How to build AI agents that actually work (Anthropic's rules revealed) SOPA Images/LightRocket via Getty Images
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.
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.
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."
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.
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.