AI-Native course · agentic engineering
AI agent development course on your codebase
The AI-Native course is for developers, technical founders, and development teams. We deliver it one-to-one or for a company, online, on the participants' codebase. The program covers agent control, specifications, acceptance tests, and quality checks. Pricing depends on the codebase, technology stack, and participant count, with a concrete quote after one conversation.
A coding agent can prepare a change quickly. Specifications, acceptance tests, and quality checks show whether that change meets the requirement. The course builds this working method into your codebase.
- A 30-minute call with the engineer who would lead the work.
- We review the process, its exceptions, and its current cost.
- Within 2 business days, we send a written summary.
In short
- Price
- Quoted after a callyour quote follows one call about the repository, format, and scope
- Format
- 1:1 or a company course, onlinesessions use your own codebase
- Who teaches
- Engineers building with agents in productionthe program is based on systems we use in production
- For whom
- A dev, founder or solo buildernot for non-technical teams (see AI training)
Do not write code and want to work on documents or your own process? See one-to-one AI training.
Participant reviews
What they say after 1:1 sessions.
The reviews below come from five one-to-one sessions. The development-team format is an offer built on the same working standard.
Kuba Koziej CEO & co-founder of MoreGrowth, board member at Natu.Care
I went from barely shipping a frontend to building full applications with a backend and a database, and deploying them safely instead of hoping they would hold. We worked through the parts I kept tripping on: Docker, the coding agents and tools, and when it is worth reaching for skills, MCPs and connectors. It stayed practical the whole way, on what I actually wanted to build.
Hlib Utkin
Public administration
A large stream of documents passes my desk every week. I now review, draft and prepare them with Claude in a fraction of the time, and research that used to eat an afternoon takes minutes. It is wired into the services I already work in, so the documents reach me instead of me hunting for them.
Ivan Chepurin Senior Software Engineer, Immutable
I came in using coding agents ad hoc and left orchestrating them: loops, an agent kanban, a full development cycle that ships. What stayed with me is running context and cost on purpose, and building my own harnesses instead of waiting for a tool to ship one. It changed how I work day to day.
Oleksandr Usyk Co-founder & Art director, jakotako
I run a design studio, not an engineering team, and I still left with something working. We built an agent loop that pulls in leads and keeps an eye on the competition, and I put my own site together with Claude. It was hands-on, on my real work, not a talk about AI.
Why does agent-written code need a different control method?
Speed is easy to see, while correctness requires verification. The course adds executable specifications, acceptance tests, quality checks, and a durable decision history.
| Area | Ad hoc workflow | Controlled workflow |
|---|---|---|
| Steering | Prompts and manual correction | Defined scope and approval rules |
| Requirements | Plans drift away from the code | Requirements are checked against the implementation |
| Tests | Tests mirror the agent's assumptions | Independent acceptance checks verify behavior |
| Docs | Stale the moment the code moves | They keep up with the code |
| Context and cost | Context and model cost grow unchecked | Context and cost are measured and limited |
It is the same method our own builds run on, in production, under load. See case studies →
Which level do you work at today?
This qualitative ladder describes three ways of working with AI in a codebase. Pick the description closest to your practice to see which change in method leads toward work based on a checkable system.
01
Level 1
Exploring
Occasional use on isolated tasks
Has a ChatGPT account, pastes in code snippets, sometimes gets a useful hint. An agent in the repo? Tried once, got burned once, went back to working by hand.
02
Level 2
Transition point
Assisted development
Faster work with manual verification
Pays for the tools, writes good prompts, and the agent speeds up single tasks. But trust is manual: they still read every diff twice, because the agent gets things almost right. Scale ends at their own eyes.
03
Level 3
Where the course takes you
Controlled agent workflow
Defined scope and automated acceptance checks
The agent works within a defined scope. Acceptance tests and quality checks run before human review, so the reviewer can focus on decisions the checks cannot settle.
