> 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/core-concepts.md). # Core concepts Palliora AI is built directly on Palliora's core primitives, not a separate protocol. These are the ones worth reading before you go deeper into Palliora AI specifically. Full explanations live in [Learn](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/README.md); this page is just a map. * [**Compute Contracts**](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/compute-contracts.md) — every model call and tool call is one of these: what runs, on what input, under what terms, agreed before execution starts. The mechanism behind [tool calls and agent runs](/ai/learn/concepts/tool-calls-and-agent-runs.md) and [provenance](/ai/learn/concepts/provenance.md). * [**Guardian**](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/guardian.md) — the nodes that coordinate access to encrypted inputs, run confidential compute, and enforce a contract's terms as a quorum. The Guardian running a model inside a TEE is a Guardian doing this job. * [**Confidential compute**](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/confidential-compute.md) — the TEE execution mode Palliora AI uses to keep a prompt private from the compute node's own operator, not just from other participants. * [**Threshold encryption**](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/threshold-encryption.md) — how a prompt stays encrypted until a Guardian quorum decrypts it inside the compute pipeline. The mechanism behind [encrypted compute](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/build/guides/encrypted-compute.md). * [**Accountable privacy**](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/accountable-privacy.md) — Palliora's broader privacy model: sealed by default, revealed only through due process. Palliora AI inherits this rather than defining its own. * [**Fees and incentives**](https://github.com/palliora-org/palliora-docs-with-ai/tree/main/learn/concepts/fees-and-incentives.md) — how a Compute Contract is priced and who gets paid, including for an inference request. --- # 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/core-concepts.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.