How to Build Secure AI Coding Agents with Cerebras and Docker Compose (www.docker.com)

Cerebras published a hands‑on demo showing how to build a portable, secure AI coding agent using Cerebras Cloud for high‑performance inference, Docker Compose, ADK‑Python, and the MCP (Microservice Control Plane) toolkit. The repo provides a ready-to-run stack: clone the project, add your CEREBRAS_API_KEY to .env, then docker compose up --build to launch an ADK‑Python agent UI at localhost:8000. The demo supports multi‑agent setups, routing between a local Qwen model and the Cerebras inference backend, and shows how agents call external “tools” exposed as MCP servers to read/write files, fetch docs, or run code. Technically, the key innovation is packaging custom toolservers (e.g., context7 docs and a node-code-sandbox) as long‑lived MCP servers and orchestrating isolated execution via Testcontainers. The sandbox is a Quarkus MCP server that programmatically spins up Node.js containers with networking disabled for strong containment; Testcontainers handles lifecycle and cleanup. The MCP catalog can pin Docker images by sha256 for reproducibility, and the gateway composes servers together. Implications: this pattern gives reproducible, auditable agent toolchains that mix local and remote models, enforces granular security (no network in sandboxes), and is extensible for production needs (controlled npm access, hardened images) while leveraging Cerebras for scaling heavy inference workloads.