Moving from assisted development to a controlled agent workflow requires shared specifications, verification, and operating rules. The course establishes that method in your codebase.
AI-Native Harness
How does the AI-Native Harness work?
The harness combines control, verification and a recorded working method. The agent stays within defined boundaries, and its output is checked before it reaches a person.
Defined scope
The agent works inside the bounds you set. The result is repeatable.
- A human approves where it matters
Independent verification
Product behavior is checked from the outside with acceptance tests and quality checks.
- Plausible but incorrect results are caught before review
Documented working method
You leave with your own written method. It is repeatable on the next project, and the same method can onboard a team once you grow.
- Long projects that do not drift
What will you learn in the AI agent development course?
You learn to control an agent, write executable requirements, build acceptance tests, and finish with a pilot. Each module leaves a working asset in the codebase or in your team method.
Theory · groundwork
How agents work and where they fail, before we touch the project.
01GroundworkHow coding agents fail
Where the answer that only looks right comes from, and the most expensive failure mode: an unsupervised agent cements accidental decisions into its own tests, and every later session is afraid to touch them.
- 02Trust
Move verification into the workflow
Today trust depends on reading every change twice. We define what the agent must demonstrate in tests before its output reaches human review.
Practice · your codebase
You drill on your own code and finish with a pilot.
03ControlContext the agent can reliably load
Context discipline: a twenty-line AGENTS.md and a docs tree the agent lists at the start of every session. You document only surprises, the delta between the internet and your project, and stale plans stay out of context so the agent does not go off fixing history.
Module outcome
An AGENTS.md and a docs tree your agents load
- 04Verification
Executable acceptance criteria
Executable specifications: requirements written as given-when-then acceptance tests the agent must pass before the result reaches review. You stop checking by eye what a machine can check for you.
Module outcome
A set of executable specs for one workflow in your repo
- 05Discipline
Regression checks for long-running work
A fixed reference set, a starting score, and an experiments folder show which change actually improves the result. Failed experiments remain documented as knowledge. For tasks with a measurable goal, the agent repeats the cycle until it reaches the agreed result.
Module outcome
A fixed example set with a quality-check script for one important metric
- 06Pilot · Scale
Pilot and operating method
You close the course with a pilot done the new way, and you write the method down so it survives us leaving. The same method later onboards your team.
Module outcome
A working pilot, and the method written down so it works without us
Who is the AI agent course for?
The course is for developers, technical founders and development teams with a codebase and a goal. We fit the depth to the role, but everyone leaves with a method that stays in daily work.
A technical founder
A founder who codes and ships product themselves.
- A repeatable way to build with agents
- An agent that gets it right the first time
- What to automate first, what to keep human
- You leave with a working setup and your own method
A solo builder or small team
An indie hacker or a small team moving into agent work.
- Agent work that holds in production-shaped conditions
- Control and verification you can trust
- Context and cost kept on a budget
- A method that grows with you
Dev team lead
Your team already uses agents, each person differently, and review chokes more every week.
- Shared conventions for working with agents
- Lower review cost
- Onboarding new people with the same method
- A pilot on shared code
Formats and price
How much does the AI agent development course cost, and what are the formats?
The AI-Native course is quoted after a codebase call. We work online one-to-one or with a development team; the amount depends on scope, stack and participant count.
| Format | For whom | Scope | Price |
|---|---|---|---|
| 1:1 intensive | A founder, engineer or solo builder who keeps building after the course. | One-on-one on your repo: we stand up the method on your codebase and the way of working that holds after we leave. | Quoted after a call · net |
| Company course | A dev team moving into agent work | As many developers as you need, on your shared codebase: the same method and conventions, so the whole team works the same way and closes the course with a shared pilot. | Quoted after a call · net |
Price depends on format, participant count, stack, and scope. You receive a quote after one call about the repository and goal. The method and artifacts created during the course remain with you.
When is general AI team training the better choice?
