[START HERE If You Are New or Prefer a More Structured Learning Path]

This page is a short guide to the Agenteer AI learning articles library. It offers a few starting points based on how you think about agents:

• As a business or product leader focused on strategy and outcomes.
• As an engineer or technical builder focused on systems and implementation.
• As someone who wants to explore specific topics like search, cybersecurity, or blockchain.

If you only have time for a couple of articles, go for these two (one Business and one Engineer

• Why Voice AI Agents Are No Longer Optional: A Practical Guide for Small Business Owners
  Where the Voice AI technology stands today, what missed calls cost your industry, what voice AI makes possible beyond just answering the phone, and what separates successful implementations from failed ones.
• AI Agent Engineering: Building Agentic Systems for Enduring Value (deeper dive)
  A 35‑minute deep dive on how to design agentic systems with enduring value, across short‑, medium‑, and long‑term horizons. Use this when you are ready for a more comprehensive mental model.

Otherwise, use the path that feels closest to your current questions. You can always come back and try another one later.

For business & product leaders

If you’re responsible for strategy, products, or teams—and you want a solid understanding of AI agents without writing code—this path gives you a recommended sequence.

1. Why Voice AI Agents Are No Longer Optional: A Practical Guide for Small Business Owners
   Where the Voice AI technology stands today, what missed calls cost your industry, what voice AI makes possible beyond just answering the phone, and what separates successful implementations from failed ones.
2. Exploring AI-Native vs. AI-Augmented Business Models in the GenAI Era
   See how AI changes business models, pricing, and value creation. This article helps you distinguish when AI is a supportive layer versus the core of the product.
3. Voice First: The Next Paradigm Shift is Coming
   Understand where interfaces are heading as agents and voice become more natural entry points. This frames how AI agents will actually show up for your customers.
4. Behind the Curtain of Generative AI: What are GPT Large Language Models (LLMs) and How Are They Created?
   Get a clear mental model of how modern LLMs are trained, what they can and cannot do, and why they behave the way they do.
5. Harnessing the Power of Generative AI: A Primer to the Action-Brain-Context (ABC) Framework for LLMs
   Learn the Action–Brain–Context (ABC) framework that underpins how you design AI systems and agents, instead of thinking in terms of “magic black boxes.”
6. AI Agent Engineering: Building Agentic Systems for Enduring Value (deeper dive)
   A 35‑minute deep dive on how to design agentic systems with enduring value, across short‑, medium‑, and long‑term horizons. Use this when you are ready for a more comprehensive mental model.

For engineers & technical builders

If you design or build systems and want to understand how to actually work with agents, this sequence goes deeper into models, frameworks, and architecture.

1. AI Agent Engineering: Building Agentic Systems for Enduring Value (deeper dive)
   Use this as the canonical reference while you design or refactor production systems. It connects the earlier pieces into a coherent architecture and time‑horizon view.
2. Behind the Curtain of Generative AI: What are GPT Large Language Models (LLMs) and How Are They Created?
   Get a clear mental model of how modern LLMs are trained, what they can and cannot do, and why they behave the way they do.
3. Harnessing the Power of Generative AI: A Primer to the Action-Brain-Context (ABC) Framework for LLMs
   Learn the Action–Brain–Context (ABC) framework that underpins how you design AI systems and agents, instead of thinking in terms of “magic black boxes.”
4. Takeaways on Building Effective Agents
   Learn practical lessons and pitfalls from Anthropic’s work on agents, including failure modes, evaluation, and guardrails.
5. Can Large Language Models (LLMs) Reason and Plan? Exploring the LLM-Modulo Framework
   Go deeper into the limits of LLM reasoning and planning, and how to design compositions and scaffolding around those limits.
6. Demystifying the Model Context Protocol (MCP) – and How It Complements AI Agent Frameworks
   Understand how MCP sits beneath agent frameworks and what it unlocks for tools, data access, and orchestration.
7. MarketMind Agent Tutorial (Python)
   Apply the ideas in real code by building a basic financial assistant agent in Python. You will wire data sources, reasoning, and actions into a working agent.

Explore by topic

After you’ve followed one of the paths above, you can move into specific themes as they become relevant. These articles are easier to absorb once you already have the basics.

Claude Code and similar Agentic AI Tools

• The Two Context Bloat Problems Every AI Agent Builder Must Understand
  Understand the sources and solutions to the two context bloat problems is crucial for improving your agent performance.
• Unlock Claude Code's Power through the Capability Lifecycle
  The Capability Lifecycle is a mental model that clarifies the relationships of seemingly confusing Claude Code building blocks and help you get the most out of these types of agentic AI tools.
• Ralph Wiggum Loop: The Two Architectures You Need to Understand
  Ralph Wiggum Loop lets your AI agent keep crunching for hours. Learn the two dimensions—context and enforcement—that determine which approach fits your task.

Search & answer engines

• Seven Takeaways from Lex Fridman Podcast with Aravind Srinivas, CEO of Perplexity AI
  A focused look at how Perplexity’s answer engine approach is reshaping search and retrieval, and what that implies for agents and knowledge workflows.

Security

• Generative AI: A Blessing or a Curse for Cybersecurity?
  A realistic view of how generative models change the security landscape—for both attackers and defenders—and what leaders and builders should watch.

Blockchain & data

• Can ChatGPT Unlock Blockchain Data for the Masses? Early Insights from Building ChatWeb3
  Lessons from applying LLMs to complex, structured data (on‑chain activity), and what this pattern suggests for other agentic data interfaces.

What to do next

• Follow one complete path (leaders or builders) from top to bottom.
• Then pick one or two Explore by topic pieces that match your current work.
• Or feel free to explore on your own!

You can return to this guide any time when new articles or tutorials are added; it will always reflect the current recommended paths through the library.