Process, not prompts
This is not a course about prompt tricks. It teaches how to frame problems, design systems, produce clear artifacts, and run AI-assisted delivery with control.
Workalaya Academy
Course: AI-Native Engineering
Stop coding with AI. Start designing systems with it.
AI-Native Engineering is Workalaya Academy's flagship course for experienced engineers, tech leads, and architects who want to use AI effectively across projects and codebases while keeping quality, scope, and architectural control intact.
Cohort
10-15
Only 10 to 15 founding members will be selected.
Pricing
No cost
No cost for founding members.
Schedule
May 9 start
Weekend live sessions. Final time set based on cohort geography.
Signature teaching
AI-Native Engineering
Learn a framework for using coding agents without letting them create noise, drift, and debt.
Keep deterministic boundaries around agents and make them work from clear architectural position.
Build with defined patterns, governance, review, testing, and ownership at every stage.
Who this is for
Experienced engineers, tech leads, architects, and strong independent builders who want to operate at the architecture level while using AI deliberately.
Why AI-Native Engineering
AI can generate code. Engineers still have to design systems that work.
The problem right now is not that AI cannot produce code. It is that the output is noisy, quality is hard to control, scope drifts, and technical debt accumulates quickly. The opportunity is in creating deterministic boundaries around agents so they can help you move faster without eroding the system.
Bring a real project
From week three onward, you apply the method to a project that matters to you so the learning turns into specs, decisions, reviews, and real execution progress.
Human in control
You will learn where humans must stay decisive: architecture, tradeoffs, review, evaluation, testing, and release decisions. The goal is ownership, not automation theater.
Why Join
A framework for making coding agents actually work
This is for engineers who already know how to build software, but want a better way to control AI-assisted delivery. The course teaches the Workalaya process for keeping agents bounded, architecturally aware, and useful across real codebases.
In practice, that means agents can implement a bounded module, endpoint, or workflow, but they do not get to invent architecture, expand scope, or redefine the system without human review.
Use AI effectively across projects and codebases
The course teaches a repeatable way to work with coding agents so they stay useful beyond one demo, one stack, or one codebase.
Reduce noise, quality drift, and unmanaged technical debt
The problem today is not getting code generated. It is controlling quality, reducing noise, and avoiding the technical debt that appears when AI output outruns architectural judgment.
Work with clear boundaries, scope, and governance
You will learn how to keep deterministic boundaries around agents, keep them aware of their architectural position, limit their scope, and use defined patterns and governance to stay in control.
Curriculum blueprint
Six weeks. One architecture-level way of working.
You will use your own project to learn concrete concepts that matter in modern AI engineering: model and agent behavior, retrieval and tool use, system boundaries, architecture artifacts, evaluation, testing, observability, governance, and production-minded execution through the Workalaya process.
Module 01
May 9-10
The Agentic Landscape
Build a grounded mental model for modern AI engineering so you can choose the right tools, failure boundaries, and interaction patterns instead of working from hype.
Models, agents, tools, MCP-style integrations, and when each pattern fits
Context windows, retrieval, memory, latency, cost, and common failure modes
How to reason about reliability, security, and human oversight from day one
Module 02
May 16-17
The SDLC Reckoning
Rebuild the software delivery lifecycle for AI-assisted teams so speed goes up without design quality, traceability, or accountability collapsing.
What changes in planning, implementation, review, and ownership when agents write code
Spec-first delivery, task decomposition, handoff packs, and artifact-driven execution
Why stronger requirements, interfaces, and acceptance criteria matter more, not less
Module 03
May 23-24
Systems Thinking
Shape your project the way an architect would so both humans and agents can work from a clear system model rather than intuition alone.
Requirements, scoping, domain boundaries, and the right first slice
Architecture artifacts: system diagrams, interfaces, ADRs, and implementation plans
Breaking a real project into services, components, workflows, and reviewable work units
Pause
May 30-31
Gap Week
No live sessions. Use the break to refine your project scope, absorb feedback, and prepare for the execution phase.
Refine your project scope and implementation plan
Consolidate architecture work and review notes
Prepare for the final execution stretch
Module 04
June 6-7
Human in Control
Learn the control points that keep AI-assisted engineering trustworthy: review, evaluation, testing, observability, and release discipline.
Code review patterns for AI-generated changes and architecture drift detection
Testing strategy, evaluation harnesses, regression checks, and rollback thinking
Observability, traceability, and ownership boundaries for production use
Module 05
June 13-14
AI-Native Execution
Run your project through the Workalaya process so AI supports implementation inside clear boundaries while you manage sequencing, reviews, quality gates, and system integrity.
Execute against your specs with agent support across coding, testing, and documentation
Apply the Workalaya process: bounded scope, architectural position, defined patterns, and governance
Manage tradeoffs across speed, quality, cost, and maintainability on a live project
Module 06
June 20-21
Showcase and Reflection
Consolidate the operating model you built so you leave with a reusable method, not just a demo.
Present the system, the design decisions, the review process, and the execution results
Document what worked, where the agents failed, and how you corrected course
Leave with a clearer AI-native engineering method you can reuse with your team or clients
Format and outcomes
Not another AI tools course
This course is designed around process. Tools can change. Prompt fashions can change. Sound engineering judgment and repeatable system design do not.
Expected outcomes
A repeatable AI-native SDLC process you can run on future projects
Architecture artifacts for your own project: scope, interfaces, ADRs, and execution plan
A better way to use agents, retrieval, tools, and context without losing system control
A concrete review discipline covering evaluation, testing, observability, and release decisions
Founding cohort
Only 10 to 15 founding members will be selected, deliberately and by fit.
No cost for founders
The founding cohort is free while the course is refined with a strong early group.
Architecture-level focus
Built for practitioners who want to design better systems, not just code faster.
Live weekend schedule
Weekend live sessions. Final session time will be set to best accommodate the enrolled cohort across time zones, plus homework and project work.
Founding cohort
Built for engineers who want to operate at the architecture level
The founding cohort is intentionally small and free so the process can be sharpened with a strong founding group before future paid cohorts. Only 10 to 15 founding members will be admitted.
Ideal applicant
Experienced developers moving toward architectural responsibility
Engineers already using AI who want stronger design and quality discipline
Tech leads or architects who want a more deliberate way to work with AI
Builders willing to bring a real project and think deeply
FAQ
Common questions
What will I learn that I do not already know as an experienced engineer?
How to run an AI-native engineering process end to end: choose the right agent patterns, shape work into artifacts agents can execute, use retrieval and tools deliberately, and keep quality under control with evaluations, tests, and review loops.
What tools do I need?
You can use Claude Code, Cursor, Codex, or other agentic tools. The course is designed around process, not tool lock-in.
What kind of project should I bring?
Bring something real that matters to you and can be scoped for one builder over a few weeks. We will help you narrow it properly in Week 3.
Do I need prior AI experience?
No. Week 1 starts from first principles. You need software experience, not prior AI fluency.
Why is it free?
Because the founding cohort is about refining the experience with a strong early group. Future cohorts may be paid.
Apply now
Applications are open for the founding cohort
There is no cost for founding members. If you want to design software at the architecture level while AI helps implement it, this is where to start.
Program details
Live online for 6 teaching weeks starting May 9, 2026. Sessions will run on weekends, and the final live session time will be set to best accommodate the enrolled cohort across time zones, plus homework and project work.