, Atomic Agents, DSPy, Langgraph, and Pydantic AI. You'll NEVER guess the winners.

A complete walkthrough for building a code-generating AI agent that recommends grocery items by querying SurrealDB's HNSW vector index.

This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for re...

Frameworks for LLM Agents have been surprisingly disappointing. I want to offer some principles for building agents based on our own trial & error, and explain why some tempting ideas are actually quite bad in practice.

My step-by-step approach for building and evolving Agentic Systems that work.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? - humanlayer/12-factor-agents

A curated list of 100+ resources for building and deploying generative AI specifically focusing on helping you become a Generative AI Data Scientist with LLMs - business-science/awesome-generative-...