About this article
This article is the first main entry of the Architecture Crash Course for the Generative-AI Era series. Under the heading âWhat an IT Architect Actually Doesâ, it lays out the roleâs overall picture, the domains it covers, the right mindset, and how the decision criteria are shifting in the AI era.
The series-wide framing lives in the previous article, How to Read This Book.
What is an IT architect in the first place
An IT architect is, roughly speaking, âthe person who decides the systemâs skeleton.â Like a master builder who designs a buildingâs foundation and load-bearing structure before any walls go up, the IT architect determines the fundamental technical decisions â language, cloud, structure, deployment â that shape the system for years to come. These decisions, once made, are extremely hard to reverse. In the AI era where code generation is increasingly automated, the value of âdeciding what to build and whyâ is rising, not falling.
I think of âarchitectâ as a role, not a title. Regardless of what your business card says, you can leave your name on a design decision starting tomorrow.
What is happening in the world of architecture
In 2020, an analytics platform company called Segment published a blog post titled âGoodbye Microservicesâ, declaring that the 140+ microservices they had grown into were being collapsed back into a monolith. When they originally adopted microservices in 2013, that was the textbook industry-correct answer.
Five years later, the same choice was treated as technical debt. That is the world of architecture.
In other words, âit is mainstream so it is correctâ can flip into âit was mainstream so it is debtâ in five years.
In a domain where these reversals happen on repeat, the IT architect is the person who decides the systemâs skeleton and writes down the reasons. Even in an era where AI writes code â especially in such an era â the value of this role is going up.
AI knows the answer to âhow to write itâ but cannot answer âwhat to buildâ or âwhy pick thisâ, because those depend on the organizationâs context. In Japanâs IT industry, the precise definition of the job title varies wildly by company, but two things never waver: âdecide the skeletonâ and âleave the reasoning behind.â
This article walks through the architectâs overall picture, the taxonomy of architectures they handle, the mindset, and how decision criteria have shifted now that AI-driven development is the default in 2026.
Who this article (and series) is for
The audience is anyone working in IT, regardless of title or years of experience.
- Managers â to understand the rationale behind technical decisions and reinforce your own.
- Consultants â to validate the technical proposals you put in front of clients.
- Systems engineers â to widen the range of design judgment.
- Programmers â to grasp the âwhy this designâ that lives upstream of the code.
For engineers who are not full-stack, the conceptual knowledge and decision criteria in each domain translate directly into day-job value. A frontend specialist who knows infra. A backend specialist who knows security. That resolution into adjacent domains is the first step toward becoming an architect.
I think of âarchitectâ as a role, not a title. Regardless of what your business card says, you can leave your name on a design decision starting tomorrow.
What is an IT architect?
An IT architect is the person who creates âarchitectureâ in the IT field. Put simply, the person who designs the system.
But âdesignâ here does not mean drawing screen mockups or class diagrams. It is the person who decides the systemâs full skeleton â what language, what cloud, what structure, how it deploys.
In typical projects this person operates as Tech Lead or Lead Engineer, with final responsibility for the teamâs technical decisions.
With the rise of DX, the architect role has become more important year over year. When the shape of the business sits directly on top of software, the systemâs skeleton is the businessâs skeleton.
The architect lives at the boundary between âthe people who write codeâ and âthe people who draw diagrams.â Architects who never touch code lose contact with reality and drift into armchair theory; architects who only write code drift away from organizational decision-making and end up with narrow influence.
A real architect plants both feet on both sides and runs technical and organizational decisions in parallel.
What is âarchitectureâ anyway?
The word âarchitectureâ originally means the design or structure of buildings. Its etymology is the Greek arkhitekton (chief carpenter), pointing to both the person overseeing the design and the design philosophy itself.
In IT, system development is often analogized to construction, and the term âarchitectureâ expanded to cover system structure, design overall, and the design philosophy behind it.
As with buildings, once the skeleton goes up it is extraordinarily hard to change later, and the initial design dictates how the next 5-30 years of operation will go. The âbuilding a houseâ metaphor keeps coming back because the cost of going back feels similar.
The main types of architecture
Architecture splits into multiple types by the scope it covers. This series organizes its articles along this taxonomy.
| Architecture | Scope |
|---|---|
| System Architecture | Hardware, software, network â the comprehensive whole |
| Software Architecture | Application external structure (monolith / microservices / etc.) |
| Application Architecture | Application internal structure and implementation policy |
| Frontend Architecture | UI/UX, rendering, state management |
| Data Architecture | Data collection, storage, management, and use |
| Security Architecture | Security foundation design |
| Operations Architecture | Runbook design, SRE design |
| Enterprise Architecture | Business (BA) / Data (DA) / Application (AA) / Technology (TA) â the enterprise-wide design |
This taxonomy is not fixed. The granularity shifts by company and project. What matters is identifying âat which layer does the design judgment need to happen?â and assigning an architect who can handle it.
