Learning how to BMAD
My evolution in agentic development has been somewhat typical. At the end of 2025, it was all about the new thing at the time — Vibe Coding. Then, at the beginning of 2026, SDD with OpenSpec became a big game changer. It provided a way to scale agent-driven development without letting the codebase turn into a mess. And the next natural step? Multi-agent agentic workflows with BMAD. At first…
In 2025, the focus was on Vibe Coding. By the start of 2026, SDD with OpenSpec revolutionized agent-driven development. This led to the introduction of Multi-agent agentic workflows with BMAD. While BMAD appears complex, a simple approach was taken: talking to a friend who uses BMAD, transcribing the conversation, and asking a Generative AI to summarize it.
Vlad, a colleague, scaled a production architecture using BMAD AI framework. He shifted his role from writing syntax to orchestrating architecture, maximizing Claude's highest subscription tiers and transforming output vector.
The operational framework for high-leverage execution using BMAD includes strict adherence to spec-driven workflows. The process involves brainstorming, creating a Product Requirement Document (PRD), an Architecture Decision Registry (ADR), defining Epics and Sharding, checking Implementation Readiness, implementation and review. Should the AI's readiness score be low, an engineer issues a "correct course" command.
Technical debt is eliminated with AI's reverse calculation. Vlad's monorepo CI/CD pipeline was burdened by batch scripts causing test cycles to take 90 minutes. Instead of manual rewriting, AI orchestrated an automated intervention: analyzing the repository, identifying quadratic scaling bottlenecks in batch scripts, evaluating different languages, and proposing a Go port. A refactor that would have taken a week of human engineering time was resolved asynchronously while the engineer managed top-level logistics.
Technical guardrails for scaling AI agents like BMAD include enforcing monorepos, capping context windows, automating traceability, and shifting unknowns left. Monorepos are preferred to avoid friction when tracking commit hashes across dependencies. Context windows should be limited between 100k and 260k tokens to avoid AI hallucinations.
Automated traceability becomes crucial as project scales. Structural unknowns and technical spikes should be addressed early in the Epic cycle to ensure near-zero human intervention in the later stages.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
