# SDAD: Spec-Driven Agentic Development for the AI-Native SDLC

arXiv: https://arxiv.org/abs/2608.20341 · HTML: https://mlsanity.com/paper/2608.20341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 5 May 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 11 of 11)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [On the Evaluation of Large Language Models in Unit Test Generation](https://mlsanity.com/paper/2406.18181/) (2024)
- [Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects](https://mlsanity.com/paper/2511.04427/) (2025)
- [How much does AI impact development speed? An enterprise-based randomized controlled trial](https://mlsanity.com/paper/2410.12944/) (2024)
- [An Empirical Study on the Code Refactoring Capability of Large Language Models](https://mlsanity.com/paper/2411.02320/) (2024)
- [Migrating Code At Scale With LLMs At Google](https://mlsanity.com/paper/2504.09691/) (2025)
- [ChatCollab: Exploring Collaboration Between Humans and AI Agents in Software Teams](https://mlsanity.com/paper/2412.01992/) (2024)
- [Large Language Models for Automated Web-Form-Test Generation: An Empirical Study](https://mlsanity.com/paper/2405.09965/) (2024)
- [AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI](https://mlsanity.com/paper/2602.21251/) (2026)
- [Tiered Super-Moore's Law: Price Evolution, Production Frontiers, and Market Competition in Large Language Model Inference Services](https://mlsanity.com/paper/2603.28576/) (2026)

## Abstract (arXiv)

Frontier coding agents backed by large language models with context windows from hundreds of thousands to millions of tokens are restructuring the Software Development Life Cycle (SDLC). Rich context handling and multi-step reasoning now allow substantial Functional Requirement Documents (FRDs) and repository context to be ingested in a single workflow, making specification quality the execution fuel for autonomous delivery. This report formalises Spec-Driven Agentic Development (SDAD) as a synthesis of disciplined up-front formalisation and high-velocity implementation: intent capture, machine-readable specification, agentic synthesis, and independent multi-agent verification under human sign-off. We revisit the historical pendulum between Waterfall and Agile, introduce AI-code as a fourth production paradigm, and compare Human-Agile (circa 2020) with Agentic-SDAD (circa 2026) across artefacts, cadence, accountability, and security posture. Beyond process description, we extend the model to team role metamorphosis (engineer, QA, platform, and product functions), quantitative governance (Ambiguity Tax, Spec Fidelity, SER, and TCI_agentic with repair multiplier phi), and pragmatic adoption via hybrid estimation and a staged migration blueprint. Industrial and research evidence on AI-augmented testing and verification is integrated to motivate separation between synthesis and release authority. Overall, the paper argues that agentic speed does not eliminate engineering discipline; it relocates discipline upstream into specification precision, explicit gates, and auditable provenance.