Pragmatic AI Labs

Agentic Patterns: Parallel Subagent ML with Rust

Pragmatic AI Labs

Agentic Patterns: Parallel Subagent ML with Rust

Noah Gift

Instructor: Noah Gift

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
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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There are 5 modules in this course

You see why starting a team of agents is cheap and trusting what comes back is the expensive part, and you meet the rule the whole course follows: what a worker says about its own work is a claim, not a result. You then learn the two numbers that decide whether fanning out pays: the serial fraction of a job, which no number of workers can lower, and the cost of merging their outputs back into one result.

What's included

2 videos3 readings1 assignment

You give every worker a write set that belongs to nobody else and a deliberately capped context budget, so parallel workers neither overwrite each other nor drown in context. You then watch the contract checker PV judge three agents editing one README and refuse each planted cheat, naming the rule it broke, and see a gate decide a build-and-review workflow from its receipt. You finish with Simpson's rule ported from a textbook under contract, where a planted one-character change to a weight turns the gate red and the fix turns it green again.

What's included

4 videos4 readings1 assignment

You compare a cold worker that loads its model for every request with a resident worker that stays warm, and learn why only a latency measured in a recorded run counts. You then watch two agents share one local model server, a writer and a checker, and see that a sequential and a pipelined schedule produce identical bytes. Finally you write that workflow down as an ontology with shapes and attack it with planted mutants to see what the shapes catch and what they cannot.

What's included

3 videos6 readings1 assignment

You meet the traffic cop: one minter that hands out tickets, one reducer that merges finished work, and one inbox that is the only door between a worker's private output and shared state. You learn why one model run three times is still one reviewer, and why a quorum needs lanes from different models where any single fail blocks the result. You then watch three agents fan out in the Antigravity app, each in its own workspace, and see the line stop when one goes red so that nothing merges.

What's included

4 videos8 readings1 assignment

You learn why a red result should stop the line until someone understands it, and why a rerun that happens to pass is not a fix; five whys takes a failure past a shallow story to a mechanism you can change. You then study a release that sat stuck for over a day and the process rules that fix it, from letting one run finish to giving the release a priority lane, and close with a recap of the course's patterns.

What's included

3 videos4 readings2 assignments1 programming assignment1 ungraded lab

Instructor

Noah Gift
Pragmatic AI Labs
62 Courses12,366 learners

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