📘 Awesome AI Engineering

The Full-Stack LLM Engineering Playbook.

The guides below combine slides, mind maps, and hands-on code to move from concept to implementation. For a deep dive into the research frontier, explore the 📖 LLM Technology Landscape & Evolution — a curated reading list covering the full LLM stack, from model architectures and training, fine-tuning, inference optimization, reasoning, and Agent systems.

📑 Table of Contents

📚 Introduction to AI Agents

🔑 Key Concepts

🧠 Mind Map (Key Concepts)

📥 Download High-Resolution Mind Map (.jpg)

📑 Presentation Slides

💡 Tip: Press Ctrl + Click (or Command + Click) to open in a new tab.
📥 View the "Introduction to AI Agents" Slides (PDF)
📥 Download PDF (Direct Link)

🚀 Hands-on: A Minimal ReAct Agent

👉 View the AI Agent Project in the LLMs-Lab repository on the Eric-LLMs GitHub profile.

To bridge theory with practice, I developed a modular AI Agent project that implements autonomous reasoning and task execution:

• Architecture: Utilizes a decoupled structure with dedicated directories for Agent logic, Tools, Utils, and Prompts.
• Reasoning Loop: Features an AutoGPT.py implementation using ReAct (Reasoning and Acting) logic to handle complex, multi-step goal decomposition.
• Functional Tools: Includes custom tools for deep data analysis (Excel processing via Pandas), automated communication via email, PDF-based QA interrogation (FileQATool), requirements-driven document generation (WriterTool), and dynamic script-based auditing of structured files using custom heuristics and thresholds (PythonTool).
• End-to-End Workflow: Supports real-world scenarios, such as identifying underperforming suppliers from sales records and autonomously drafting/sending notifications.

🧰 Key Frameworks & References

The following open-source projects represent the current frontier in agentic AI engineering:

These projects showcase diverse agent architectures — from developer-focused coding agents (Claude Code/claurst) to general-purpose personal assistants (OpenClaw) and self-learning agents (Hermes-Agent). Studying their design decisions is valuable for building your own agent systems.

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📚 Building LLMs for Production

This guide covers LLM production, from Transformer architectures to advanced techniques like RAG and Fine-Tuning. It explores frameworks like LangChain, methods to mitigate hallucinations, and optimization via quantization. Learn to build autonomous agents for real-world use.

🔑 Key Concepts

🧠 Mind Map (Key Concepts)

📥 Download High-Resolution Mind Map (.jpg)

📑 Presentation Slides

💡 Tip: Press Ctrl + Click (or Command + Click) to open in a new tab.
📥 View the "Building LLMs for Production" Slides (PDF)
📥 Download PDF (Direct Link)

🛠️ Hands-on Lab & Examples

👉 Explore Practical LLM Implementations in the LLMs-Lab repository on the Eric-LLMs GitHub profile.

The production-grade principles discussed in this book—including Fine-Tuning, RAG optimization, LangChain, Prompt Engineering, Function-Calling, Agent, etc.—have each been researched as a standalone module, and each module features multiple project implementations.

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📚 Building High-Performance, Private AI Infrastructure for the Enterprise WIP (Work In Progress)

🔑 Key Concepts

🧠 Mind Map (Key Concepts)

📑 Presentation Slides

🛠️ Hands-on Projects and Examples

👉 WIP (Work In Progress)

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📚 Mastering the Model Context Protocol (MCP)

🔑 Key Concepts

🧠 Mind Map (Key Concepts)

📥 Download High-Resolution Mind Map (.jpg)

📑 Presentation Slides

💡 Tip: Press Ctrl + Click (or Command + Click) to open in a new tab.
📥 View the "Mastering the Model Context Protocol (MCP)" Slides (PDF)
📥 Download PDF (Direct Link)

🛠️ Hands-on Projects and Examples

👉 Explore Model Context Protocol (MCP) Projects on GitHub A curated collection of industry-standard Model Context Protocol (MCP) server implementations.

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📚 Agent Memory Part I

🔑 Key Concepts

🧠 Mind Map (Key Concepts)

📥 Download High-Resolution Mind Map (.jpg)

📑 Presentation Slides

A Blueprint for Memory in Agentic Intelligence

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📥 View the "A Blueprint for Memory in Agentic Intelligence" Slides (PDF)
📥 Download PDF (Direct Link)

Unforgettable Agents Architecting AI Memory

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📥 View the "Unforgettable Agents Architecting AI Memory" Slides (PDF)
📥 Download PDF (Direct Link)

📑 Further Reading / Resources

For a comprehensive list of papers related to Agent Memory, we highly recommend checking out:
👉 * Agent-Memory-Paper-List by Shichun-Liu.

