How To Build AI Agents That Actually Work (Anthropic's Rules Revealed) (2025)

Everyone's talking about AI agents but most can't explain how they actually work. A friend texted me saying "I feel like nobody uses agents the way they're being hyped." She's right. The excitement doesn't match reality.

Anthropic, the company that created the powerful large language model (LLM), Claude, recently released its playbook for building AI agents that work, drawn from dozens of successful teams. They've seen what succeeds and what fails in the real world. Here's what they found.

What actually is an AI agent?

An AI agent is an automated system that can process information, make decisions, and take actions based on inputs. Unlike simple workflows that follow a strict set of rules, AI agents can adapt to changing information and use external tools to achieve their goal.

These agents operate within platforms like OpenAI and Anthropic and can be customized for specific tasks, from handling customer support to generating content.

What's actually working with AI agents right now

Pick the right setup

Anthropic found that teams succeed when they match the right approach to their task. They say "workflows offer predictability for well-defined tasks, whereas agents shine when flexibility and model-driven decision-making are needed at scale."

What does this mean? If you're writing social posts that follow a formula, use a workflow. If you're analyzing course feedback that needs flexible thinking, use an agent. Don't overcomplicate what could be simple. Think of workflows as rule-based automation, while agents make decisions dynamically.

Chain your tasks together

Want to create content fast? Anthropic's teams use "prompt chaining" to break tasks into clear steps. If you’re creating content with AI, ask for the outline first, check it meets your rules, then write the full piece. Each step builds on the last.

They explain that "prompt chaining is ideal when tasks can be cleanly broken into fixed subtasks." Your agent writes the first draft, another checks it matches your tone, a third handles scheduling. Stack the tasks so you don’t compromise on quality.

Chaining tasks means breaking a process into sequential steps, where each builds on the last. Splitting work, on the other hand, assigns distinct responsibilities to different agents, allowing for specialization. This distinction ensures tasks are handled more efficiently and accurately.

Split the work

Running multiple agents at once works better than one doing everything. Anthropic found "LLMs perform better when each consideration is handled by a separate call." By call they mean instruction. Have one agent write your email while another checks the tone matches your brand.

Think of your agent team as your mini VAs. But every one has their own specialist subject. This means one can "implement guardrails where one model processes content while another screens for issues." More agents, more confidence in your output.

Use an orchestrator

Bigger tasks need a leader. Anthropic's teams use what they call an "orchestrator-workers workflow" where one agent breaks down the task and others handle specific parts. Perfect for "tasks where you can't predict the subtasks needed."

The orchestrator agent spots what needs doing, assigns the work, then brings it all together. It acts like a project manager, delegating responsibilities to other agents and ensuring everything is completed correctly. This structure improves efficiency and ensures complex tasks are handled properly.

Test properly or fail

Skip testing and your agent will mess up. Anthropic is clear: "extensive testing in sandboxed environments" is essential. Test every scenario you can think of before letting your agent loose on real work. Let them brainstorm 100 titles before you refine your rules for the perfect one.

Anthropic said effective teams "spent more time optimizing tools than the overall prompt." Build clear instructions, test relentlessly, fix the issues before they happen in a live scenario.

Use the right tools

Your agent is only as good as the tools you give it. Anthropic says "put yourself in the model's shoes" when using or creating tools. Think like you're writing instructions for a new team member. Make it obvious what each tool does and when to use it.

What do we mean by tools? These can be external software integrations, APIs, databases, or even other AI models. For example, if an agent needs to retrieve information, it may use a search API or a knowledge database. The key is ensuring the AI interacts with the right tools in the right way.

The Anthropic teams found "the model would make mistakes with tools using relative filepaths." So they changed every tool to need exact locations. Errors disappeared overnight. Small tweaks make big differences.

Evaluate and optimize

Smart teams use what Anthropic calls an "evaluator-optimizer workflow." One agent creates while another critiques. They say it works best "when LLM responses can be demonstrably improved when a human articulates their feedback."

This means that feedback loops are critical. When a human provides clear feedback, such as "this response is too formal" or "this summary misses a key point," the agent can adjust and refine its outputs over time. The more structured the feedback, the more useful it becomes for improving performance.

Let your agents iterate and improve. Build feedback loops into your process. Watch the quality level up with each round.

Keep control of costs

Anthropic warns that "the autonomous nature of agents means higher costs." Stay profitable by setting clear stopping points. Give your agent a maximum number of tries. Build in checkpoints where you review progress.

You can also set spend limits. Platforms like OpenAI let you define budget constraints for agents, ensuring they don’t overconsume resources. Their teams "maintain control" by defining exactly when agents should stop or ask for help. Don't let your AI run wild with your budget.

Make your move today: understanding AI agents

Want AI agents that actually deliver? Start with one task. Break it into clear steps. Build an agent to handle each microtask, then test extensively. Build up from there.

The teams winning with AI agents aren't chasing the latest hype and letting the terminology get them down. They're following this playbook and getting real results. They pick the right setup, chain tasks together, split the work smartly, and use orchestrators to manage complex jobs.

People no smarter than you are building AI agents right now. They're testing, refining, and scaling what works. Time to join them and build something that delivers.