The AI Workflows of Every's Six Engineers (every.to)

Every asked its six engineers to map their day-to-day AI stacks and the result is a practical blueprint for how small teams run multiple AI products: highly personalized, multi-model pipelines glued together with orchestration layers, documentation integrations, and disciplined human guardrails. Engineers routinely pair models (Claude Code vs. Codex/GPT-5 Codex) to exploit different strengths—Claude for explanatory, iterative work and Codex for literal, precise fixes—while tools like Droid and Warp provide a unified CLI for running Anthropic and OpenAI models side-by-side. Integrations such as Figma MCP (letting models read design systems directly) and Context 7 MCP (pulling versioned docs and code examples into prompts) reduce friction and improve provenance; GitHub and a “work” command translate plans into agent tasks and automated PR workflows. Technically this highlights three repeatable patterns: multi-model ensembles for complementary behaviors, prompt-driven orchestration that pulls canonical context into agents, and lightweight memory/monitoring (a rolling “learnings” doc and AgentWatch notifications) to manage parallel sessions. Features are triaged into small/medium/large flows with automated agent work and human review loops—reinforcing that shipping still relies on human planning, code review, and explicit guardrails (timeboxing, build vs. exploration modes). For the AI/ML community, Every’s setup is a concrete case study in productionizing agentic workflows: combine best‑of‑breed models, inject authoritative context, automate routine tasks, and keep humans tightly in the loop.