AI Agent Control Logic with Pattern Matching
summary
Explains how pattern matching reduces orchestration code by using Java pattern matching examples with sealed types, guarded patterns and deconstruction to extract fields, enforces completeness, and avoids casts. Shows a concrete LangChain + OpenAI pipeline with planner → coder → reviewer, a retry loop with configurable max attempts (example max 3), a router dispatching sealed task types to translator or summarizer, and concurrent agents with a judge agent; code samples and slides are posted on the uploader’s blog.
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chapters (9)
- 0:00AI agent control logic
- 2:32Pattern matching benefits
- 5:04Pattern matching in Java
- 13:39AI agent orchestration
- 16:10AI agent pipelines
- 18:43Task delegation with pattern matching
- 20:07Retrying AI agents
- 21:31Concurrent AI agents
- 22:54Q&A
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