Running several AI agents at once is easy. Trusting what comes back is not. This course takes parallel subagent work apart one seam at a time and asks, at each seam, what refuses a bad result. You will measure the serial fraction that caps every speed-up, give each agent a disjoint set of files to write, and keep one model resident behind a server that several agents share instead of loading it cold for every call. Then you build the parts that say no: a reducer that gives the same answer in every arrival order, a receipt without which no verdict is Green, a quorum in which one FAIL blocks the merge, and a ratchet whose baseline never regresses. Every demo is a Rust program with pinned inputs, machine-checked assertions and a receipt. None of this makes an agent correct. It makes a wrong result stop the line before it ships.

Agentic Patterns: Parallel Subagent ML with Rust
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Agentic Patterns: Parallel Subagent ML with Rust
This course is part of Agentic Machine Learning in Rust Specialization

Instructor: Noah Gift
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Intermediate level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Measure the serial fraction and merge cost of a job before deciding how far to fan out
Partition parallel work into disjoint write sets with small context budgets, and reduce results deterministically
Gate agent work with rerunnable checks, contracts and multi-model review, and stop the line on a red result
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Recently updated!
October 2026
Assessments
6 assignments
Taught in English
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This course is part of the Agentic Machine Learning in Rust Specialization
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