Agents

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.

Fall 2024

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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-...

Learn common agentic design patterns and workflows for building robust, scalable AI applications, understanding when to use each.

Contribute to philschmid/gemini-samples development by creating an account on GitHub.

How do we create LLMs that can reason? Exploring Test-Time Compute Techniques and DeepSeek-R1.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Explore the main components of what makes LLM Agents special.