Level 8 Complete
You now orchestrate production-grade AI workflows — sequential pipelines with progress streaming, custom agent loops with state tracking, and robust safeguards against uncontrolled behavior. From single calls to orchestrated pipelines, from prototype to robust application.
What You Learned
Section titled “What You Learned”- Workflows: Chain multiple
generateTextcalls sequentially — output of Step N becomes input of Step N+1. Each step has its own system prompt, its own role. Individually testable, individually debuggable. Token tracking across the entire pipeline. - Streaming to Frontend: Stream workflow progress to the frontend in real time with
createDataStreamandwriteData. Custom Data Parts for step updates,mergeIntoDataStreamfor the final text stream. One channel for progress and text — synchronous and in the correct order. - Custom Loop: Build your own agent loops with a while loop and messages array. Push
result.response.messagesback for context continuity. State tracking (tool call count, tools used), termination based on specific tool results instead of just step count. - Breaking the Loop: Four safeguards against uncontrolled behavior — max iterations, timeout with
AbortController, cost guard (token budget), and quality check. Partial results instead of empty results on termination.breakReasonfor monitoring and debugging.
Updated Skill Tree
Section titled “Updated Skill Tree”Boss Fight Recap
Section titled “Boss Fight Recap”In the Boss Fight you built a Multi-Step Research Pipeline — a system with a custom agent loop, break conditions, sequential workflow, and real-time streaming. Your pipeline researches autonomously, summarizes, formats — and is protected against infinite loops, timeouts, and exploding costs. This is the pattern that separates production AI systems from prototypes.
Next Level
Section titled “Next Level”Level 9: Advanced Patterns — The finale. Guardrails protect your app from harmful inputs and outputs. Model routing automatically selects the right model for each task. Multi-output compares results from different models. Production-ready patterns that make the difference between a prototype and a robust AI application.