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📚 Building Memory Modules for Agentic AI Systems

A comprehensive guide on designing memory systems for AI Agents. This document synthesizes academic surveys with practical implementation strategies, covering: * Theory: Taxonomy of agent memory (Forms, Functions, Dynamics). * Frameworks: Deep dive into Mem0, Letta (MemGPT), and LangMem. * Practice: Enterprise-grade solutions using Amazon Bedrock AgentCore

🔑 Key Concepts

🧠 Mind Map (Key Concepts)

📥 Download High-Resolution Mind Map (mindmap.png)

📑 Presentation Slides

Building Memory for Agentic AI: Theory, Frameworks, and Practice

💡 Tip: Press Ctrl + Click (or Command + Click) to open in a new tab.
📥 View Slides (PDF)
📥 Download PDF (Direct Link)

📑 Key Frameworks & Code Samples

The following frameworks and repositories are discussed in this guide, representing the current state-of-the-art in Agentic Memory:

• Mem0: A dual-layer memory framework supporting working, factual, and semantic memory types for agent state persistence.
• Letta (MemGPT): Manages infinite context by treating agents like an OS with virtual memory and recursive summarization.
• LangMem: A LangChain library that implements Semantic, Episodic, and Procedural memory integration for LangGraph agents.
• Amazon Bedrock Samples Comprehensive collection of examples for using Amazon Bedrock, including various implementations of Agentic workflows and memory patterns.

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📚 Agent Evaluation (Eval) Engineering

"In the age of Agents, your product is only as good as your ability to measure it."

Evaluating AI Agents requires a fundamental shift from simple output checks ("vibe checks") to analyzing multi-step trajectories, environment changes, and tool usage. This repository consolidates frameworks and engineering practices for moving from intuition to instrumentation.

It synthesizes industry standards from Anthropic, LangChain, and real-world engineering practices to build a robust Evaluation Harness.

🔑 Key Concepts

• The Intuition Trap: Why manual "vibe checks" fail as complexity scales.
• The Harness: Building a standardized environment for agent execution composed of Inputs, Tasks, and Graders.
• Trajectory vs. Outcome: Evaluating the journey (reasoning logs, tool calls) rather than just the destination (final answer).
• Reliability Metrics:
  • Pass@k (Creativity): Can the agent succeed at least once in k tries? (Good for brainstorming).
  • Pass^k (Reliability): Can the agent succeed every single time in k tries? (Critical for autonomous agents).
• Swiss Cheese Model: Layering defenses (Automated Evals → Human Review → Production Monitoring) to ensure reliability.

🧠 Mind Map (Framework Overview)

📥 Download High-Resolution Mind Map (mindmap.png)

📑 Presentation Slides

A Comprehensive Guide to Evaluating AI Agents Focuses on the engineering framework for testing, including the "Clean Room" methodology, reliability metrics (Pass@k), and the "Harness" architecture. It treats evaluation as a core development practice.

💡 Tip: Press Ctrl + Click (or Command + Click) to open in a new tab.
📥 View Slides (PDF)
📥 Download PDF (Direct Link)

🛠️ Key Frameworks & Code Samples

1. The Tooling Stack (Ecosystem)

Implementing a robust evaluation pipeline requires specific infrastructure. The following tools are referenced and utilized in this framework:

2. Architecture: Hybrid Agent (Fast vs. Slow)

To balance cost and performance, we implement a Hybrid Agent Architecture:

• Reactive Layer (System 1): Handles simple, direct queries (e.g., "What is the stock price?") with low latency.
• Deliberative Layer (System 2): Activated for complex planning or multi-step reasoning tasks.
• Coordination Layer: A router that classifies intent and dispatches tasks.

3. Evaluation Strategy: The "Clean Room"

To prevent "cheating" through shared state, every evaluation trial runs in a fresh container/sandbox.

• Isolation: Fresh container for every trial.
• Mocking: Simulate external APIs to control latency and deterministic outputs.
• Cleanup: Aggressive state teardown (no shared history).

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