// Jeremy Knox — AI Operator

Build AI systems
that do useful work.

Learn to turn everyday tasks into reliable AI workflows, using the methods behind my own production systems.

Start learning free →See a working system

Free starting tracks. Build at your own pace.

Explore a workflow

What would you automate?

Pick a task. See what you give the agent, what it does, and where your judgment belongs.

Turn scattered sources into a decision brief.

Illustrative walkthrough · No AI is running

Input

Define the question and evidence.

Compare two project tools for a small engineering team. Supply their documentation and your requirements.

Question: Which tool supports our review process?
Sources: product docs + our requirements
Rule: cite every capability claim

You define the task. You check the result.

Learn to structure the workflow

Research workflow: Input. Define the question and evidence.

// Start here

Finish one useful project first.

Pick the path that matches how you work today. Both end with something concrete you can use.

Beginner8 lessons

Basic Training

Learn the vocabulary, give AI the right context, write a bounded prompt, and verify what comes back.

  • Vocabulary
  • Context
  • Prompting
  • Verification
DeliverableA real task, a clear prompt, and a checked result.
Start Basic Training Preview the first lesson
Experienced builder5 questions

Define your first agent

Choose a repeatable task. Turn it into an agent brief with a defined role, the right tools, and clear operating rules.

CLAUDE.md + SKILL.mdYour instructions and workflow, ready to adapt.

Create your starter files in the browser. No email required.

Open the generator

// A working system

Blueprint turns an idea into a decision you can act on.

Instead of generating a flattering plan, it tests whether the idea is ready to build and exposes what still needs work.

Blueprint · Public leaderboard
Full-size capture of Blueprint's public idea leaderboard

Captured from the public product. Scores are Blueprint assessments, not business results.

Inside Blueprint: the public idea leaderboard. Select to inspect the full interface.
  1. ProblemA promising idea is still too vague to execute.
  2. MechanismBlueprint assesses readiness and pressure-tests the weak spots.
  3. OutputA staged execution plan with the open questions made visible.
Open Blueprint See a production-system case study
Explore an exampleTestPilot: a useful idea still needs validation.
Public idea

AI unit test generation for VSCode.

TestPilot proposes bringing AI-generated unit tests into the editor where developers already work.

Blueprint verdictVALIDATE

67 / 100 readiness score

View the public record ↗

Idea title, score, and verdict from Blueprint’s public leaderboard, checked September 25, 2026. The walkthrough below is an illustrative follow-up, not the product’s original report.

Questions to resolve

  • Which tests do developers struggle to write?
  • Will they trust and maintain the generated tests?
  • What does this add to their existing coding assistant?

A bounded next experiment

  1. Choose one language and one test framework.
  2. Try it on a small set of real, permissioned repositories.
  3. Have maintainers review correctness, edge cases, and upkeep before expanding.

The useful output is a decision. Define what needs evidence, then make the next build small enough to test.

Assess your own idea →

// Proof in the mechanism

Systems built around the work they must carry.

Each case study opens the implementation, tradeoffs, and operating model behind the claim.

01 / Operate

One place to see what needs attention

Mission Control brings portfolio, monitoring, incidents, and system health into one operational view.

Inspect Mission Control
02 / Remember

Give every session the context it needs

Akashic Records makes project docs, lessons, decisions, and daily logs searchable across the agent stack.

Inspect Akashic Records
03 / Verify

Audit the output, not the story

The development record publishes its time windows, methodology, exclusions, and underlying proof of work.

Review the audit

// Supporting tools

Use the part that fits the job.

Jeremy KnoxJeremy Knox · Engineer & AI operator

Why I teach this

The methods behind the systems are worth sharing.

I build AI systems for work I actually need to do: research, development, and keeping an agent organization running.

The Academy makes that practice teachable. Give the system clear context. Bound the task. Check the result. Build something you can use again.

More about my approach →
// Useful AI reading

Ideas you can put to work

Graph Engineering Earns Its Complexity Tax at One Specific Moment
AI

Graph Engineering Earns Its Complexity Tax at One Specific Moment

A graph isn't more mature than a loop — it's more expensive than one. The only question worth asking is whether you're buying something with that expense, and most teams can't say what.

August 8, 20266m read
Loop vs. Graph Was Never the Real Question
AI

Loop vs. Graph Was Never the Real Question

The industry spent a month arguing about which box-and-arrow diagram to draw. The engineers who actually shipped something were asking a completely different question the whole time.

August 8, 20267m read
The Agent Loop Was Never the Problem
AI

The Agent Loop Was Never the Problem

Most teams shipping 'agentic AI' this year are running a fifteen-line while-loop with a fancy name. That's not a confession — it's the correct architecture.

August 8, 20267m read
Full archive →

// The AI Operator's Academy

Build the system. Check the work. Keep what compounds.

Start with the free track and turn one everyday task into a workflow you can trust.

Start learning free Free starting tracks. Build at your own pace.