Valdr

What is Valdr?

AI agents are impressive in demos. Then you try to use them for real work — and everything falls apart. Context disappears between sessions. Conventions get ignored. Nobody can explain why something changed or who approved it.

Valdr is the layer where you manage, observe, and govern agent work — so it compounds instead of resets.

Think of it like a control plane for AI agents: it doesn’t run the agents themselves, but it structures how they run — with plans that persist, tasks that trace to requirements, capabilities that encode your expertise, and sessions that capture evidence.

Every agent run is scoped, logged, reviewable, and connected to the plan that created it. Nothing disappears into chat history. Nothing ships without a trail.

This is not an AI toy. It is infrastructure for using agents without losing control.

Why Valdr exists

CLI agents and IDE copilots plan in memory, then discard everything when the session ends. Context you painstakingly built? Gone. The reasoning behind a decision? Lost. Your team’s conventions? Re-explained every session.

The moment you add:

• more than one agent
• more than one person
• reviews, handoffs, or validation
• or the need to explain why something changed

…prompt files and copy-pasted scripts collapse.

Valdr exists because engineers need a way to use agents without creating invisible risk.

It turns agent work into structured execution with:

• Plans
  explicit plans that persist beyond sessions
• Tasks
  traceable tasks linked to requirements
• Reviews
  enforced review points
• Capabilities
  encoded expertise that agents always follow

You can see what happened, who approved it, and why it was accepted — weeks later, not just while it’s fresh in your head.

What Valdr replaces

It replaces:

• ❌ prompt folders no one trusts
• ❌ agent runs no one can explain later
• ❌ “it worked last time” automation
• ❌ context you re-explain every session
• ❌ tribal knowledge that lives in your head instead of agent prompts

Without Valdr, you are the context store, the reviewer, and the memory — until that breaks.

Why engineers use Valdr

Engineers don’t adopt Valdr to “run agents.”

They adopt it to:

• ✅ stop firefighting brittle automation
• ✅ encode institutional knowledge once, not re-explain every session
• ✅ make agent behavior visible and reviewable
• ✅ ship faster without guessing what broke

This is what predictable delivery looks like when agents are involved.

Who Valdr is for

If that’s what you want, Valdr will feel heavy — and that’s the point.

Where the value compounds

Most AI tools reset every time you start a new session. They forget what you decided last week, how your codebase works, why a change was approved, and what went wrong before.

That’s why agent work feels impressive — but fragile.

It does that by layering four things most tools treat as disposable:

Individually, each layer helps. Together, they create something rare in AI workflows: predictable outcomes over time.

What this looks like in practice

Here’s a concrete example: adding pagination to an API endpoint.

Without Valdr:

1. You open Claude/Cursor and explain what you need
2. The agent writes code — maybe ignoring your team’s pagination pattern
3. You fix it, re-prompt, iterate
4. A week later, someone asks “why did we do it this way?” — no answer exists
5. Next time, you re-explain the same conventions from scratch

With Valdr:

1. Your api.conventions capability already encodes your pagination pattern — the agent knows it automatically
2. You describe the feature in the Planner; it generates a plan with requirements and acceptance criteria
3. Tasks are created, each linked to specific requirements
4. The agent writes code that follows your conventions without being told
5. The session captures what happened — decisions are traceable
6. Next time, the capability still applies. The plan is still there to reference.

The difference isn’t speed — it’s accumulated context. Every capability you encode, every plan you create, every session you run makes the next one better.

Core principles

Where to start

Once you see agent output turn into auditable artifacts instead of chat transcripts, it’s hard to go back.

What we’re focused on (and what we’re not)