From AI Chaos to Context Engineering: Lessons from Building Packmind OSS

At Packmind, over the past year, AI coding assistants like Copilot, Cursor, Claude Code, and Kiro have become part of our daily workflows. About 65% of our commits are now done by AI agents.

They make us more productive but also expose a new kind of complexity.

We kept running into the same question across teams and projects:

How do we make sure all these assistants share the same understanding of our codebase — our patterns, rules, and decisions? And more importantly, how do we ensure they actually follow them?

That question led us to explore Context Engineering — the practice of capturing, structuring, and maintaining the knowledge that guides AI coding.

This post shares what we learned, and the open-source tool we built to help.

The Tw…

At Packmind, over the past year, AI coding assistants like Copilot, Cursor, Claude Code, and Kiro have become part of our daily workflows. About 65% of our commits are now done by AI agents.

They make us more productive but also expose a new kind of complexity.

We kept running into the same question across teams and projects:

How do we make sure all these assistants share the same understanding of our codebase — our patterns, rules, and decisions? And more importantly, how do we ensure they actually follow them?

That question led us to explore Context Engineering — the practice of capturing, structuring, and maintaining the knowledge that guides AI coding.

This post shares what we learned, and the open-source tool we built to help.

The Two Big Problems We Kept Seeing

1️⃣ The Blank Context Problem

Most teams don’t know where to start.

What should go into an AI context file — naming rules? architecture patterns? design principles?

A lot of that knowledge lives in people’s heads, Slack threads, or wikis.

Without a shared source of truth, every assistant codes a little differently.

2️⃣ The Consistency at Scale Problem

Even when you do capture context, it quickly drifts.

A rule changes in one repo but not another.

Copilot uses an outdated version of your standards.

Cursor gets a different prompt.

Suddenly, you’re debugging not just your app — but your assistants’ behavior.

What We Built: Packmind OSS

We built Packmind OSS to make Context Engineering practical.

It’s an open-source framework that helps you create, scale, and govern your engineering playbook — the shared context behind your AI code.

Here’s the idea in one sentence:

Your rules, decisions, and prompts become a living “Context Database” that every AI assistant can sync with.

⚡ Try It in 30 Seconds

You can get started locally or in the cloud:

Option 1 — Try in the cloud (fastest): Sign-up Option 2 — Run locally using Docker Compose or Kubernetes

🧠 Why Context Engineering Matters

We think Context Engineering could be the missing layer in AI development.

Just like CI/CD brought consistency to releases, context governance could bring consistency to AI-generated code.

It’s not about control — it’s about trust and repeatability in how AI writes code for your team.

💬 Let’s Talk About It

This is still early.

We’re learning a ton from the community and refining the OSS project as teams adopt it.

If your team is experimenting with AI-assisted development, we’d love to hear:

How are you handling context today?
Where does your knowledge live — and how do you keep it aligned across agents?

Repo’s here if you want to explore or contribute:

👉 https://github.com/PackmindHub/packmind

We believe every engineering team deserves a way to scale AI coding without losing their standards.

Packmind OSS is our contribution to that journey.