The course is designed for people who already build software and can bring a codebase and a goal. We recommend another format before quoting when those conditions are absent.
Ready if
- You build software and want a repeatable agent harness
- You have a repository to work on, new or inherited legacy, beyond curiosity
- You want output you can trust, with specs, tests and a trace
- You will keep building after the course and own the handoff
Probably not, if
- You want it built for you: start with the free scan and our builds
- You need AI literacy for a non-technical team: see AI training
- You want a no-code promise or a certificate for the wall
- You expect production hardening, ownership and SLA from a course
Or we build it with you
Rather have it built and run in production?
The course teaches the method. Production hardening, ownership and maintenance stay a paid build. If you would rather not do it yourself, start with the free process scan, and we will tell you straight what is worth building, and whether it is worth building at all.
What being in the Claude Partner Network means.
We say plainly what this status means, and what it does not.
Responsibility for the course
- Syntalith is responsible for the course program and delivery.
- The scope, working method, and signed participant reviews provide the basis for assessing its quality.
- The course comes from Syntalith's own delivery work and engineering method.
What it means
- We are in Anthropic's partner program for Claude.
- We build production AI systems for Polish companies.
- We teach from delivery work and the behavior of agents in production systems.
How do I learn to build AI agents on my own project?
Start with a codebase, one goal and a way to verify the result. The course begins with a call about the project and stack, then builds a harness and pilot on your code.
- 01A call about your repo, stack and goal: we set the format and scope.
- 02You get a quote and a plan fitted to your codebase.
- 03Working sessions on your repository, online, building the harness.
- 04You keep the harness, the templates, the way of working and a pilot done the new way on your repo.
Questions about the course
Why pay for a course when I could figure this out myself?
You can. The course is for people who want to establish the method on a live codebase with an engineer, rather than assemble it through trial and error over several projects.I already tried agents and rewrote the code anyway. How is this different?
That is exactly what the course is for. We do not show you better prompts; we show you a way of working where the agent stays inside bounds you set and its output gets checked before you trust it. The starting point is the very code that burned you.Won't I learn something that is stale in six months?
We teach a method that survives changes in models and plugins: how to set an agent's boundaries and verify its output. You apply it to your codebase and complete a pilot, so the method is already in use when you return to the project.How much does the AI-Native course cost?
Price depends on the format, repository, stack, goal, and number of participants. We give you a fixed quote after one call about the codebase.Is this course for a beginner without a codebase?
Not as a course based only on curiosity. You need your own codebase and a working goal; if the project is only starting, the first session can set it up. Without code and a goal, one-to-one AI training on a process or documents is the better fit.We have old code nobody wants to touch. Does the course still apply?
Yes. Coding agents can help map inherited code, compare documentation with implementation, and add verification before new features are introduced. The course applies that method to your repository.Does the course work with the tool my team already uses?
Yes. We work in the tool used in your codebase. The method rests on specifications, documentation, and quality checks, so it transfers between providers. We also explain when a different tool or working method would be more suitable.A one-to-one course or a course for a development team?
Both. A 1:1 course for a founder, or a company course with as many developers as you need, on shared code and with a shared pilot at the end. We set the format with you before the quote.Does the course cover coding-assistance tools?
Yes. We can work in the tool used in your codebase. The program teaches context management, boundaries, specifications, acceptance tests, quality checks, and decision history.What do you keep afterwards?
You keep the method in your codebase, the working process and a pilot project built with it.
Next routes
Choose the right format and material
Tell us your repo and goal, and we will send you the plan and the quote.
- A 30-minute call with the engineer who would lead the work.
- A review of the processes that cost you the most time and money.
- A written summary of what to automate first and the likely cost range.
Times are shown in your own time zone. We work with clients across time zones.
The scan is free and creates no obligation. If automation is unlikely to pay off, the written recommendation will say so.
Quoted per scope · led by engineers with systems in production