The AI Tech Stack I'd Pick for 2027: 7 Layers, From Coding Agents to Agentic Infrastructure
Blog post from Qovery
A proposed 2027 AI development stack is organized into seven interoperable layers: models and inference, IDE coding agents, AI code review and deterministic quality gates, agent orchestration and MCP tooling, retrieval and data, evaluations and observability, and agentic infrastructure for deployment. It argues that as coding agents such as Cursor, GitHub Copilot, Claude Code, Windsurf, and Devin automate more implementation work, the main challenges shift toward reviewing, testing, deploying, monitoring, and safely reversing agent-produced changes. The piece recommends selecting swappable tools connected through standards such as MCP, Git, OpenAI-compatible APIs, and OpenTelemetry, while retaining human accountability for architecture and security decisions. It emphasizes isolated preview environments, scoped permissions, policy checks, audit logs, short-lived identities, and CI/CD-controlled production releases rather than granting agents direct cloud credentials. The author presents Qovery as an example of agentic infrastructure that can provision and automatically tear down environments within a customer’s cloud account, alongside alternatives including DIY Kubernetes and Terraform, PaaS platforms, serverless containers, and other internal developer platforms. It also recommends tracking task success, cost per resolved task, human intervention, and change failure rates through continuous evaluations and observability, and suggests progressively more complex stacks for startups, scale-ups, and enterprises.
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