- Understand the architecture and capabilities of agentic AI systems.
- Build agents that use tools, memory, and multi-step reasoning.
- Evaluate trade-offs in agent design: reliability, cost, and autonomy.
- What makes a system "agentic": planning, tool use, and feedback loops.
- Large language model fundamentals for developers.
- Tool/function calling and structured outputs.
- Memory patterns: in-context, external retrieval, and persistent state.
- Multi-agent coordination and orchestration frameworks.
- Prompt engineering for reliability and instruction-following.
- Observability, evaluation, and failure modes in agentic pipelines.
- Safety considerations: human-in-the-loop, scope limits, and guardrails.
- Build a tool-using agent that can query an API and summarize results.
- Implement a multi-step reasoning pipeline with error recovery.
- Add memory to an agent using a vector store or key-value store.
- Evaluate agent outputs against a set of expected behaviors.
- Working agentic application with at least two integrated tools.
- Evaluation report documenting success rates and failure cases.
- Live agent demo handling an unseen multi-step task.
- Code review focused on prompt design and tool integration.