Code Consistency for AI Agents: Style Is a Control System

Part of SAgentLab's AI-Native Engineering series - practical notes for founders building real products.

If you’re letting agents write code across your codebase, you are no longer just “coding.”

You are managing a distributed writing system that happens to emit TypeScript, Python, SQL, and YAML.

Consistency isn’t aesthetics. Consistency is how you reduce entropy.

The core problem

Humans can hold style in their heads. Agents cannot. They’ll happily:

• invent new patterns
• duplicate utilities
• create near-identical abstractions
• change naming conventions mid-file

It’s not malicious. It’s sampling.

The solution is to treat style and standards as executable constraints.

1) Put style into tools, not tribal memory

You want:

• formatter (Prettier, Black, gofmt)
• linter (ESLint, Ruff)
• type checker (tsc, mypy)
• import organization

Then make the agent run them.

Rule: if the standard cannot be verified, it is not a standard.

2) Create a “repo constitution”

Agents work best when you give them a small, stable doctrine.

Create a short document like ENGINEERING.md:

• naming conventions
• folder layout
• how to write tests
• error-handling philosophy
• logging guidelines
• API patterns

Keep it short. Think: 1–2 pages.

Agents will read it. Humans will too.

3) Use scaffolds to shrink the search space

Instead of asking an agent to “add an endpoint,” give it:

• a generator
• a template
• a reference implementation

For example:

• “New API routes must use createRoute() wrapper.”
• “All DB access must go through db.ts.”

Every time you standardize, you delete a thousand weird futures.

4) Enforce “knowledge reuse” explicitly

Agents love to re-implement because it’s locally optimal.

Add a policy:

• Before adding new helpers, search the repo for existing utilities.
• Prefer existing patterns, even if not perfect.

Concrete workflow:

1. agent lists 3 existing similar files
2. agent states the pattern it will follow
3. agent implements using that pattern

This simple ritual prevents accidental “framework creation.”

5) Make the agent explain diffs, not just output code

A strong control trick:

• require a short change log
• require which conventions were followed
• require the tests run

Example prompt fragment:

Before you finish, list:
- files changed
- why this approach matches existing patterns
- commands you ran (tests/lint)
- any assumptions

This forces the agent to bind itself to reality.

6) Centralize config and versions

Drift happens when each package has its own:

• tsconfig
• eslint config
• prettier rules
• build scripts

If you want multiple agents to behave consistently, centralize the rules.

7) Guardrails: CI is your second brain

CI is where agents learn humility.

Minimum bar:

• lint + format check
• unit tests
• type check

Better:

• static analysis (Semgrep)
• dependency audit
• e2e smoke test

If it’s not in CI, it’s optional. Agents treat optional as “maybe later.”

A practical pattern: “Spec → Diff → Verify”

Train the system around a loop:

1. Spec: short acceptance criteria
2. Diff: small PR with constrained scope
3. Verify: tests + lint + typecheck

Agents thrive when the loop is tight.

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Bottom line: with agents, style is not taste. Style is a stability mechanism. When you turn standards into tools, you get code that looks like one team wrote it—even if it was 20 agents on a Tuesday night.

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Work with SAgentLab

If you're trying to ship AI-native features (agents, integrations, data pipelines) without turning your codebase into a demo-driven science project, SAgentLab can help.

• Website: https://www.sagentlab.com/
• Contact: https://www.sagentlab.com/#contact