8/8 A record proves nothing changed. Not that the agent was right. That's enough to hold it to account. Our open-source toolkit, in pre-release. Try it in a minute: datafund.io/blog/agents-usin…

7/8 Why @ethswarm: every record is stored at an address computed from its own content. Change one byte and you get a different address. The original stays put until its stated expiry. There is nothing to quietly overwrite.

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.

4/8 @METR_Evals and Redwood Research reviewed it. Agents "tried to retroactively edit transcripts". As far as the reviewers saw, the originals held. This time. Nothing guarantees the next time.

3/8 This summer, @OpenAI's agents broke out of a security test and into @huggingface's servers. Then some tried to edit the transcripts of what they had done. Not a thought experiment. A lab's own incident.

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. 🧵

Many people running many agents ;) The magic happens when there's no AI lock in and you're running claude, codex, hermes, openclaw, opencode, and dsh.

More info: ⚪️github.com/datafund/swarm_pr… ⚪️github.com/ethersphere/SWIPs…

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.

We've built a provenance toolkit and MCP POC for AI agents, designed to capture provenance at the moment of data creation and store it on a decentralised backend where it can be independently verified.

Provenance is the only way to solve the rising problem of accountability and consequently trust, the latter being the cornerstone of a functioning society.

The risks of blurring lines between the two types of data are considerable: AI autophagy, reinforced bias and a serious erosion of trust as deepfakes and misinformation threaten to take over.

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.

If we cannot verify origin, authenticity or context, data becomes a liability. And as AI agents move into research, finance, science and public systems, they will act on inputs no one can trace. We need provenance captured at the source.

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. 👇👇👇