SDAD formalizes spec-driven agentic development for AI-native SDLC
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Agents with 100K-1M token context windows can now ingest entire FRDs and codebases in a single pass, making specification quality the primary lever for autonomous software delivery. Production teams must shift engineering discipline upstream by formalizing precise, machine-readable specs—poorly defined requirements now directly bottleneck agent output velocity and correctness, while high-quality specs enable end-to-end agentic synthesis with verifiable outputs. If you're running coding agents, this means 80% of your effort moves from writing code to writing and validating specifications, with corresponding changes to team roles and release gates.
Coding agents with hundreds-of-thousands to million-token context windows can now consume large requirement docs and repository context in a single workflow, making the spec the primary control surface for autonomous implementation. For production teams, the practical shift is that SDLC discipline moves upstream: vague tickets become expensive failure modes, while machine-readable specs, explicit acceptance gates, provenance, and independent verification agents become required infrastructure before human release sign-off.