How We Build Effective Agents: Barry Zhang, Anthropic
“Wow, it's uh incredible to be on the same stage as uh so many people I've learned so much from. Let's get into it.” Imagine starting a presentation with that humble yet potent statement. That’s how Barry Zhang, a brilliant mind from Anthropic, kicked off a session that's rapidly becoming essential reading for anyone serious about AI. Forget the hype and dive deep into the practicalities of building truly effective AI agents.
In an increasingly AI-driven world, the ability to develop sophisticated, reliable agents isn't just an advantage—it's a necessity. Whether you’re a developer looking to optimize workflows, a business leader aiming for transformative automation, or simply curious about the cutting edge of AI, figuring out how to build agents that actually work is paramount. This article distills Barry’s invaluable insights, offering key principles for developing sophisticated agentic systems. We’ll emphasize simplicity, strategic application, and, crucially, understanding an agent's perspective to achieve optimal performance.
Get ready to unpack the core distinctions between agentic systems and mere workflows, explore a practical checklist for when to build agents, and embrace the power of the ‘Keep It Simple’ approach to agent design. Finally, we’ll dive into the surprisingly intuitive concept of 'thinking like your agent' for unparalleled results. By the end, you’ll have a clear roadmap for crafting agents that don't just exist, but excel. So, let’s get into it and unlock the secrets to building AI agents that genuinely deliver.
Table of Contents
- Agentic Systems vs. Workflows: Defining the Future of AI Automation
- When to Build an Agent: A Practical Checklist for Use Cases
- The 'Keep It Simple' Principle in Agent Design and Optimization
- Thinking Like Your Agent: Improving Performance Through Perspective
Agentic Systems vs. Workflows: Defining the Future of AI Automation
Kicking things off, let's dive into how AI automation has totally transformed, moving from basic tasks to incredibly smart systems. You know, we all started with pretty simple AI features, right? Think summarization, classification, or just extracting information. Barry Zhang from Anthropic points out that these felt like magic just a few years ago but are now totally expected.
Then, as we got more sophisticated, we realized a single model call often wasn't enough. So, we started putting together these things called workflows. What's interesting about workflows is that they orchestrate multiple model calls using predefined control flows. Basically, you're telling the AI: "Do this, then do that, then if X, do Y." It’s a structured way to get better performance, even if it means a bit more cost or latency. It's like a recipe where every step is clearly laid out.
Here's where it gets really exciting: enter agentic systems. These aren't just following a recipe; they're more like a chef who can adapt on the fly. Barry explains that unlike workflows, agents can decide their own trajectory and operate almost independently based on environment feedback. This means they're not rigidly bound by a predefined path. They can explore, react, and make decisions based on what's happening around them. It's a huge leap in AI capability, allowing for much more dynamic and intelligent behavior.
So, what's next for these incredible agents? Barry admits, "It's probably a little bit too early to name what the next phase of agentic system is going to look like especially in production." But he does share some cool ideas about where we're headed. We might see single agents become a lot more general-purpose and capable, able to handle a wider range of tasks with impressive autonomy. Or, you know what's even more fascinating? We could start witnessing collaboration and delegation in multi-agent settings. Imagine a team of AI agents working together, each handling a specific part of a complex problem and communicating to achieve a common goal. This kind of multi-agent dynamic opens up a whole new world of possibilities for tackling incredibly intricate challenges.
Ultimately, whether we're talking about general-purpose agents or collaborating teams, the trend is clear: as we give these systems a lot more agency, they become more useful and more capable. This evolution from simple features to dynamic agentic systems truly defines the future of AI automation.
When to Build an Agent: A Practical Checklist for Use Cases
So, you're thinking about building an AI agent? That's exciting! But hold on a second. As Barry Zhang from Anthropic wisely points out, you shouldn't just jump into building agents for every task. Agents aren't a drop-in upgrade for all use cases, and honestly, not every problem even needs one. It's too early to predict the "next phase of agentic systems," whether it's more general-purpose single agents or complex multi-agent collaborations. What we do know is that as agents gain more agency, the potential consequences of errors also increase significantly.
Before you dive in, consider a practical checklist to figure out when an agent truly makes sense. First off, analyze the complexity of your task. Agents really shine in ambiguous problem spaces where the decision tree isn't easily mapped out. If you can define the entire workflow explicitly, a simple, optimized workflow will be much more cost-effective and give you greater control.
Next, think about the value of your task. Building an agent, especially with all that exploration it does, can consume a lot of computational "tokens," driving up costs. If your budget for a task is just a few cents (like in a high-volume customer support system), a workflow for common scenarios will often capture most of the value without the exorbitant expense. However, if you're in a scenario where you think, "I don't care how many tokens I spend, I just want to get the task done!", then an agent might be exactly what you need.
It’s crucial to derisk critical capabilities early on, identifying potential bottlenecks before they multiply costs and latency. For instance, with a coding agent, you'd want to confirm it can write good code, debug effectively, and recover from its own mistakes. If you spot a bottleneck, simply reduce the scope, simplify the task, and try again.
Finally, a major factor is the cost of error and ease of error discovery. If an agent's mistakes are high-stakes and hard to find, you'll struggle to trust it with autonomy. While you can mitigate this with read-only access or human-in-the-loop interventions, those also limit its scalability. You want verifiable outputs.
