> 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/tips-and-best-practices.md). # Agent usage guidelines Practical habits for getting dependable operational work from the Agent. Good Agent use is less about prompt tricks and more about **clear outcomes, trustworthy evidence, explicit boundaries and reusable corrections**. ## Start with the business outcome Describe the job in operational language. Tell the Agent what should be true when the work is finished rather than scripting every tool call. ## Make source authority clear If sources can disagree, say which one wins. Examples: * ERP owns the current order quantity. * The signed policy outranks an old presentation. * Customer-specific rules outrank generic industry guidance. * A remembered preference does not outrank a current system record. ## Ask for evidence For important work, ask the Agent to make the basis of its answer inspectable. Useful evidence can include the records it checked, the document supporting a value, the calculation behind a recommendation, the file it produced or the system change that now exists. A fluent answer is not itself proof. ## Make the human boundary explicit Say what the Agent may decide, what it may prepare and what still needs a person. If the Agent reaches a point that genuinely needs judgement, let it ask rather than guess. Some boundaries can also be enforced through approvals or Workflow Requests. ## Give useful corrections When something is wrong, include: * what is wrong; * where it appears; * which source or example should have been used; * what you expected instead. For generated documents, inspect the document itself before it leaves the organisation. ## Keep exceptions separate from normal rules If this case should intentionally differ from the normal method, say so. If the same correction keeps returning, move it to the narrowest durable layer that owns it—for example the Skill, maintained data, Knowledge, Memory, Project instructions, Workflow or approval setup. ## Use Skills for recurring methods A Skill is the right place for a reusable way of doing work without turning the whole task into a rigid sequence. Let the Agent use available Skills when they fit. Invoke one deliberately when you need a specific method followed. ## Use Workflows for stable background paths When the route becomes sufficiently known and repeatability matters more than interpretation, move that part into a Workflow. A common pattern is: **Agent investigation → Workflow execution → human decision if needed → Workflow continues → result back to the operator** ## Improve from real use Response feedback, session feedback, repeated corrections, failed cases and recurring manual intervention are useful evidence. Use that evidence to improve the maintained setup. Do not turn every bad result into another sentence in a giant prompt. ## When something goes wrong Diagnose the layer before changing the setup or retrying repeatedly. * Wrong answer or output → inspect the source evidence, activity and result. * System action unavailable → check Project scope, Connection and permissions. * Same mistake recurring → improve the durable method or source. * Failed response → use **Retry** when it is safe to repeat the Agent response. For deeper investigation, see [Troubleshooting and support](/reference/troubleshooting-and-support.md). For consequential work, verify the resulting state in the system or maintained source that owns it. --- # 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/tips-and-best-practices.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.