> For the complete documentation index, see [llms.txt](https://docs.lleverage.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lleverage.ai/run-operations/agent/agent-faq/access-control-and-trust.md). # Access and permissions Common questions about Agent access, permissions, approvals and trust. ## What systems and data can the Agent access? Only capabilities available to the current run. That depends on your access, Project or Channel scope, configured Connections and the permissions of the external account behind those Connections. ## Where do passwords and API keys go? Use the supported Connection and credential flow. Do not paste secrets into a conversation, Skill or Project instructions. ## Can the Agent change our ERP, CRM or other systems? Yes, when an authorised Connection exposes the required action and the current run is allowed to use it. Consequential actions can require approval. ## How do approvals work? The Agent can pause for your judgement, and configured write actions can require explicit approval. A typed reply can answer a pending write-access approval. It does not bypass the approval. ## What happens when the Agent gets something wrong? Correct the current task, then identify the cause if it is recurring: the Skill, source data, Memory, Knowledge, Connection or Workflow. Leave feedback when useful so builders can inspect the bad outcome with the execution evidence. ## How do we verify important work? Check the actual post-condition in the system that owns it—the ERP record, sent email, created file or other business result. See [Permissions and approvals](/run-operations/agent/agent-context/access-and-approvals.md) and [Working well with the Agent](/run-operations/agent/tips-and-best-practices.md). --- # 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.lleverage.ai/run-operations/agent/agent-faq/access-control-and-trust.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.