> 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/understand-lleverage/readme/understanding-before-automating.md). # Process discovery The name of a process is usually much simpler than the process itself. “Process orders.” “Do invoice matching.” “Plan production.” “Follow suppliers.” Each can hide a chain of lookups, document interpretation, local rules, spreadsheets, exceptions and experienced judgement. That is why understanding the operation is part of building the automation. ## Process definition For each recurring job, establish: * **Trigger** - what causes the work to exist: email, file, system event, schedule or person? * **Input** - what documents, records or messages arrive? * **Entities** - which customer, supplier, product, order, contract or shipment must be resolved? * **Rules** - what decisions are deterministic and where are those rules maintained today? * **Judgement** - what does an experienced person decide that is not encoded anywhere? * **Systems** - where is the authoritative record, and where must the final result be written? * **Exceptions** - what causes the ordinary path to stop or branch? * **Outcome** - what must be true in the business system for the job to count as complete? A successful API call or staging write is not automatically the same thing as a completed operational outcome. The business post-condition matters. ## Operational terminology People say “find the customer,” “match the article,” “chase the supplier,” “fit the week on the lines,” “check the invoice,” “book the freight,” or “prove it was delivered.” Those phrases contain useful design information about where the hard part sits. The process inventory behind these docs is evidence, not a canonical taxonomy. It shows recurring domains and patterns across our target companies, but every implementation still needs to be grounded in that company's systems, rules and people. See Operational examples for the current non-exhaustive process map. ## Source evidence Operational truth can be distributed across ERP and CRM records, email and supplier correspondence, spreadsheets and local calculations, procedures and contracts, files and engineering documents, interviews and user corrections, and previous execution history. Those sources do not all carry the same authority. An Agent inference, an old spreadsheet and the current ERP value should not silently become interchangeable. ## Distinguish configuration, execution and outcome Three states are easy to confuse: 1. **Configured** - a Workflow, schedule or Connection exists. 2. **Executed** - the run actually happened. 3. **Completed operationally** - the intended business post-condition is true. Monitoring can prove execution. The process design should define what proves the business outcome. ## Scope You do not need a perfect enterprise process map before improving anything. The broader Autonomous Back Office lifecycle is: > **Understand -> Automate -> Operate -> Expand** Within a live operation, improvement is continuous: observe what happens, measure the outcome, learn from failures and operator feedback, and feed that evidence into the next reviewed change. Start with a bounded recurring job, make the evidence and human boundaries explicit, automate it deliberately, operate it against real work, and expand once the capability is dependable enough to extend. --- # 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/understand-lleverage/readme/understanding-before-automating.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.