Large projects split architects by layer. Small ones have one person doing all of it.
A quick term refresh: a monolith (one large bundled application) and microservices (many small services connected together) are concepts that recur throughout this series. The deep dive lives in the Software Architecture category.
Why is architecture necessary?
To satisfy both functional and non-functional requirements
If satisfying functional requirements alone were enough, you could pick any technology â cloud, on-prem, anything. Login works, data persists, screens render, requirements met.
But real systems also have non-functional requirements: cost, performance, security, availability, scalability, maintainability. Deciding which technologies to combine â and how â to satisfy these is the architectâs job.
Functional requirements ask âwhat can it do?â Non-functional requirements ask âhow well does it actually do it?â
If you only look at the functional ones, every selection comes out as âworks = OKâ, and after production launch you get blowups: performance fails to materialize, outages stack up, operating costs explode. Architecture design can be reframed as a bundle of decisions to satisfy non-functional requirements.
Because redoing it is hard
Once a system has been built, changing its core is essentially impossible. Switching language, switching DB, switching cloud vendor â all of them carry ârebuild from scratchâ levels of effort.
That is why an architect with the knowledge and skills must be there before construction starts.
The cost of going back is the same as in construction. Replacing wallpaper is trivial. Moving a load-bearing column is rebuilding the house. Knowing which decisions are wallpaper and which are columns is the architectâs first job.
When to make each architectural decision
Timing depends on the target. Not everything needs to be decided upfront, but the harder something is to change later, the earlier it must be decided.
| Timing | Architecture type | Examples |
|---|---|---|
| Planning stage | System Architecture | Cloud vs. on-prem, cloud vendor, network topology |
| Planning stage | Software Architecture | Monolith vs. microservices, primary language/framework |
| Requirements â basic design | Data Architecture | RDB vs. NoSQL, primary DB engine, data model outline |
| Requirements â basic design | Security Architecture | Auth method (in-house vs. IDaaS), encryption policy, zero-trust scope |
| Basic â detailed design | Frontend Architecture | Rendering strategy (SSR/SSG/SPA), framework selection |
| Basic â detailed design | Application Architecture | Layer structure, API design policy, error-handling policy |
| Design â operations start | DevOps Architecture | CI/CD pipeline, monitoring design, SLO definitions |
The items listed under âPlanning stageâ are the ones where changing later effectively means rebuilding. The further down the table, the more room there is to adjust while running.
Postponing means downstream decisions move forward on tentative assumptions, and once upstream solidifies, you absorb a massive rework hit. Cloud vendor, primary language, and DB engine selection in particular â deferring these core decisions sweeps the entire team into a wave of rework.
The architectâs mindset
Five mindset items I have learned from books, articles, and field experience.
1. There is no correct answer in architecture
The most important mindset in architectural design is accepting that there is no correct answer. Even an apparently perfect design will hit problems as development progresses, and a âcorrectâ design from one era can become a âbadâ design in the next.
It always requires holistic judgment over the companyâs business background, technical constraints, developer skill, and existing system landscape. A universal correct answer does not exist.
Microservices around 2013 swept the industry as âthe new correct answer.â By 2020 they were being mocked as âtoo operationally heavyâ, with a wave of monolith-reversal case studies.
Five-year reversals are the industry default. Swallowing the current mainstream whole turns into debt five years later. âX is mainstream, so letâs switchâ is how decisions almost always go wrong.
2. Beware over-engineering
Microservices for small systems. Pre-built features for hypothetical future needs. Interface abstraction at every layer. These look sophisticated but in most cases waste time and money.
YAGNI is the strongest brake on over-engineering.
The single habit that prevents over-engineering is constantly asking: âIs there a simpler way?â Thoughtful and experienced people tend to layer for the future, but most of those layers are never used and only consume operational cost.
In some sense, over-engineering is a disease of the experienced. The courage to pick a boring design is what determines the long-term outcome.
3. Revise architecture when needed
If a change is needed, donât change it in the field unilaterally. Loop in the architect who oversees the whole. Architecture decisions ripple across the organization, and bending one in isolation surfaces inconsistencies later.
Always reflect changes back into the architecture document and keep it current. The moment the document drifts from reality, it becomes a âmap of the pastâ that nobody trusts.
A document that has lost trust never gets updated again, and you eventually rebuild it from scratch.
4. Donât get lost in the words
âASPâ, âWeb Serviceâ, âWeb Appâ, âCloud Serviceâ, âSaaSâ â technically these mostly point to the same thing. Renaming is often commercially motivated; the substance has not really changed.
Understanding the substance and producing the appropriate architecture is the architectâs job. The industry is full of cases where the same thing got designed three times under three trendy names.
