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.

Fast path: Go agent runtime starter pack.

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

Related hub: AI agent runtime — definition and production failure modes for the loop itself.

Post What you’ll learn
What Is an AI Agent Runtime? Plain definition of the production agent loop
Go vs Python for AI Agents — Why We Chose Go Language decision for a production agent runtime
Go Platform Architecture at Speed — Without Drowning Growing a Go codebase fast without drowning in complexity
AI Agent Runtime vs Platform — Why We Split Them CLI agent vs enterprise multi-tenant platform
Prompt Caching for AI Agents Is an Architecture Problem Stable context, early compaction, isolated helpers, and delegated budgets
Claim-Aware Evidence Packing Pack verifier bags around claim tokens; fail open on truncation; keep the runtime domain-agnostic

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.

What is an AI agent runtime vs a platform?

The runtime is the Go loop that plans, calls tools, and manages context. The platform adds tenancy, policy, durable workflows, and IaC-configured agents — see what-is-an-ai-agent-runtime, why-go, and aiden-platform.

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.