Production LLM engineering
Prompt design, retrieval-augmented generation, and tool-use orchestration.
About Arch Agent
We build versioned, reviewable context for AI coding agents - so the agents your team relies on work from a single, governed source of truth instead of drifting prompts pasted between tools.
Our mission
AI coding tools are only as good as the context you give them.
Every team we talked to was solving the same problem in different ways - copy-pasted prompts, ad-hoc rule files, screenshots of architecture diagrams, and Slack messages summarising decisions made six months ago. None of it was reviewable. None of it was versioned. None of it survived a tool change.
Arch Agent treats agent context the way mature engineering organisations treat source code: versioned in Git, reviewed in pull requests, deployed across multiple AI tools from a single canonical source, and rolled back the moment something regresses.
Why we're authoritative on agent context
Our work sits at the intersection of three mature disciplines.
Platform engineering
Versioned configuration, RBAC, and multi-tenant data isolation.
Developer experience
Review workflows, deployment pipelines, and observability.
We support every major coding agent and we have spent years inside their ecosystems building real production workflows. That is the experience we bring to every context artefact we generate, review, and ship.
- Supports
- Claude Code
- Cursor
- GitHub Copilot
- Codex
- Gemini
- Aider
We are opinionated about what good looks like
- Zero silent fallbacks
- Explicit failure modes
- Row-level isolation by default
- Audit every byte that reaches a model
Founder
Yishai Tamir
Founder, Arch Agent
I started Arch Agent because I kept hitting the same wall inside Claude Code, Cursor, and Copilot: the model is capable, but the context I hand it is brittle, copy-pasted, and impossible to audit. Every team I talked to was rebuilding the same fragile prompt scaffolding from scratch, and watching it silently rot the moment the codebase moved. I am convinced that context engineering - not bigger models - is what separates AI coding tools that ship from ones that mislead. So I build Arch Agent the way I want the rest of the stack to behave: service-first, with zero silent fallbacks and explicit failure modes, because an AI that quietly returns the wrong answer is worse than one that refuses to guess. I lead product and engineering, and I write most of what you read here.
Get in touch
Questions about the product, the roadmap, or whether Arch Agent is right for your team? We read every message.
Email the teamOr reach us directly at support@arch-agent.ai