When AI Gets Faster, Engineering Needs More Structure
If AI can execute tasks faster, how do we know whether what it builds is actually correct?
The problem is not always that AI performs poorly. It may stem from unclear requirements, incomplete context, or unresolved business rules. When these elements are ambiguous, AI tends to fill in the gaps with assumptions of its own.
This is why working with AI is evolving from Vibe Coding to Agentic Engineering. It is not simply about having more capable AI. It is about changing how humans design, guide, and control the way AI works.
From “Ask AI to Build” to “Define What Good Looks Like”
Imagine asking AI to:
“Build me a leave management system.”
AI might quickly generate a leave request page, an approval interface, and a leave history page. But many important questions remain unanswered.
Who is authorized to approve requests? Can users approve their own requests? When should leave balances be deducted? What happens if the same request is approved twice? And what falls outside the scope of the system?
These are not coding problems. They are questions of requirements, business rules, and human decision-making.
If these elements are not clearly defined, AI has to make assumptions. Therefore, as AI becomes faster, organizations need to focus on more than writing better prompts. They need to define clearly what a correct outcome looks like.
The key principle is to give AI the information it needs without introducing irrelevant context. This means defining not only what AI should do, but also what it is allowed to do and what it must not do.
Defining the scope of AI's work is becoming an essential part of the engineering process.
A Functional UI Does Not Mean the Logic Is Correct
When AI can generate UIs quickly, seeing a webpage that looks right and responds to clicks is no longer enough to determine whether the system is correct.
For example, if a requirement states that users must not approve requests submitted by members of other teams, checking whether the Approve button is hidden is insufficient. A user might still call the API directly.
What matters is whether the system actually rejects unauthorized requests.
This is the difference between Output and Evidence:
Output is what AI produces.
Evidence is proof that the output behaves as required.
A reliable workflow should connect:
Requirement → Test → Evidence
For example:
Requirement: Leave balances must not be deducted twice.
→ Test: Approve the same request twice.
→ Evidence: The leave balance is not reduced a second time.
Or:
Requirement: Cross-team approvals are not allowed.
→ Test: Send an API request from an unauthorized user.
→ Evidence: The system rejects the request.
Viewed this way, testing is no longer just a step that comes after code has been written. It becomes part of defining what a correct outcome actually means.
Agentic Engineering Is a Workflow Shift
The difference between Vibe Coding and Agentic Engineering is not simply which AI is more capable. It is how work is assigned, structured, and controlled.
With Vibe Coding, we might start with a broad instruction, ask AI to generate an output, and then spend multiple rounds reviewing and fixing it.
Agentic Engineering starts before the coding begins. Humans define:
Intent → Requirements → Context → Scope → Acceptance Criteria → Verification
AI can then help plan and execute the work within those boundaries.
The Engineer's role does not disappear. Instead, it evolves:
From writing everything → to defining what AI should build
From reviewing every line → to evaluating logic, tests, and evidence
From fixing outputs → to designing workflows that make correct outputs more likely from the start
As AI becomes faster, breaking work into smaller pieces becomes even more important. Instead of assigning an entire large feature to AI in one go, teams can divide the work into smaller tasks, each with checkpoints that enable faster feedback.
Concepts such as Vertical Slicing help teams build functionality in small, testable increments that can be demonstrated early. This allows teams to identify and correct deviations before they become larger problems.
The New Bottleneck Is Not Coding
Adopting Agentic Engineering does not require an organization to transform its entire engineering process overnight.
Teams can start with a few practical principles:
Clarify requirements.
Provide the necessary context.
Define the scope and acceptance criteria.
Ask AI to plan before executing.
Break work into smaller tasks with checkpoints.
Verify evidence against the original requirements.
Feed accumulated knowledge back into specifications and tests.
The goal is not to replace Engineers with AI. It is to build workflows that allow Human Intent + Engineering Discipline + AI Capability to work together systematically.
Because as AI gets faster, ambiguity becomes more costly. The future of Software Engineering may not be determined by who can write code the fastest.
Instead, it may be determined by who can design workflows that turn AI's speed into correct, reliable, and verifiable outcomes.