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Introduction

Felspar turns your company's policy documents into tested, approved decisions that every agent can share.

Felspar turns the policy documents your company already writes into Decision Packs: tested, approved, versioned decisions that every agent, workflow and app can ask for the same answer.

You keep writing policy in Google Docs. An assistant such as ChatGPT or Claude drafts a pack from the document. A person on your team reviews it and releases it. From then on, your agents stop deciding for themselves. They gather evidence, send it to Felspar, and get back the company’s decision, why, what to do next, and a receipt anyone can check.

What Felspar does

  • Turns a policy into a pack. Every question, answer, rule and next step in a pack quotes your document word for word. Nothing is paraphrased, so the pack can’t drift from what the policy says.
  • Tests it before anyone relies on it. A pack carries test cases. Felspar runs them, and flags gaps such as an answer the document never defines.
  • Makes approval explicit. A proposal does nothing until an approver reviews it and releases it. A release is immutable: it can be replaced by a newer one, never edited.
  • Decides for your agents. An agent sends the exact words its sources say. Felspar reads numbers and dates exactly, classifies text against the definitions in your policy, and applies your rules. The decision itself is plain code: the same evidence and the same release always give the same answer.
  • Says when it doesn’t know. When evidence is missing, stale or unclear, the result says so and names what would settle it. It never guesses.
  • Leaves a receipt. Every decision is tied to the exact release and evidence that produced it. The code that acts on a decision can ask Felspar to replay it and confirm it before acting.
  • Watches your documents. When someone edits a policy document, Felspar notices. Agents keep using the approved release until a person decides whether the edit needs a new one.
  • Shows the effect of a change before you make it. A backtest re-decides recent real cases under a proposed change and lists which decisions would flip and which agents would be affected.

Why it matters

The same company rule usually lives in many places: a prompt in one agent, a table in an enrichment workflow, a condition in CRM routing, a paragraph in a playbook. When the rule changes, someone has to find and update every copy. Some get missed. Two agents given the same facts give different answers, and nobody can say which version of the rule produced a given result.

Felspar keeps one approved copy of each decision, tied to the document it came from, and lets every connected agent use it. Change the document, review the change, release it, and every agent that follows the job gets it on its next call.

Why now

Companies are moving from one AI assistant to many agents: in ChatGPT and Claude, in agent frameworks, in automations and inside their own apps. Each one needs the same company judgment: who qualifies, which evidence counts, what is off limits, when to hand off to a person. Writing that judgment into each agent’s prompt doesn’t scale, and it can’t be audited.

Felspar is the shared layer for that judgment. It doesn’t replace your models, agents or tools. It gives them the same approved decisions.

Who it’s for

  • Builders who wire up agents and automations. You get four MCP tools (or the matching REST calls) that list your jobs, say what each needs, return the company’s decision and check what an agent wrote, plus a gate that checks the decision in code before a tool acts. Start with Register an agent and the Eve example.
  • Policy owners in support, legal, recruiting, compliance, marketing or sales operations who own the rules. You keep writing in Google Docs, review what a change would do, and decide what agents follow. Start with the Quickstart.
  • AI assistants and agents reading these docs. Every page is plain Markdown. The full index is at /llms.txt and the full text at /llms-full.txt.

What Felspar is not

Felspar is not an agent runtime, a model provider, a prompt library or a knowledge base. It doesn’t run your agents, write your emails or take actions. Your agents gather evidence and act; Felspar decides and proves what it decided. It can only govern agents that call it.

Next steps

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