> For the complete documentation index, see [llms.txt](https://docs.palliora.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.palliora.org/ai/learn/concepts/provenance.md). # Provenance A provenance record is what a Palliora AI run leaves behind: which model ran, which tools were called, and a reference to the Compute Contract that coordinated each one. It's a byproduct of how Palliora AI already works, not a separate system bolted on afterward. ## Why it exists An AI agent can act on someone's behalf: answering, searching, calling tools, writing code. Without a record of what actually happened, the only account of an agent's actions is a log the platform that ran it controls, and can edit. A provenance record is independent of that platform, because it's built from agreements recorded on Palliora's coordination ledger, not from the platform's own logs. ## How it's built Every model call and every tool call in Palliora AI runs as its own [Compute Contract](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/compute-contracts.md): what ran, on what input, under what terms, agreed before execution starts. A single chat turn, or a longer agent run, is usually more than one of these: a model call, then a tool call the model asked for, then another model call with the tool's result folded in. Linking that chain of contracts together produces a provenance record for the whole turn, not just one step, using references already present in each contract rather than adding anything new to generate it. ## What it tells you * **Which model ran.** The model identifier used in the underlying [Compute Contract](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/compute-contracts.md). * **Which tools were called**, and in what order, when a run involves more than one step. * **A reference to each agreement** that coordinated a step, so a claim about what ran can be checked against the coordination ledger instead of taken on trust. ## Example A user asks the Palliora AI demo app, "What's the latest PALI price, and is it up this week?" Answering needs current data, so the run looks like this: 1. **Model call 1.** The model reads the question and decides it needs a web search. This is a Compute Contract: model, prompt, and the decision to call a tool. 2. **Tool call.** A web search runs as its own Compute Contract: which tool, what query, what came back. 3. **Model call 2.** The search result goes back into the conversation. The model reads it and writes the final answer. Another Compute Contract, referencing the first two. The provenance record for this turn links all three: `gemma3:1b` ran twice, a web search tool ran once in between, and each step references the agreement that coordinated it. Anyone can check that against the coordination ledger. Nothing about the answer required trusting the platform's own account of what happened. ## How it fits into Palliora Provenance is one application of [Compute Contracts](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/compute-contracts.md) and the [Guardian network](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/guardian.md), not a separate mechanism. Anything that already gets a Compute Contract, an AI run included, gets a provenance trail for free. See [Palliora AI](/ai/readme.md) for how this fits into the product as a whole. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.palliora.org/ai/learn/concepts/provenance.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.