New — Argy Code
Argy Code — The Governed AI Coding Agent
A vibe‑coding agent (CLI + IDE) built into Argy: it guides you step by step, uses enterprise context, and stays aligned with golden paths and policies.
Useful links: Argy AI · Platform Engineering · Argy Chat · Blog
Key Benefits
Move faster without giving up governance: speed, quality, and compliance.
Governed vibe coding
An incremental workflow: the agent proposes, you validate, and outcomes stay under control.
Aligned with golden paths
Generated code follows supported and secure-by-default paths of your platform.
Enterprise context (RAG)
RAG (Retrieval-Augmented Generation) augments prompts with passages retrieved from your documents to deliver grounded, context-aware responses. across internal docs, schemas, and standards for relevant, consistent output.
Use Cases
From generation to maintenance—within an enterprise framework.
Generate scaffolding and speed up kickoff
- • Project structure and components
- • Coherent configuration examples
- • Context-aware documentation
Debugging and assisted fixes
- • Guided error analysis
- • Iterative fix proposals
- • Reduced regressions via explicit workflow
Quality & standardization
- • Patterns and internal conventions
- • Help with tests and docs
- • Fewer errors and drift
Onboarding and knowledge transfer
- • “How do we do it here?” explained in context
- • Answers grounded in internal documentation
- • Faster adoption of golden paths
How It Works (high level)
Argy Code combines an agentic coding experience with an enterprise governance framework—maximizing speed while keeping guardrails.
- 1) Intent — you describe the ticket (or goal) and constraints.
- 2) Incremental plan — the agent proposes a step sequence (e.g. scaffolding, implementation, tests, docs).
- 3) Validate each step — you stay in control of what is applied.
- 4) Context & governance — retrieval (RAG (Retrieval-Augmented Generation) augments prompts with passages retrieved from your documents to deliver grounded, context-aware responses.) leverages internal content, and AI requests flow through the governed LLM Gateway.
Examples & Callouts
Concrete outcomes, without a black box.
Before / After
Without an agent: manual research, conventions to rediscover, fixes discovered late. With Argy Code: a guided flow aligned with standards.
Guided CLI session (snippet)
FAQs
Common questions about Argy Code.
Is Argy Code a CLI tool, an IDE plugin, or both?⌃
Both: Argy Code can run in your terminal and integrate with your IDE. The goal is to keep developers in their natural A workflow is an orchestrated sequence of steps (e.g., provision → deploy → verify). Argy standardizes and observes flows to reduce cognitive load..
What does “governed vibe coding” mean?⌃
An interactive A workflow is an orchestrated sequence of steps (e.g., provision → deploy → verify). Argy standardizes and observes flows to reduce cognitive load. where the agent works step by step (plan, generate, refine) with explicit user validation and alignment with enterprise A golden path is a recommended, standardized, versioned delivery path (IaC, CI/CD, policies, runbooks). Goal: ship fast without drift, with consistent guardrails..
How does Argy Code reduce risks (data leakage, non‑compliant outputs)?⌃
AI requests are governed through the LLM Gateway: policies, usage limits, and auditability. Argy Code is designed to stay aligned with internal standards.
How is it different from JetBrains’ Junie or Anthropic’s Claude Code?⌃
Junie and Claude Code are general-purpose tools. Argy Code is native to the Argy platform: it leverages your A golden path is a recommended, standardized, versioned delivery path (IaC, CI/CD, policies, runbooks). Goal: ship fast without drift, with consistent guardrails. and internal knowledge (RAG (Retrieval-Augmented Generation) augments prompts with passages retrieved from your documents to deliver grounded, context-aware responses.) with centralized governance.
European SaaS
GDPR compliant & hosted in EU
No Lock-in
Built on open standards
API-First
Everything is automatable
Ready to turn AI into an enterprise operating system?
Share your context (toolchain, constraints, org). We’ll propose a pragmatic rollout that makes AI governed, scalable, and sovereign.