5. Design assuming AI-driven development (vibe coding)
Vibe Coding â describing requirements to AI in natural language and reviewing the generated code as a human â went mainstream from 2024, and AI is now writing large amounts of business code.
The architectâs job shifts heavily toward âdesigns that are easy for AI to write and readâ. Compared to the old âhumans-onlyâ era, the priorities flip at the root.
| AI-friendly designs | AI-unfriendly designs |
|---|---|
| Code-managed (IaC, type definitions, schemas) | GUI-only black-box tools |
| Mainstream / standard protocols (OAuth, OIDC, REST) | Proprietary protocols, in-house frameworks |
| Modular monolith + 1 DB ACID | Heavy microservices + distributed transactions |
| Convention-based (Rails-style, Next.js-style) | Designs with too many degrees of freedom |
A few terms: IaC, OAuth/OIDC (industry-standard federated login protocols), ACID (the classic DB consistency guarantees: Atomicity, Consistency, Isolation, Durability), distributed transactions (transactions that cross multiple databases).
The AI-era design principles boil down to:
- Aggressively adopt anything you can manage as code (IaC, Zod (TypeScript schema library), OpenAPI (machine-readable API spec standard), etc.).
- Learning cost matters far less as a selection criterion â pick the language for performance and to avoid cold starts.
- Mainstream, convention-based frameworks dominate.
- Avoid custom implementation â auth, crypto, distributed transactions belong on standards.
In the AI era, I believe âAI-friendly design = human-friendly designâ is the convergence. Simplicity and leaning into standards become the highest-value moves.
Three important viewpoints
Three core viewpoints that anchor architectural judgment.
One-way Door / Two-way Door
A decision-making model proposed by Amazonâs Jeff Bezos. It splits decisions into âreversibleâ and âirreversibleâ, and concentrates time on the latter. Probably the single most useful framework in day-to-day architecture work.
| Type | Examples | Approach |
|---|---|---|
| One-way Door (irreversible) | Language, DB, cloud vendor selection | Take time, deliberate, validate |
| Two-way Door (reversible) | Folder layout, library choice, class design | Decide fast and adjust on the fly |
Treating One-way and Two-way Doors with the same weight wrecks your time allocation. Spending too long on a Two-way Door and rushing through a One-way Door is a textbook failure pattern.
Reversible decisions: fast. Irreversible: deliberate. Skip this rule and your project carries both delay and debt.
CAP theorem
In distributed systems (cloud included), simultaneously satisfying Consistency, Availability, and Partition tolerance is in principle impossible. This is the CAP theorem, proposed by Eric Brewer in 2000 and a classic of distributed-system architecture.
In practice you decide between âshowing the screen even if the data is a few seconds staleâ versus âerroring out rather than displaying wrong dataâ based on business requirements.
Social network: the former. Bank balance display: the latter. Whichever you pick locks in everything downstream.
Cognitive load
No matter how lofty an architecture is, if the team cannot understand or implement it, it fails. The sight of an architectâs beloved ideal collapsing in the field within a week is common.
The right discipline is constantly asking, âcan the teamâs actual skills run this?â A design nobody understands might as well not exist.
Always leave the rationale (ADR)
Recording why a decision was made, in a visible form, is the most important behavior an IT architect performs. Systems where only the conclusion remains and nobody can explain âwhy this choice?â years later are guaranteed to become debt.
Why it matters:
- Newcomers can understand the design philosophy.
- You can judge mid-development or post-launch whether the original goal is being met.
- It feeds important context into the next round of development.
The most widely adopted format is the ADR. One decision per file, version-controlled in Git.
# ADR-0001: Adopt PostgreSQL as the database
## Status: Accepted (2026-04-01)
## Context
We need both transactional consistency and full-text search.
## Decision
Adopt PostgreSQL (rejected MySQL and DynamoDB).
## Rationale
- JSON type, full-text search, and geographic data supported out of the box.
- ACID-compliant â safe for monetary calculations.
- Available as managed service on AWS RDS / Aurora.
Teams with an established ADR culture onboard new members faster and have steadier judgment quality. Teams without ADRs have the same arguments every year and repeat past mistakes.
Architect responsibility ladder
Note: industry rates as of April 2026. These will go stale as technology and the labor market shift, so they need periodic refresh.
The architectâs job changes substantially in scope and decision-making weight by scale, experience, and organizational level.
| Tier | Years (rough) | Scope | Decision weight | Typical artifacts |
|---|---|---|---|---|
| 1 Tech Lead | 3-7 | One project, â¤10 people | Mostly Two-way Door | ADR, design docs, code review |
| 2 Product Architect | 5-10 | One product, dozens of people | One-way Door appears too | Architecture diagrams, technology selection docs |
| 3 Solution Architect | 7-15 | Customer engagements, vendor proposals | ROI, estimation, regulation handling | Proposals, PoC design, RFP responses |
| 4 Enterprise Architect | 10+ | Whole company, multi-BU | Multi-year strategic decisions | TOGAF/ArchiMate, EA maps |
| 5 Chief Architect / CTO | 15+ | Whole company, plugged into the C-suite | Executive-level agenda | Tech strategy, org design |
Years are not absolute. A small startup can have a CTO-level decision-maker in their twenties; a large enterprise can keep someone at Tech Lead in their forties.
