Example: Planning with Our Assistant › Bringing It All Together

This example shows how planning lets our assistant handle real work instead of only answering questions. The key elements that made this possible:

Task Decomposition: Breaking "book a meeting and send a summary" into eight concrete steps made the complex request manageable. Without decomposition, the assistant might have tried to do everything at once or missed important steps.

Sequential Execution: Following the plan in order, with each step building on the previous ones, ensured nothing was forgotten or done out of sequence. The assistant knew to check calendars before scheduling and to send the summary before creating the meeting invite.

Tool Orchestration: The assistant coordinated multiple tools (calendar, memory, email) to accomplish different parts of the task. Each tool served a specific purpose, and the assistant knew when to invoke each one.

Contextual Awareness: Throughout execution, the assistant maintained awareness of the overall goal and how each step contributed to it. This prevented it from getting lost in the details or forgetting why it was performing certain actions.

Graceful Handling: When problems arise, the assistant can recognize them and adapt rather than blindly continuing. This makes the difference between a brittle system that breaks easily and a robust one that handles real-world messiness.

As you build planning capabilities into your own agents, consider these trade-offs:

Plan Detail vs. Flexibility: Highly detailed plans are easier to execute but harder to adapt when things change. More abstract plans are flexible but require more reasoning at each step. For our assistant, we chose medium-detail plans that specify what to do but allow some flexibility in how.

Upfront Planning vs. Incremental Planning: Should the agent plan everything before starting, or plan a few steps and then replan? Upfront planning works well for predictable tasks. Incremental planning handles uncertainty better but adds overhead. Our example used upfront planning because meeting scheduling is fairly predictable.

Error Recovery Strategies: When a step fails, should the agent retry, skip it, ask for help, or abort entirely? The right choice depends on the consequences of failure. For our assistant, we chose to ask for help when critical information is missing, since sending incomplete information could damage professional relationships.

You now have an assistant that doesn't just respond to requests but actively works to accomplish goals. In the next chapter, we'll explore how multiple agents can work together, enabling even more sophisticated capabilities through collaboration and specialization.