Andrii Bidochko

• Updated: March 20, 2026
• 6 min read

AI Coding Agents: Intentional Code and Robust Design

AI coding agents are intelligent assistants that generate, refactor, and test code automatically, but they must be guided by intentional code practices to keep a codebase scalable, self‑documenting, and robust.

Why Intentional Code Matters in the Age of AI

Developers are witnessing a surge of AI coding agents that can write entire modules in seconds. While the productivity boost is undeniable, the speed of code generation can also introduce hidden technical debt if the output is not deliberately structured. This article serves as a manifesto for engineers who want to harness AI without sacrificing code quality. We’ll explore the AI coding agents guidelines, differentiate semantic and pragmatic functions, and provide actionable tips for building robust model design and self‑documenting codebases that scale.

Overview of AI Coding Agent Guidelines

At the core of any sustainable AI‑assisted development workflow are three pillars:

• Intentionality: Define clear objectives for what the AI should produce.
• Modularity: Break logic into reusable, well‑named functions.
• Verification: Enforce unit and integration tests that the AI‑generated code must pass.

These pillars align perfectly with the UBOS platform overview, which offers built‑in testing pipelines and model validation tools.

Being Intentional About How AI Changes Your Codebase

When an AI coding agent writes a function, it often mirrors the prompt’s phrasing rather than the underlying domain logic. To avoid “sloppified” code, developers should:

1. Specify input and output contracts explicitly.
2. Ask the AI to generate semantic functions first, then compose them with pragmatic wrappers.
3. Require the AI to include doc‑strings that explain edge‑case handling.

Following this approach reduces the risk of hidden side effects and makes the codebase easier for both humans and future AI agents to understand.

Semantic Functions vs. Pragmatic Functions

Semantic Functions: The Building Blocks

Semantic functions are pure, minimal, and focused on a single responsibility. They should:

• Accept all required inputs to achieve their goal.
• Return all necessary outputs directly, without hidden state.
• Contain no side effects unless the side effect is the function’s purpose.

Examples include quadratic_formula(a, b, c) or a generic retry helper such as retry_with_exponential_backoff_and_run_y_in_between<Y: func, X: Func>(x: X, y: Y). These functions are inherently unit‑testable and require little to no external documentation because their signatures are self‑describing.

Pragmatic Functions: The Orchestrators

Pragmatic functions act as wrappers that coordinate multiple semantic functions and handle real‑world concerns like I/O, logging, and error handling. They are typically:

• Used in a limited number of places; if they appear everywhere, consider refactoring.
• Tested via integration tests rather than isolated unit tests.
• Documented with concise comments that highlight non‑obvious behavior (e.g., “fails early when balance < 10”).

A pragmatic function might look like provision_new_workspace_for_github_repo(repo, user) or handle_user_signup_webhook(event). These functions evolve over time, so clear documentation and robust test coverage are essential.

Tips for Robust Model Design and Self‑Documenting Codebases

Models define the shape of data flowing through your application. A well‑designed model makes invalid states impossible, which in turn reduces bugs and simplifies AI‑generated code.

1. Make Wrong States Unrepresentable

Use strict typing and enumerations to prevent illegal combinations. For instance, instead of a single User object that sometimes contains a workspaceId, create distinct types like UnverifiedEmail, PendingInvite, and BillingAddress. This approach forces the compiler (or TypeScript) to catch mismatches early.

2. Prefer Composition Over Flattening

When two concepts are related but independent, keep them separate. A composite type such as UserAndWorkspace { user: User, workspace: Workspace } preserves the integrity of each model while still allowing joint operations.

3. Use Brand Types for Primitive IDs

Wrap raw strings or numbers in distinct types (e.g., DocumentId = UUID & { __brand: "DocumentId" }) so that swapping a DocumentId with a MessageId triggers a compile‑time error instead of a runtime bug.

4. Name Models Precisely

A model’s name should convey its purpose. If you see a phone_number field on a BillingAddress, the model is likely misnamed. Renaming it to ContactInfo or splitting it into separate models resolves the ambiguity.

These practices are baked into the Enterprise AI platform by UBOS, which provides schema validation and type‑safe APIs out of the box.

Practical Examples Using UBOS Tools

Below are three concrete scenarios that illustrate how to apply the guidelines with UBOS’s low‑code ecosystem.

Example 1: Generating a Self‑Documenting API Endpoint

Using the Web app editor on UBOS, you can prompt an AI coding agent to create a semantic function that validates input data:

function validate_order_payload(payload: OrderPayload): ValidationResult {
    // Returns early if required fields are missing
    if (!payload.customerId) return { ok: false, error: "Missing customerId" };
    if (payload.items.length === 0) return { ok: false, error: "No items in order" };
    return { ok: true };
}

Then wrap it in a pragmatic function that handles HTTP routing and logging:

async function createOrderHandler(req, res) {
    const result = validate_order_payload(req.body);
    if (!result.ok) {
        console.warn("Invalid order:", result.error);
        return res.status(400).json({ error: result.error });
    }
    // Business logic continues here...
}

Example 2: Building a Scalable Model with Chroma DB

Integrate the Chroma DB integration to store vector embeddings for a search feature. Define a strict model:

type DocumentEmbedding = {
    id: DocumentId;
    vector: number[];
    metadata: {
        title: string;
        createdAt: Date;
    };
};

The AI agent can then generate CRUD functions that respect this schema, ensuring no stray fields appear in the database.

Example 3: Voice‑Enabled AI Assistant with ElevenLabs

Combine the ElevenLabs AI voice integration with a semantic function that formats responses:

function format_answer(question: string, answer: string): string {
    return `You asked: ${question}. Here is the answer: ${answer}.`;
}

Then a pragmatic wrapper streams the formatted text to ElevenLabs for speech synthesis, handling retries and error logging in a single place.

All three examples demonstrate the separation of concerns: pure, testable semantic functions paired with pragmatic orchestrators that manage real‑world complexity.

Conclusion: Adopt Intentional AI Coding Practices Today

AI coding agents will continue to evolve, but the responsibility for code quality remains with developers. By embracing intentional code, distinguishing semantic from pragmatic functions, and designing robust models, you can turn AI from a source of technical debt into a catalyst for rapid, reliable innovation.

Ready to experiment with AI‑driven development on a platform built for intentional coding? Explore the UBOS pricing plans or dive straight into the UBOS templates for quick start. For startups seeking a fast‑track, the UBOS for startups page outlines a lean path to production.

For a deeper look at the original manifesto that inspired this guide, read the source article here.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.