Building AI agents? Here are the resources that actually taught me something useful, beyond the usual hype. If you're tired of theoretical papers and want practical insights that work in the real world, start here.
Building effective agents by Anthropic
What you'll learn: Stop overcomplicating things. The Anthropic team cuts through the noise and shows why the simplest agent often wins. They're brutally honest about what doesn't work - complex frameworks, over-engineered patterns, and agents that try to do everything. Instead, they advocate for boring solutions that actually ship: clear goals, simple tools, and relentless testing. The most valuable insight? Most "agent problems" are actually prompt engineering problems in disguise.
Why it matters: If you're building production agents, this will save you months of architectural regret.
How to Build an Agent by Amp
What you'll learn: This is the "hello world" tutorial you wish existed when you started. No fancy frameworks, no overcomplicated abstractions - just 400 lines of Python that actually work. You'll see exactly how tool calling works under the hood, why the basic loop pattern is so effective, and how to avoid the common pitfalls that break agent reliability. The code examples are clean and you can run them immediately.
Why it matters: Understanding the fundamentals prevents you from getting lost in framework complexity later.
Prompt Engineering by Lee Boonstra (Google)
What you'll learn: Finally, a prompt engineering guide that isn't just "add 'think step by step' to everything." Boonstra breaks down the mechanics of why certain prompts work and others fail spectacularly. The Chain of Thought and step-back prompting sections alone will change how you approach complex reasoning tasks. More importantly, it covers the unglamorous reality of production prompts - handling edge cases, maintaining consistency across inputs, and debugging when things go wrong.
Why it matters: Your agent is only as good as its prompts, and most people are terrible at writing them.
Agents by Julia Wiesinger et al (Google)
What you'll learn: This isn't just another "what are agents" overview. The Google team maps out the three critical components every agent needs: a capable model, well-designed tools, and orchestration that doesn't break. They explain why most agents fail at the orchestration layer - that observe-think-act cycle is harder than it looks. The business implications section is particularly good if you need to justify agent investments to skeptical stakeholders.
Why it matters: Provides the conceptual framework for thinking about agents as systems, not just fancy chatbots.
Evaluating AI agent applications by Weights & Biases
What you'll learn: The harsh truth about agent evaluation - it's nothing like evaluating traditional ML models. This guide tackles the eight biggest deployment challenges (spoiler: most involve reliability and user trust) and gives you a concrete framework for testing agents before they embarrass you in production. The real-time monitoring section is gold if you're running agents at scale.
Why it matters: You can't improve what you can't measure, and measuring agent performance is weirdly hard.
A practical guide to building agents by OpenAI
What you'll learn: OpenAI's take on agent architecture, with the benefit of seeing what works across thousands of real deployments. The progression from single-agent to multi-agent systems is particularly well thought out - they show you exactly when to add complexity and when to resist it. The new tooling sections (web search, computer use, Agents SDK) are essential if you're building on OpenAI's platform.
Why it matters: Shows you the architectural patterns that scale, based on real production experience.
Model Context Protocol Spec
What you'll learn: MCP is the missing piece for composable AI systems. Instead of every agent being a monolithic black box, MCP lets you build modular systems where different components can share context, tools, and data cleanly. The protocol design is actually elegant - JSON-RPC with clear boundaries and security considerations built in. If you're building multiple agents or want your agents to play nicely with other AI tools, this is your blueprint.
Why it matters: Standardization is coming to AI tooling, and MCP is likely to be a big part of it.
Reading order matters. Start with Anthropic's reality check, then build your first agent with Amp's tutorial. Master prompting with Google's guide before diving into architecture with the other resources. Skip around if you want, but don't skip the fundamentals - I've seen too many teams build impressive demos that fall apart in production because they never learned the basics.
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