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Patrick Cornelißen
Patrick Cornelißen

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From vibe coding to agentic engineering

#ai #coding #agents #workflow

Vibe coding is a good starting point, but it is not where serious AI-assisted development ends.

The next step is agentic engineering: using AI coding agents inside a controlled engineering workflow, with context, tests, review and clear boundaries.

From output to workflow

Vibe coding often focuses on the generated output:

Ask for feature -> get code -> run it -> ask for fixes
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Agentic engineering focuses on the system around the model:

Define task -> provide context -> implement small slice -> run checks -> review diff -> iterate
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The model is still important, but it is not the whole process.

What changes

In an agentic workflow, the AI tool can do more than answer questions. It can:

  • read relevant files
  • edit code
  • run tests
  • inspect errors
  • update documentation
  • prepare commits
  • compare alternatives

That is powerful, but only if the environment gives the agent good constraints.

Constraints are not bureaucracy

Good constraints make the agent more useful.

Examples:

  • project instructions in a versioned file
  • clear test commands
  • lint and typecheck gates
  • small tasks with explicit acceptance criteria
  • code review before merge
  • no direct pushes to main
  • no production credentials in the workspace

These are normal engineering practices. AI makes them more important, not less.

A practical pattern

A useful agentic coding loop looks like this:

1. State the goal and constraints.
2. Let the agent inspect the relevant files.
3. Ask for a small implementation.
4. Run tests and type checks.
5. Review the diff.
6. Ask the agent to fix only the findings.
7. Commit once the change is understandable.
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The key is that the agent is allowed to work, but the workflow keeps evidence visible.

Where agents help most

Agentic tools are strongest when the work is local but tedious:

  • refactoring a repeated pattern
  • adding tests around existing behavior
  • wiring a small feature through several files
  • updating documentation after a change
  • investigating a failing test
  • generating migration helpers

They are weaker when the task depends on unclear product judgment or hidden team context.

The developer remains accountable

Agentic engineering does not remove responsibility. It changes where attention goes.

Instead of typing every line, the developer watches for:

  • wrong assumptions
  • unnecessary complexity
  • weak tests
  • architecture drift
  • security issues
  • behavior changes hidden in refactors

The human is still the reviewer of meaning.

Bottom line

The useful future is not "AI writes all the code while developers vibe". It is AI agents doing more of the mechanical work inside workflows that still look like engineering.

That is less flashy, but much more valuable.

This article is based on the German original on KIberblick:
https://kiberblick.de/artikel/grundlagen/von-vibe-coding-zu-agentic-engineering/

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