A pragmatic measure of your tier is decision weight Ă radius of impact. As you climb, the share of organizational judgment goes up versus pure technical judgment.
The architect carries technical judgment and organizational judgment at the same time, with the organizational share increasing as the tier rises. Worth keeping in mind.
Architect failure modes â the forbidden moves
Common ways architects blow up. All of them charge a premium of âdragging the org through bad tech.â
| Forbidden move | Why it fails |
|---|---|
| âDecide because X is mainstreamâ | Ignores org constraints, existing assets, and people; becomes debt in five years. |
| No ADR | Nobody can answer âwhy this?â later, and successors flounder. |
| Over-engineering (extra layers for the future) | Unused abstractions consume operating cost; YAGNI violation. |
| Ideal design ignoring team skills | Operational impossibility, design becomes a fossil. |
| Treating One-way Doors as Two-way Doors | Reversing irreversible decisions costs years of debt. |
| Deciding in isolation (no implementer in the room) | Idealism disconnected from reality breaks at implementation. |
| Ignoring Conwayâs law in service splits | Misalignment with the org structure causes inter-team friction. |
| Outsourcing to a vendor without understanding internals | Helpless during incidents, locked in long-term. |
| Validating new tech in production (skipping PoC) | Risk goes unmeasured; trouble surfaces in production. |
| Not updating the architecture doc | Drifted âmap of the pastâ creates confusion. |
Conwayâs law mentioned above is the empirical rule that âorganizational structure determines system structure.â Deeper coverage is in another article.
Segmentâs monolith reversal from 140 microservices (2020), Amazon Prime Videoâs consolidation from serverless+microservices into a monolith with 90% infrastructure cost reduction (2023), Netflixâs GraphQL Federation move (2020+, re-aggregating to lower the inter-service API coupling cost).
Even hyperscale tech companies swap design philosophies on a 5-year cadence. âIt is mainstream so it is correctâ flips into âit was mainstream so it is debtâ when the era changes.
What happens at sites without ADRs (industry anecdote)
I myself am not (yet) in a position making architect-tier decisions, but here is a story I hear variations of often.
In a B2B SaaS, the backend message queue used an unusual middleware. The textbook choice would be RabbitMQ or Kafka, but they had picked something else. Everyone assumed âthere must have been a reasonâ at the time.
Three years later, the original designer had left. No ADR. Digging through old Slack threads turned up no record of the rationale.
The ops team treated it as âthere must be a meaning, donât touch itâ and left it untouched for three years, falling behind on version upgrades, accumulating security alerts, and eventually causing a half-day production outage.
The post-incident postmortem concluded the original selection rationale was likely âthe designer at the time wanted to play with that tech.â The truth is still unknown.
This scene plays out everywhere in the industry. Architectural decisions correct for their context become unevaluable once the context is lost. A 5-line ADR per decision would have let the successor say, âthis premise no longer holds, letâs reconsiderâ with confidence.
Without it, everyone defaults to âdonât touch itâ as self-preservation. What survives across time in architecture is not the conclusion â it is the reason.
What you must decide â whatâs your projectâs answer?
For each of the following, articulate your projectâs answer in 1-2 sentences. Leaving them ambiguous now will always come back as âwhy did we decide that?â later.
In priority order, the items the architect should decide at project start:
- Application form (Native / Web / Hybrid)
- Deployment model (Cloud / On-prem / Hybrid)
- Primary language and framework
- Datastore selection (RDB / NoSQL / Mixed)
- Authentication (in-house / IDaaS)
- Non-functional targets (availability, performance, security)
- ADR operating rules (where, who, when)
Related Articles
https://en.senkohome.com/arch-intro-learning-roadmap/ https://en.senkohome.com/arch-intro-overview/ https://en.senkohome.com/arch-intro-series-index/
Summary
This article covered âWhat an IT Architect Actually Doesâ â a role whose value rises in the AI era.
Architect is a role, not a title. The person who decides the systemâs skeleton and leaves the reasons behind. Because AI now writes the code, the human who can decide âwhat to buildâ and âwhy thisâ appreciates in value.
There is no correct answer, so leave the reason. That, I believe, is the largest gift you can hand to the next architect.
The next article tackles the âLearning Roadmapâ â a concrete reading order by role and experience.
I hope youâll read the next article as well.
đ Series: Architecture Crash Course for the Generative-AI Era (2/89)