AI Agents That Actually Work: Anthropic’s Playbook
Everyone’s talking about AI agents, but understanding how to make them work is another story. Many feel the hype doesn’t match reality. Fortunately, Anthropic, the company behind the powerful large language model (LLM) Claude, has released its playbook for building effective AI agents, based on the experiences of successful teams. Here’s what they’ve learned.
What Actually is an AI Agent?
An AI agent is essentially an automated system that can process information, make decisions, and take actions based on inputs. Unlike traditional workflows that adhere to a rigid set of rules, AI agents are designed to adapt to changing information and utilize external tools to achieve their goals. These agents operate within platforms like OpenAI and Anthropic and can be customized for various tasks, from customer support to content creation.
What’s Actually Working With AI Agents Right Now
Anthropic’s insights reveal several key strategies:
Pick the Right Setup
Teams succeed when they align their approach with the task at hand. Anthropic emphasizes that “workflows offer predictability for well-defined tasks, whereas agents shine when flexibility and model-driven decision-making are needed at scale.”
- Workflows: Ideal for tasks with a predictable, structured process, like generating social media posts based on a formula.
- AI Agents: Better suited for tasks requiring adaptability and flexible thinking, such as analyzing complex course feedback where nuanced understanding is important. Think of workflows as automating established rules, while agents make decisions more dynamically.
Chain Your Tasks Together
For quickly creating content, Anthropic’s teams use “prompt chaining” to break tasks into clear, sequential steps. For instance:
- Ask the agent to generate an outline.
- Review the outline to ensure it meets your requirements.
- Use the outline to write the full piece.
Anthropic explains that “prompt chaining is ideal when tasks can be cleanly broken into fixed subtasks.” This approach ensures each step builds upon the last, which is important for maintaining quality. By chaining tasks, you can efficiently split responsibilities: one agent writes the initial draft, another checks for brand tone, and a third schedules the content.
Split the Work
Running multiple agents in parallel often outperforms a single agent attempting to handle everything. Anthropic found that “LLMs perform better when each consideration is handled by a separate call,” meaning separate instruction. For example, one agent can write an email while another checks that its tone aligns with your branding. Treat your agent team like a group of virtual assistants, each with their own specialized expertise. This allows for the implementation of guardrails where one model processes content while another examines it for any issues. Using more agents increases confidence in the final output.
Use an Orchestrator
For larger, more complex tasks, an orchestrator agent can oversee the process. Anthropic’s teams use an “orchestrator-workers workflow” where one agent breaks down the larger assignment while others tackle individual portions. The orchestrator, perfect for “tasks where you can’t predict the subtasks needed,” identifies tasks, assigns work, and then integrates the results. It functions like a project manager by delegating roles to other agents and ensuring proper execution. This design increases efficiency and ensures complex tasks, such as coding, are handled properly.
Test Properly or Fail
Testing is crucial to ensure effective agent performance. According to Anthropic, “extensive testing in sandboxed environments” is vital. Test as many scenarios as possible before deploying your agents in a live environment. For example, let agents brainstorm numerous titles before settling on the perfect one. Effective teams, Anthropic noted, “spent more time optimizing tools than the overall prompt.” Build clear instructions, conduct thorough testing, and resolve issues proactively.
Use the Right Tools
Your agent is only as good as the tools it has access to. As Anthropic advises, when using or creating tools, “put yourself in the model’s shoes.” Ensure each tool’s purpose and usage are clearly defined, much like instructions for a new team member. These tools can include external software integrations, APIs, databases, or even other AI models. For instance, an agent retrieving information might use a search API or a knowledge database. To prevent mistakes, Anthropic recommends using specific filepaths with tools instead of relative paths. This small tweak can make a difference to performance.
Evaluate and Optimize
Smart teams use an “evaluator-optimizer workflow,” featuring one agent for content creation and another for providing constructive criticism. Anthropic found this setup best “when LLM responses can be demonstrably improved when a human articulates their feedback.” This approach highlights the importance of feedback loops. When a human gives clear feedback, such as “this response is too formal” or “this summary misses a key point,” the agent can improve and refine its outputs continuously. Structuring feedback leads to more useful improvements. Allow your agents to iterate and improve. Integrate feedback loops to gradually raise quality.
Keep Control of Costs
Anthropic warns that “the autonomous nature of agents means higher costs.” To maintain profitability, set clear limits and checkpoints. Grant your agent a maximum number of attempts for a task. Require periodic reviews to track progress. You can also implement spending limits. Platforms like OpenAI allow budget constraints for agents, preventing overconsumption. Their teams “maintain control” by defining specific stopping points or when agents should request help, ensuring your AI doesn’t drain your budget.
Make Your Move Today: Understanding AI Agents
Want to create AI agents that really work? Start with a single task and break it into clear, sequential steps. Build an agent for each microtask, then test extensively.
Teams succeeding with AI agents recognize that success is not about chasing the latest buzzwords. They follow a practical playbook: They choose the proper setup, chain tasks, distribute work strategically, and use orchestrators to direct complex jobs. Building effective AI agents is within reach; it’s about following best practices, testing, and iterating.
