Frontier coding agents backed by large language models can handle context windows of hundreds of thousands to millions of tokens, reshaping the Software Development Life Cycle (SDLC). Rich context handling and multi‑step reasoning allow entire Functional Requirement Documents (FRDs) and repository contents to be ingested in a single workflow, making specification quality the fuel for autonomous delivery.
This paper formalises Spec‑Driven Agentic Development (SDAD) as a synthesis of disciplined upfront 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 swing 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, the model extends to team role metamorphosis (engineer, QA, platform, product) and quantitative governance metrics: Ambiguity Tax, Spec Fidelity, System Error Rate (SER), and Agentic Time‑to‑Completion Index (TCI_agentic) with repair multiplier $\phi$. Pragmatic adoption is guided by hybrid estimation and a staged migration blueprint.
Industrial and research evidence on AI‑augmented testing and verification supports the separation of synthesis and release authority. Overall, the argument is that agentic speed does not eliminate engineering discipline; it relocates discipline upstream into specification precision, explicit gates, and auditable provenance.
Blogger's Review: SDAD elevates specifications to the central driver, leveraging LLM context capabilities for unprecedented delivery speed and control, yet it still demands rigorous engineering effort during the specification phase.