Engineering brief
RAD from 1991 Is the Missing Workflow for AI Coding Agents
This engineering brief covers RAD from 1991 Is the Missing Workflow for AI Coding Agents, with practical context for AI and developer-tool decisions.
The Brief
Rapid Application Development (RAD) from 1991 maps perfectly onto AI coding. But the prototype alone isn't safe—you need a spec.
Decision relevance
Read this for workflow impact, implementation trade-offs, and the claims that need technical scrutiny before they reach team planning.
Summary
James Martin's Rapid Application Development (RAD) methodology, introduced in 1991, is surprisingly prescient for today's AI coding with agentic tools. It advocates lightweight planning, rapid prototyping, iterative construction, and verified cutover. The video argues that AI agents have made prototyping incredibly fast, but the missing piece is a precise spec.
Modern vibe coding accelerates the prototyping phase, but it introduces security risks. The example of an expense approval app shows that AI-generated code can miss critical rules like self-approval prevention. A study cited finds 45% of AI-generated code has security weaknesses. The prototype alone is insufficient for production.
The solution is spec-driven development: discoveries from the prototype are captured in a formal spec that includes business rules, security requirements, and acceptance criteria. That spec becomes the source of truth, and the generated code must pass tests derived from it. This mirrors RAD's original two halves: a precise description and code generation.
The implication is that programmers shift from writing code to writing specs and verifying results. Teams must adopt governance around AI-generated code, combining rapid prototyping with rigorous specification. The tradeoff is speed versus safety, but RAD's framework shows both can coexist with discipline.
Why It Matters
A 35-year-old methodology provides a proven workflow for AI coding, balancing speed with security.
Editorial analysis
Key claims
- Adopt spec-driven development to govern AI-generated code, not just prototype-driven development.
Practical use cases
- Use this as input for tooling evaluation, workflow planning, and technical due diligence.
Risks / caveats
- The historical nostalgia; the claim that RAD was universally adopted.
Who should care
- Engineering managers, tech leads, and CTOs evaluating AI or developer tooling decisions.
Related topics
Bottom Line
Adopt spec-driven development to govern AI-generated code, not just prototype-driven development.
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