An Engineer’s Take: Augmented Development Workflows

Orrin Francis, Principal Consultant

Pat recently shared his experience in our blog with using Claude to build a production-grade app—no coding background required. It’s a great read (and worth checking out if you haven’t already). But as the resident engineer, I have a slightly different take.

While tools like Claude are empowering business leaders to get hands-on with tech in new ways, engineers are experiencing their own quiet revolution. The tooling we now have access to—especially IDE-native platforms like Cursor—offers a radically more efficient and robust path to building scalable, secure applications.

So here’s how someone like me might tackle the same kind of project, just with an engineering lens.

Step 1: Problem Definition & Architecture

Business Owner Approach

Pat outlined his goal in plain English, and Claude interpreted and generated code accordingly.

Engineering Workflow with Cursor

I’d use Cursor’s /spec command to turn requirements into a technical blueprint—complete with architecture diagrams via Mermaid.js, and a scaffolded FastAPI project with PostgreSQL models and a job scheduler built in.

# /spec: "Build a market data tracker with API polling, PostgreSQL storage, and error handling"

Cursor returns:

- FastAPI scaffold

- SQLAlchemy models

- APScheduler config

Step 2: Code Implementation

Business Approach

Claude helped Pat build the project step-by-step through chat, iterating as he went.

Engineering Workflow

Cursor’s code generation is aware of the entire codebase, not just one file. If I want to add retry logic to an API client, Cursor applies the right decorators based on the project’s existing patterns. It also supports real-time collaboration with other devs or AI reviewers, right in the IDE.

# Type: "Add retry logic to API client"

→ Injects @retry decorators with Tenacity, consistent with codebase

Step 3: Debugging & Optimisation

Business Approach

When errors popped up, Claude helped interpret them and offered fixes.

Engineering Workflow

With Cursor, I get error-specific suggestions and performance profiling built-in.

Example:

ERROR: API rate limit exceeded (HTTP 429)

→ Suggestion: "Implement exponential backoff? [Apply Fix]"

Or, if performance is an issue:

# Highlight code → "Benchmark this"

→ Identifies bottleneck in pandas.DataFrame

→ Recommends Polars rewrite with parallelism

Step 4: Security & Maintainability

Business Approach

Security and resilience weren’t a core part of the build, yet. And that’s totally fine for a quick prototype.

Engineering Workflow

We build with security-first principles baked in. Examples:

Secret detection flags exposed API keys
`cursor audit --cve` checks for vulnerable dependencies
Auto-generated documentation and typed interfaces

Business vs. Engineering: Two Valid Paths

Aspect	Business Approach	Engineering Approach
Code Context	One file at a time	Full project awareness
Debugging	Manual, iterative	Integrated + Intelligent
Scalability	Prototype-friendly	CI/CD Ready
Security	ad hoc	Built-in + enforced

Why This Matters for Engineers

AI is not replacing engineers—it’s changing where we focus our time. With tools like Cursor:

Boilerplate disappears – reducing effort by up to 40% (Cursor internal benchmarks).
Project standards are enforced – AI knows what “good” looks like in your repo.
Governance is built-in – with policy enforcement, version control, and audit trails.

A New Model for Engineering Teams

We're not just coding faster—we're working smarter. Here's the new rhythm:

Spec → Code: Use /spec to generate test-driven modules
Review → Refactor: Validate architecture with /ask and implement lint rules
Deploy → Monitor: Auto-generate Kubernetes manifests, dashboards, and alerts

Limitations to Watch

Distributed Systems Still Need You: Large-scale architecture still requires human oversight.
Tech Debt Risk: AI can write code fast—but enforcing linting, typing, and review is critical.