MAE is how we speed up software development with AI agents. Agents write the code, and a senior engineer approves the plan and reviews every feature.
Sound familiar?
Your team codes with Cursor, Copilot or Claude Code. Features ship sooner, but no one fully understands the new code.
It was built fast, maybe vibe-coded, and now it needs proper auth, payments and code that holds up.
The roadmap keeps growing and the board expects AI to speed things up, but there's no budget for new people.
Work passes from frontend to backend to QA and back, and a lot of it gets redone.
A funding round or a competitor's release sets the deadline, and you need something working in weeks.
It took months and budget without a clear result, so you won't sign up for another open-ended build.
WHAT DOES MAE MEAN?
Managed Agentic Engineering (MAE) is OTAKOYI's delivery model for building software with AI agents — under senior human control, from spec to merged code.
One senior full-stack engineer leads a pod of AI agents, each with a single fixed role. Nothing ships without a human-approved plan and a human-reviewed diff.
in calendar time, vs a classic 4 FTE team of the same scale
per feature, vs a classic team of the same scale
The MAE Workflow, Step by Step
The task is written down with clear acceptance criteria before work starts. This becomes the single source of truth both the agent and the engineer build against – no drift between what was asked and what gets shipped.
The agent proposes the architecture and approach; the engineer reviews it before the first line is written. The agent works from a curated stack and provider-supplied skills.
A mandatory checkpoint. Nothing runs without confirmation. It's the same review a senior engineer would do on any pull request – just moved earlier, before code exists to second-guess.
Module by module, with a review at each step. While one module builds, the engineer is already planning the next – two timelines run in parallel instead of one waiting on the other.
The engineer tests each module against the original specification. Documentation, specs, diagrams, runbooks – updates automatically with it, so it never drifts from what actually shipped.
Docs ship in the same pull request as the code — specs, diagrams and runbooks reviewed alongside it, not filed afterwards. The branch then merges and deploys through the standard pipeline, so production and its documentation leave together.
The Real Reason This Works
The agent does not invent how to use a database or a framework — it follows a prepared skill for that specific tool. The usual LLM mistakes on a random stack are eliminated at the source.
One typed schema from database to UI. Frontend and backend do not agree a data format separately — the compiler checks it. Zero sync meetings, zero drift between the documented and the real API.
Agent skills are versioned in git and reviewed like any other code, instead of staying one-off prompt experiments.
While the agent executes one task, the engineer is already planning the next. Two streams run at once instead of waiting in sequence.
THE STACK OUR AGENTS KNOW
TYPESCRIPT
REACT
NEXT.JS
TAILWIND CSS
The MAETON Methodology
MAE is the methodology. MAETON (MAE + skeleton) is how it starts on day one: a ready technical foundation on OTAKOYI's canonical stack, with auth, multi-tenancy, billing, queues, notifications, file storage and a basic admin panel already built in.
A new project therefore starts roughly 70% complete at the foundation level. You pay to build what makes your product different, not to reassemble authentication and billing that have been built and proven already.
MAE Is Not Another Specification Format
Tools exist that help structure technical specifications for AI agents. They solve a narrower problem: how to write requirements so an agent understands them better.
MAE is not a document format. It is the infrastructure that format lives in. A specification alone drifts from reality like any other document; what keeps it accurate is everything around it:
What does This Mean for You?
Architecture is approved before code is written, not after. Expensive rework is caught at the plan stage instead of weeks into development.
Every project is led by a senior full-stack engineer who makes the architectural decisions - not a prompt operator.
A classic two-week sprint compresses to a few days, because the agent takes over execution, not judgement.
Architecture notes and docs are updated in the same pull request as the code, so they are current by definition.
Every key step passes an engineer's review before anything moves forward.
Pod vs Classic Team
The lean staffing model — a senior engineer and product owner, backed by AI agents that handle QA. Agent speed, with a human at every checkpoint — built for projects that move fast without a five-person team.
Total headcount
2.0 FTE
The traditional staffing model — a dedicated specialist in every seat: PM, tech lead, frontend, backend, and QA. More parallel capacity, for complex builds that need full human coverage across every discipline.
Total headcount
4.0 FTE
Volodymyr Hodiak
CTO
AI agents don't make a team faster on their own. Left alone, they write a lot of code that nobody on the team really understands. What speeds us up is how we work around the agent. A senior engineer approves the plan before any code gets written and checks every feature against the spec.
Every architecture decision is written down as we build. That's how a two-week sprint fits into a few days, and the code stays clear enough for any team to take over, yours included.
Where Agentic Development Actually Delivers
Definition of success
The task has a measurable success criterion agreed before the start
Shape of the work
The work splits into small self-contained modules, each verifiable on its own
Your involvement
You are ready to invest time in Discovery and to sit the review checkpoints with us
The stack
The project runs on a managed stack with an existing pattern library, like MAETON
Definition of success
The task is research by nature and success cannot be stated as a threshold in advance
Shape of the work
The decision needs an architectural trade-off that calls for human judgement
Your involvement
You expect full autonomy without review. Production quality needs human input
The stack
The stack is off the pattern library, speed is spent learning the codebase over building the feature
Where does MAE Fit in Your Journey?
We work with clients in three engagement models, and the model decides who owns the decisions.
We diagnose and steer, the decisions and the outcome stay yours. This is where Discovery sits — a fixed-scope, fixed-price stage where we close the key risks and unknowns together and agree exactly what will be built, before development starts.
We own delivery end-to-end and hand over a finished result. This is where MAE works: how a Discovery specification becomes production code through controlled checkpoints and a gated cycle. This page is about the middle one, Build — and about how MAE keeps delivery fast without giving up decision quality.
We add capability to your team — a standing pod inside your organisation, working to the same MAE principles, with direction and backlog staying with you.
Why Discovery Closes the Risks
The most expensive mistake in an Al project is a decision made silently: the agent filled in an API contract because nobody defined it, or assumed a scope that later had to be reworked in live code. Discovery exists so those decisions are made by people, deliberately, before development starts.
Explore how we turned our clients' ideas into successful, market-ready products.
Fewer people work on each feature. A pod is 2 FTE against a classic team of 4, and a feature goes through the cycle in about a third of the calendar time. Discovery stays fixed in scope and price. After it, we price the build by milestones and show you a timeline in weeks.
The key to a successful project is a strong business idea backed by real market need, a solid tech solution, and a clear go-to-market plan.
