Graph Engineering
Ten lectures and two project labs on the shape problem: why most of the edges in a multi-agent workflow are imaginary, how to write node contracts a graph can route on, when to fan work out instead of chaining it, and where durable state and topology-as-cost-model keep a graph from becoming an expensive mess.
You end withA working graph design for one real task of yours — node contracts, a route table, a join rule, a durable state schema, and a written cost breakdown — plus the judgment to know when not to build one.
Start ifA single agent loop can no longer hold your task and you need to split the work across agents.
Take this course first
- Harness Engineering. Graph Engineering assumes one agent loop already runs reliably inside a harness. A graph multiplies whatever each node does, so the rules, gates and handoff of a single run have to hold first.
The loop you learn
Map → Contract → Route → Verify
Every lesson has the same shape.
You know what you are buying before you buy it.
01Failure first
Every lecture opens on a concrete coordination failure — a stalled chain, a self-approving reviewer, a run that dies with no memory of where it was — not on an agenda.
02The mechanism
Why the failure happens structurally, and the smallest change to the graph's shape that removes it.
03Self-check
Questions inline, answers hidden until you commit to one.
04Deliverable
A named artefact — a dependency map, a contract, a route table, a state schema — you add to your own graph design before moving on.
Why it is built this way.
Three decisions that shaped every lesson.
An edge is a claim, not decoration
Every arrow in a graph diagram should mean one thing: the next node reads what the last node produced. An arrow that doesn't earn that claim is a chain you built out of habit, not dependency.
Contracts before wiring
A node without a named job, a declared input, a structured output, and a named failure state isn't ready to connect to anything. Fix the node before you fix the graph.
Shape is a cost model, not an aesthetic
Fan-out is not free parallelism — it's a spend decision. The topology you draw determines what the run costs before a single token is generated.
10 lessons and 2 labs in 6 modules.
The whole path is public, and every lesson is free to read. Saving progress and the labs are what the paid tiers unlock.
MOD 01Prologue
The hook: most of the arrows in your workflow diagram are imaginary, and that's costing you wall-clock time, not correctness.
- 01The Shape ProblemRead · 14m · concise path · 3 quiz · free · updated 2026-09-16
You end with: A dependency map of one real multi-step agent task you run today, with every arrow labeled real or assumed.
MOD 02I. Map the Real Work
Separate genuine read-dependencies from arrows you drew out of habit, then give every remaining node a contract the graph can route on.
- 02Auditing a Real Task's EdgesRead · 11m · concise path · 3 quiz · updated 2026-09-15
You end with: Your task's dependency map redrawn with every fake edge removed — only true read-dependencies remain.
- 03A Node You Can't Describe Is a Node You Can't RouteRead · 12m · concise path · 3 quiz · updated 2026-09-15
You end with: A written contract — job, input, structured output, named failure state — for every node in that map.
MOD 03II. Shape the Coordination
Classify your graph into the four shapes that cover almost everything, then decide where a fan actually needs a join.
- 04Four Shapes Cover Almost EverythingRead · 12m · concise path · 3 quiz · updated 2026-09-15
You end with: Your graph re-classified into chain, fan, router, and cycle segments — one shape per segment, named.
- 05A Join Is a Decision, Not a FormalityRead · 11m · concise path · 3 quiz · updated 2026-09-15
You end with: A join point defined for your fan-out, with a written reason it's needed and a partial-failure rule.
MOD 04III. Control and Verification
Split what the model judges from what the graph decides, then put a verifier on the edge instead of inside the generator.
- 06Let the Model Judge, Let the Graph DecideRead · 11m · concise path · 3 quiz · updated 2026-09-15
You end with: A route table that separates your classifier (model, probabilistic) from your router (table, deterministic).
- 07The Most Valuable Node Produces NothingRead · 9m · concise path · 3 quiz · updated 2026-09-15
You end with: A standalone verifier node, running in its own context, gating one generator's output in your graph.
MOD 05IV. State and Cost
Give the graph durable state that survives a crash, then price the shape you drew before you run it.
- 08State Is the Part the Diagram HidesRead · 10m · concise path · 3 quiz · updated 2026-09-15
You end with: A durable state schema for your graph: current node, completed nodes, artifact references, budget, retries.
- 09Your Shape Is Your Cost ModelRead · 10m · concise path · 3 quiz · updated 2026-09-15
You end with: A cost table for your graph: which nodes run on a cheap model, which earn a strong one, and the written reason for each.
MOD 06V. Judgment and Capstone
Decide when a graph is the wrong tool, place the five engineering layers in order, then build one for real.
- 10When a Graph Is the Wrong ToolRead · 13m · concise path · 3 quiz · updated 2026-09-15
You end with: A one-page decision note: does a specific task of yours earn a graph, or does it stay a loop, with the five layers placed in order underneath it.
- 11Build a Fan/Join GraphProject · 10m · updated 2026-09-15
You end with: A working fan/join graph — node contracts, route table, join rule — applied to a real task, plus a measured before/after against the chained version.
- 12Add a Verifier and Durable StateProject · 10m · updated 2026-09-15
You end with: That same graph extended with a standalone verifier node and a durable state file that survives a mid-run crash and resumes correctly.
Start with the free lesson.
Engineers who already run single-loop coding or research agents and are hitting tasks a single loop can't hold.
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