Artificial intelligence agents can search for information, use tools, execute task steps, and make decisions throughout the process. For this capability to function correctly, it’s not enough to simply state the desired outcome. You need to provide detailed guidance that defines what should be done, the limits to respect, and how to handle unexpected situations.
Experts at OpenAI suggest breaking down complex tasks into smaller steps, defining specific actions, and anticipating scenarios where information is missing. Anthropic also emphasizes the importance of clear instructions, sufficient context, and structures that help the agent distinguish between different parts of the task.
1. Define the goal precisely
Start by clearly stating exactly what the agent should accomplish. Instead of a vague instruction like “take care of my emails,” specify the task: identify urgent messages, separate newsletters, and prepare responses for specific types of requests. The more concrete the goal, the less room there is for differing interpretations.
2. Break down the work into steps
A lengthy task can be organized into smaller actions. For example, the instruction might specify that the agent first consults a source, then compares the data, and only then produces a report. This breakdown reduces ambiguity and makes it easier to track what needs to be done.
3. Establish what to do with incomplete information
Not every task reaches the agent with all the necessary data. Therefore, instructions should anticipate situations such as missing fields, unexpected requests, or contradictory information. You can specify that the agent should request data from the user, interrupt execution, or follow an alternative path.
4. Specify authorized actions
If the agent has access to tools, make it clear when and for what purpose each tool can be used. Ambiguous descriptions increase the likelihood of incorrect calls. Anthropic recommends clearly specifying the inputs, outputs, and functions of available tools.
5. Provide examples of the expected result
When the format of the answer is important, examples can be clearer than a lengthy explanation. They help the agent identify patterns in language, structure, and content. It’s recommended to use examples close to the real situation and include varied cases to prevent the agent from simply copying a single pattern.
6. Set clear boundaries
It’s also important to explicitly state what the agent should not do. This may include actions that require human authorization, sources that should not be used, or situations where execution needs to be interrupted. Agents operate more autonomously than a typical chatbot, making these boundaries especially important.
7. Continuously test and refine instructions
A first version will rarely solve all cases. Ideally, observe where the agent interprets an instruction differently than expected and adjust the text based on these results. OpenAI recommends an iterative approach: test, review the response, and refine the instructions as needed.
For professionals starting to work with AI agents, the skill goes beyond knowing how to write good prompts. It’s necessary to transform business goals into clear instructions, anticipate exceptions, and evaluate whether the agent actually performs the expected work. As AI tools take on multi-step tasks, this type of knowledge is likely to become even more important.