ASPLOS '26 Tutorial: Building Agentic AI Systems with Jac

Learn to build agentic AI systems using Jac — a language designed around the 7 primitives of agentic computation. By the end of this tutorial you will have built a self-correcting, parallel research agent from scratch, one primitive at a time.

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What You Will Learn

This tutorial introduces 7 composable primitives split into two layers:

Mind primitives — what the LLM does:

Step	Primitive	What it does
1	Generate	LLM returns free text from a function signature
2	Extract	LLM returns structured, typed data — enforced by the compiler
3	Invoke	LLM calls tools, observes results, and loops (ReAct cycle)

Flow primitives — how work moves:

Step	Primitive	What it does
4	Pipe	Chain operations sequentially
5	Route	LLM picks a path from a graph of nodes
6	Loop	Repeat until a typed quality check passes
7	Spawn	Run multiple walkers in parallel and merge results

Step 8 puts all 7 together into a single self-correcting parallel research agent.

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Setup

1. Run the setup script

source setup.sh

This will:

• Install the Jac language runtime (standalone binary)
• Add jac to your PATH

2. Set your API key

You will receive an OpenAI API key at the start of the tutorial. Export it in your terminal:

export OPENAI_API_KEY="your-key-here"

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Running the Steps

Each step is a self-contained .jac file in the code/ directory:

jac run code/step1_generate.jac
jac run code/step2_extract.jac
jac run code/step3_invoke.jac
jac run code/step4_pipe.jac
jac run code/step5_route.jac
jac run code/step6_loop.jac
jac run code/step7_spawn.jac
jac run code/step8_composition.jac

Each file begins with a docstring explaining the primitive covered, what it builds on, and what to observe in the output.

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Resources

• Jac documentation: https://docs.jaseci.org
• Jaseci GitHub: https://github.com/Jaseci-Labs/jaseci
• Community Discord: https://discord.gg/6j3QNdtcN6