Introduction
Context
When organizations start building AI agents, the instinct is often to jump straight to the platform and begin building. Modern agent platforms make that fast, and the pressure to show results is real. But agents that skip structured planning can experience problems in production that are entirely avoidable: data dependencies that weren't considered, governance gaps that surface only after an incident, and interaction patterns that require more oversight than anyone planned for.
Planning before building isn't a formality. It's the difference between an agent that delivers value and an agent that introduces more problems than solutions.
Module overview
This module walks you through four planning areas that together produce a complete agent solution design. You explore how to scope a business outcome and define the success metrics that prove it's working, map the data sources and workflows each agent depends on, assign interaction patterns and channels that fit both the task and the audience, and establish an identity, governance, and responsible AI posture that keeps each agent's authority in bounds from day one.
Each planning area feeds the next. The outcome you commit to determines the data quality bar. The workflow you map determines the identity and permissions each agent needs. The interaction pattern you assign informs where guardrails are most critical. By the time the design is complete, every element is traceable to a deliberate decision, not a build-time assumption.
Outcome
By the end of this module, you'll be able to plan a complete AI agent solution, from defining what it should accomplish to governing its deployment and use.