This is exactly why coding is an ideal agent use case.
- It's an incredibly ambiguous and complex task, going from a design document to a complete pull request.
- The value of good code is exceptionally high; developers know this well.
- Coding workflows often already integrate with cloud services.
- Crucially, coding has a "really nice property" where outputs are "easily verifiable through unit tests and CI." This discoverability of errors makes it a playground for effective agents.
Once you pinpoint a suitable use case, the next step, according to Barry, is simple: "keep it as simple as possible."
The 'Keep It Simple' Principle in Agent Design and Optimization
Alright, let's talk about building effective agents, and more specifically, why keeping things super simple upfront is your secret weapon. You know, when you’re building an agent, Barry Zhang from Anthropic breaks it down into three core components: the environment it operates in, the tools it uses, and the system prompt that guides its behavior. Think of it like setting up a new virtual assistant – what world is it in, what gadgets does it have access to, and what are its main instructions?
Here's the thing: we've learned the hard way that throwing a ton of complexity at these three elements from the get-go is a huge mistake. As Barry puts it, "any complexity up front is really going to kill iteration speed." When you're trying to figure out what works, you want to be able to test ideas quickly. Keeping these foundational components simple lets you iterate fast, and that rapid testing gives you some serious ROI (Return on Investment) on your development time.
You might be thinking, "But what about all those cool optimizations?" And you're right, those are important! But not at the beginning. Optimizations like trajectory caching to save on costs, parallelizing tool calls for lower latency, or even crafting how you present the agent's progress to build user trust – these are things you layer on after you've got the basic behaviors locked down. It’s like building a house; you don’t start with the fancy landscaping before the foundation is poured, right?
Focusing on these three simple building blocks first is crucial. The environment is often dictated by your use case, so your main design decisions come down to defining the tools you're offering and the prompt you're giving your agent. You know what's interesting? Even vastly different agents, serving different product needs, can share almost the exact same underlying code if you adhere to this simple backbone principle. "We can think and talk and reason all we want," Barry notes, "but the only thing that's going to take effect in the environment are our tools." That’s why getting those tools and instructions right, simply, at the start, is paramount. Trying to optimize before you’ve established fundamental function is like putting the cart before the horse – it just won’t work. Get the basics right, then scale up.
Thinking Like Your Agent: Improving Performance Through Perspective
Have you ever wondered why your brilliant AI agent sometimes fumbles tasks that seem incredibly simple to you? It turns out, that’s a common pitfall for developers, even seasoned ones. The key to unlocking better agent performance isn't just about tweaking code; it's about shifting your perspective and thinking like your agent.
Here’s the thing: we often project our own vast cognitive abilities onto these agents, but they don't see the world the way we do. Barry Zhang from Anthropic highlights that agents operate within a very limited context window, typically around 10-20k tokens. Imagine trying to navigate a complex problem with only that much information – their "worldview" is inherently constrained. This small context means everything the model knows about its current state is packed into those tokens.
To truly grasp this, you've got to simulate the agent's experience. Barry suggests a fascinating exercise: become a "computer use agent" yourself. Picture this: you get a static screenshot and a poorly written description. "We can think and talk and reason all we want, but the only thing that's going to take effect in the environment are our tools." So, you click, effectively "closing your eyes for three to five seconds" while the agent executes the action. Then, you open them to another static screenshot, with no idea if your last action worked or, say, shut down the computer! This vivid analogy reveals just how crucial real-time, comprehensive context is for an agent. They need details like screen resolution to even click accurately, plus recommended actions and clear limitations to avoid unnecessary exploration.
Fortunately, you don't have to literally close your eyes to get this perspective. Since these systems "speak our language," you can leverage models like Claude to gain insight. Here’s how you can make a difference:
- Analyze System Prompts: Ask Claude, "Is any of this instruction ambiguous?" or "Does it make sense to you? Are you able to follow this?"
- Evaluate Tool Descriptions: Throw in your tool descriptions and see if the agent understands how to use them, or if it needs more or fewer parameters.
- Review Trajectories: A powerful technique is feeding the agent’s entire trajectory into Claude and asking, "Hey, why do you think we made this decision right here? And is there anything that we can do to help you make better decisions?"
This kind of introspection, guided by an intelligent model, won't replace your own understanding, but it will certainly "help you gain a much closer perspective on how the agent is seeing the world." Remember, as you iterate on your agents, continuously put yourself in their shoes. It's a game-changer for optimization.
Conclusion
In essence, crafting truly effective AI agents isn't about blind application, but rather strategic simplicity and empathetic design. Barry Zhang’s insights underscore the power of restraint: don't build agents for every task. Instead, intimately understand your agent's perspective, providing only the essential context and keeping complexity at bay for as long as possible. This disciplined approach – being selective, prioritizing clarity, and deeply understanding the agent's operational needs – is the bedrock for robust and reliable AI systems.
Embrace these core principles to develop agents that truly deliver value, and join us in shaping the future of AI. The journey doesn't end here; significant challenges lie ahead, from agent budgeting and self-evolving tools to advanced multi-agent communication. What are your biggest open questions or challenges when it comes to building and deploying effective AI agents in production? Let's collectively illuminate the path forward for intelligent automation.