u/Vektoris_AI_Workflow

We gave our agents a shared company memory in plain markdown. What we learned after 4 months

We're a small AI studio in Germany. Since June our whole company runs on one Obsidian vault that we and our Claude agents both read and write. It's at 600+ notes now, plain markdown in git with a small local search index on top.

A few things that turned out to matter more than we expected.

Agents can write but they don't get to decide. Every change they make shows up as a git diff and one of us approves it before it counts. Without that rule we wouldn't trust anything in there.

Numbers need a source. When our morning briefing agent reports a number it has to say where it came from, and if it can't find a source it writes "not verifiable" instead of guessing.

Raw material never gets edited. Call transcripts, PDFs and exports go into one folder and stay as they are. The agents write short notes from them somewhere else and link back, so you can always check what the original said.

It gets messy faster than you'd think. Our last check found 49 broken links and 59 notes missing basic metadata. A script finds it, an agent suggests fixes, we approve.

Decisions get their own note with what we decided, why, and what would make us change our mind. We have 21 of those now and they save a lot of "wait, why did we do this" conversations.

How are you handling shared memory for agents across a team? Do you let them write directly or do you gate it too?

We also made a free example version of this setup with a fictional company in it (MIT). Happy to share it if anyone wants to look.

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u/Vektoris_AI_Workflow — 6 days ago
▲ 0 r/mcp

I built an extraction tool that returns null instead of guessing — with a source quote per field

Sharing something I built to solve a problem that kept biting me: LLM extraction fills every field even when the value isn't there, and a hallucinated value is indistinguishable from a real one until you check by hand.

proofetch (HTTP API + MCP server) takes a source — PDF, URL, or text — plus a JSON schema, and returns schema-valid JSON where each field is either:

- backed by an exact quote from the source (with character span), or

- null + flagged, never guessed.

Verification is two-layer: deterministic grounding (is the value literally in the source?) + optional NLI to catch "right value, wrong field". Every result is sealed in a signed receipt (Ed25519 + hashes) so it's auditable later.

Billing is per verified field (€0.02) — unprovable fields cost nothing.

There's a no-signup live demo on the page and a free tier (50 fields, no card) if you want to test a real document. Genuinely after feedback — especially on whether grounding-only verification is enough for your use cases.

https://vektoris.de/proofetch

(MCP users: it's in the official registry and on Smithery — works as a remote MCP server, you pass your key as a bearer token / apiKey param.)

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u/Vektoris_AI_Workflow — 4 months ago