How We Build

WebF1 builds software with a multi-tier AI agent architecture - a plan-first, approval-gated workflow where every step is reviewed, every model is chosen for the task, and every change is accountable.

The AI Development Pipeline

Work flows through a team of specialized AI agents, each with a distinct role - from high-level coordination to line-by-line code review. Each agent does one thing exceptionally well.

Orchestrator

Coordinates the entire development workflow - breaks down requests into plans, routes work to specialized agents, and tracks progress from concept to deployment.

Plan Builders

Analyze requirements and produce detailed execution plans with step-by-step delivery tasks, guideline compliance checks, and testing strategies before any code is written.

Implementers

Execute approved plan steps one at a time, writing code that follows project conventions and passing through build verification after every change.

Code Reviewers

Review code changes after each step against project standards defined in AGENTS.md, catching issues early before they compound across the codebase.

PR Reviewers

Conduct formal pull request reviews with Approve, Comment, or Request Changes verdicts - so every merge meets quality and architecture standards.

Plan-First, Approval-Gated Workflow

Before any code is written, a detailed execution plan is created and reviewed. Each plan is broken into discrete delivery steps, and every step requires developer approval before the agent may proceed. Nothing happens without explicit sign-off.

A checkbox accountability system tracks every task - plan validation, code implementation, build verification, and review - so progress is always visible and auditable. Steps are never skipped; they are checked off as completed or left open as work-in-progress.

Multi-Model Flexibility

Different AI models excel at different things. We do not lock into a single model - we use multiple leading models from different providers.

Right Model for the Task

Planning, implementation, and code review each demand different strengths. One model may produce outstanding architectural plans while another writes cleaner code or catches subtle review issues. The developer chooses the optimal AI tool for each specific job.

Provider Independence

By drawing on models from multiple providers, the pipeline stays independent of any single vendor. As new models are released, they can be evaluated and adopted without restructuring the workflow - the architecture is model-agnostic by design.

Developer in Control

The developer decides which model handles each step. This is AI-assisted development, not autonomous AI development - where a human selects the tools, approves the plans, reviews the code, and merges the pull requests. Keeping a developer at every critical decision point means hallucinations are caught before they reach production, business context is applied where no model can infer it, and every change has clear ownership. The human directs the work; the AI accelerates it.

Skill-Based Knowledge System

Domain knowledge is encoded in reusable skill files - not hardcoded into prompts. A library of specialized, purpose-built skills covers backend, frontend, DevOps, scaffolding, and code review.

Each skill is a structured document that defines conventions, patterns, and step-by-step procedures for a specific domain. When an agent works on a task, it loads the relevant skill and follows it precisely. This ensures consistency across projects - the same rules apply whether building a new service or modifying an existing one.

Business Object Implementation

Standardized inheritance, interface placement, and data access patterns for all business logic components.

ORM Conventions

Entity creation rules, migration patterns, and query conventions that keep the data layer consistent across projects.

Message Bus Patterns

Contract design, queue naming, publisher and consumer patterns for reliable message-based architectures.

Docker & Kubernetes Deployment

Image building, Kustomize overlay management, and processor deployment to Kubernetes clusters.

Automated Quality Gates

Quality is built in at every stage - not bolted on at the end. Six review checkpoints ensure code is correct, consistent, and reviewed before it reaches production.

  1. 1

    Plan Validation

    Every execution plan is validated for structure, completeness, and compliance before any implementation begins.

  2. 2

    Post-Step Code Review

    Code is reviewed against project standards after each implementation step - not just at the end of the PR.

  3. 3

    Pre-PR Review

    A full review pass simulates a pull request before one is created, catching issues while they are still cheap to fix.

  4. 4

    Formal PR Review

    Pull requests receive formal reviews with Approve, Comment, or Request Changes verdicts - no unreviewed merges.

  5. 5

    DI Verification

    Dependency injection containers are checked to confirm every service and processor is correctly registered.

  6. 6

    Build Verification

    The project builds successfully after every step - compilation errors are caught and fixed immediately, never deferred.

Full-Stack DevOps Integration

The pipeline extends well beyond code - through building, deploying, and managing the full infrastructure lifecycle.

Docker Image Building & Deployment

Automated build scripts produce tagged container images and push them to a private registry, ready for deployment.

Kubernetes (Kustomize) Configuration

Environment-specific overlays manage deployments across dev, staging, and production without duplicating manifests.

NuGet Package Upgrade Analysis

Dependency analysis produces upgrade plans that surface security risks, deprecation warnings, and backwards-compatibility concerns.

New Project Scaffolding

A guided orchestrator asks technology-choice questions and generates a complete .NET solution structure with multiple interconnected projects.

Git Integration

All git operations - branches, commits, pull requests, releases, issues - are handled through Git-based tools without manual CLI work.

The Result

AI-assisted development with plan-first workflows, multi-model flexibility, and automated quality gates produces software that is faster, more consistent, and fully accountable.

Faster Development Cycles

Plan-first workflows and automated quality gates eliminate rework, so features move from concept to production faster.

Consistent Code Quality

Skill files and automated reviews ensure every component follows the same conventions, across every project, every time.

Right Model for Every Task

Multi-model flexibility means the right model is selected for each task - planning, implementation, or review.

Full Accountability & Audit Trail

Checkbox-driven plans, step-by-step reviews, and formal PR approvals create a clear, traceable record of every change.

Scales From Features to Full Scaffolding

The same pipeline handles a single feature enhancement or a complete new project scaffold - from one-line fixes to full multi-project solutions.

Let's build your next system with AI

Our AI-assisted development methodology helps us deliver your project faster, without sacrificing quality. Let's talk about what we can build for you.

Get in Touch