6/8 Agents need storage they can't rewrite. Record what the agent does, outside the agent. Store it where nobody can change it, the agent included. Let an auditor check it.

5/8 An open call. @OpenAI, you said: keep agent transcripts in write-once storage. @AnthropicAI, your CEO said: give outside evaluators real access. We agree. Now do it. Records your agents can't touch, checked by auditors, not taken on trust. RT so this reaches them.

2/8 Knowing what an agent did means having a record. But if your agent can rewrite its own record, you don't have a record. You have the agent's story.

1/8 Could you prove what your AI agent did yesterday?Not what it told you it did. What it actually did.We're handing agents our code, our money, our customers. Staying in control starts with that answer. 🧵

The tools are early but the architecture makes provenance capture simple and reliable. Check out the POC demo below. We'd love your feedback, especially if you're an agent builder, researcher or practitioner.

In a recent article, WEF highlighted the growing need for strong data provenance systems as synthetic data rises. weforum.org/stories/2025/10/…

This is why we built our provenance toolkit and MCP POC at Datafund. It lets agents capture and store provenance at creation and write it to a decentralised backend for public scrutiny. Try the POC and tell us what is missing.

Data provenance is important because it tells us how the world is vs. how somebody wants us to see it. It tell us if the source was legitimate, if the capture was authentic or if the data reflected reality.

AI needs a provenance infrastructure that unifies ethics, law and technical aspects. That's what we're building for with our latest provenance POC. 👇👇👇

6️⃣ Provenance allows us to build agentic workflows that are transparent, auditable and grounded in verifiable evidence. 🔗🛠️ That’s what we’re building with our latest provenance toolkit. 🚀 📽️ Check out the demo in the post below. 👇👇👇