What gets in the way
- Agents are useful but inconsistent, especially under edge conditions.
- Tool calls and prompts are hard to govern, creating safety and quality risks.
- You are not confident the behavior will hold up across every shift.
Course
Design AI agents you'd trust with your users
You can make agents that work sometimes, but trust and consistency are still missing for real customers.
What this course helps you do
- Use schema-first tool design to reduce ambiguity and bad tool calls.
- Build failure handling, permission boundaries, and defensive prompts.
- Apply cost discipline, observability, and error recovery patterns.
Outcomes
Architect a dependable tool-calling loop with explicit I/O contracts.
Implement memory strategies without leaking sensitive context.
Build robust error handling for retries, fallbacks, and escalation.
Apply safety checks so prompt injection and unsafe outputs are minimized.
FAQ
How is this different from a prompt engineering course?
It focuses on architecture and operations: contracts, memory, tool controls, testing, and rollout risk.
Do I get a practical path to implementation?
Yes. Each lesson includes practical systems you can adapt directly into real applications.
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