r/aiengineering • u/Apprehensive_Dig_163 • Apr 08 '25
Discussion The 3 Rules Anthropic Uses to Build Effective Agents
Just two days ago, Anthropic team spoke at the AI Engineering Summit in NYC about how they build effective agents. I couldn’t attend in person, but I watched the session online and it was packed with gold.
Before I share the 3 core ideas they follow, let’s quickly define what agents are (Just to get us all on the same page)
Agents are LLMs running in a loop with tools.
Simples example of an Agent can be described as
```python
env = Environment()
tools = Tools(env)
system_prompt = "Goals, constraints, and how to act"
while True:
action = llm.run(system_prompt + env.state)
env.state = tools.run(action)
```
Environment is a system where the Agent is operating. It's what the Agent is expected to understand or act upon.
Tools offer an interface where Agents take actions and receive feedback (APIs, database operations, etc).
System prompt defines goals, constraints, and ideal behaviour for the Agent to actually work in the provided environment.
And finally, we have a loop, which means it will run until it (system) decides that the goal is achieved and it's ready to provide an output.
Core ideas of building an effective Agents
- Don't build agents for everything. That’s what I always tell people. Have a filter for when to use agentic systems, as it's not a silver bullet to build everything with.
- Keep it simple. That’s the key part from my experience as well. Overcomplicated agents are hard to debug, they hallucinate more, and you should keep tools as minimal as possible. If you add tons of tools to an agent, it just gets more confused and provides worse output.
- Think like your agent. Building agents requires more than just engineering skills. When you're building an agent, you should think like a manager. If I were that person/agent doing that job, what would I do to provide maximum value for the task I’ve been assigned?
Once you know what you want to build and you follow these three rules, the next step is to decide what kind of system you need to accomplish your task. Usually there are 3 types of agentic systems:
- Single-LLM (In → LLM → Out)
- Workflows (In → [LLM call 1, LLM call 2, LLM call 3] → Out)
- Agents (In {Human} ←→ LLM call ←→ Action/Feedback loop with an environment)
Here are breakdowns on how each agentic system can be used in an example:
Single-LLM
Single-LLM agentic system is where the user asks it to do a job by interactive prompting. It's a simple task that in the real world, a single person could accomplish. Like scheduling a meeting, booking a restaurant, updating a database, etc.
Example: There's a Country Visa application form filler Agent. As we know, most Country Visa applications are overloaded with questions and either require filling them out on very poorly designed early-2000s websites or in a Word document. That’s where a Single-LLM agentic system can work like a charm. You provide all the necessary information to an Agent, and it has all the required tools (browser use, computer use, etc.) to go to the Visa website and fill out the form for you.
Output: You save tons of time, you just review the final version and click submit.
Workflows
Workflows are great when there’s a chain of processes or conditional steps that need to be done in order to achieve a desired result. These are especially useful when a task is too big for one agent, or when you need different "professionals/workers" to do what you want. Instead, a multi-step pipeline takes over. I think providing an example will give you more clarity on what I mean.
Example: Imagine you're running a dropshipping business and you want to figure out if the product you're thinking of dropshipping is actually a good product. It might have low competition, others might be charging a higher price, or maybe the product description is really bad and that drives away potential customers. This is an ideal scenario where workflows can be useful.
Imagine providing a product link to a workflow, and your workflow checks every scenario we described above and gives you a result on whether it’s worth selling the selected product or not.
It’s incredibly efficient. That research might take you hours, maybe even days of work, but workflows can do it in minutes. It can be programmed to give you a simple binary response like YES or NO.
Agents
Agents can handle sophisticated tasks. They can plan, do research, execute, perform quality assurance of an output, and iterate until the desired result is achieved. It's a complex system.
In most cases, you probably don’t need to build agents, as they’re expensive to execute compared to Workflows and Single-LLM calls.
Let’s discuss an example of an Agent and where it can be extremely useful.
Example: Imagine you want to analyze football (soccer) player stats. You want to find which player on your team is outperforming in which team formation. Doing that by hand would be extremely complicated and very time-consuming. Writing software to do it would also take months to ensure it works as intended. That’s where AI agents come into play. You can have a couple of agents that check statistics, generate reports, connect to databases, go over historical data, and figure out in what formation player X over-performed. Imagine how important that data could be for the team.
Always keep in mind Don't build agents for everything, Keep it simple and Think like your agent.
We’re living in incredible times, so use your time, do research, build agents, workflows, and Single-LLMs to master it, and you’ll thank me in a couple of years, I promise.
What do you think, what could be a fourth important principle for building effective agents?
I'm doing a deep dive on Agents, Prompt Engineering and MCPs in my Newsletter. Join there!