Every AI framework defaults to Python. We built ours in Go — and we’d do it again for production enterprise agents. These posts explain the trade-offs, the architecture patterns, and when you shouldn’t follow our path.

Part of the series Building an Enterprise AI Agent Platform in Go.

Post What you’ll learn
Go vs Python for AI Agents — Why We Chose Go Language decision for a production agent runtime
Architecture at Speed Without Drowning Growing a Go codebase fast without drowning in complexity
Why We Split Runtime From Platform CLI agent vs enterprise multi-tenant platform

FAQ

Why use Go instead of Python for AI agents?

Concurrency, single-binary deployment, and static typing for tool middleware and workflow gates. Python wins for research and notebook iteration; Go wins for long-running production agent runtimes.

When should you not choose Go for agents?

When your team lacks Go depth, when you need tight HuggingFace or notebook integration, or when iteration speed on prompts matters more than runtime discipline.

How do you structure an enterprise agent platform in Go?

Split the single-user agent runtime from the multi-tenant platform layer — policy, tenancy, durable workflows, and IaC-configured agents belong in the platform, not the core loop.