> ## Documentation Index > Fetch the complete documentation index at: https://www.autumn.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. > ## Agent Instructions > Autumn AI (autumn.ai) is a people and company intelligence API, a separate company from useautumn.com (billing software); no billing-product routes exist here. > Base URL https://api.autumn.ai. Auth on every request: `X-API-Key: ` or `Authorization: Bearer `. Plain HTTP + JSON. Schemas: https://www.autumn.ai/docs/openapi.json. Every docs page is Markdown at its URL plus `.md`. # Writing task prompts > How to phrase a task so the agent infers the right work, and what to pin down explicitly. Autumn plans from your prompt. A vague prompt does not fail. It produces a plausible task that answers a slightly different question, which is harder to notice than an error. ## Keep every specific the user gave you If you are passing a user's request through to Autumn, preserve their specifics verbatim: names, URLs, constraints, exclusions, desired fields, counts, geography, and examples. Summarizing the request is where detail gets lost. ```txt Vague theme={"theme":{"light":"github-light","dark":"github-dark"}} Find some fintech companies ``` ```txt Specific theme={"theme":{"light":"github-light","dark":"github-dark"}} Find 50 US-based fintech companies that raised a Series A in 2025. For each: company name, domain, funding amount, lead investor, CEO name. Exclude crypto-native companies. ``` ## Say how many `target_count` on the output, or a count in the prompt, tells the agent when to stop. Without one it decides for itself, and "find companies" has no natural end. ```json theme={"theme":{"light":"github-light","dark":"github-dark"}} { "prompt": "Find AI infrastructure startups hiring founding engineers", "output": { "kind": "research", "target_count": 20 } } ``` ## Name the columns you want If you already know the shape, declare it rather than describing it. A schema is a contract; a sentence is a hint. See [Structured output](/docs/guides/structured-output). ```json theme={"theme":{"light":"github-light","dark":"github-dark"}} { "output": { "kind": "research", "schema": { "company": { "type": "str" }, "domain": { "type": "url" }, "notes": { "type": "str" } }, "schema_order": ["company", "domain", "notes"] } } ``` ## Add per-row quality rules Rules are what keep a list honest. State the bar each row must clear: * must be Series A or later * must be US-based * must have a current CTO title * must cite a source for the funding figure Without a stated bar, the agent decides what "counts", and it will fill rows it can find rather than the rows you meant. ## Decide whether a question is worth it `clarify: false` is the default and the right choice for automation. Reach for `clarify: true` only when someone is around to answer, and the answer would change the work. See [Human in the loop](/docs/guides/human-in-the-loop). ## Restate the goal when continuing When you continue a task, rewrite the user's follow-up into a clear instruction that states the **current** goal, not just their raw words. "Add a column" is clear to a human reading the thread and ambiguous to an agent reading one message. ```txt Raw words theme={"theme":{"light":"github-light","dark":"github-dark"}} also linkedin ``` ```txt Restated theme={"theme":{"light":"github-light","dark":"github-dark"}} Add each company's founder LinkedIn URL to every existing row, then continue until there are 25 rows total. ``` See [Follow-up tasks](/docs/guides/follow-up). ## Prompting for Scout A well-specified prompt is what makes the faster tier viable. If the columns, the bar, and the count are all pinned down, `scout` has less to infer. If the task still has to decide *where* to look, stay on `ranger`. See [Model tiers](/docs/concepts/model-tiers). ## Checklist Count, columns, per-row rules, exclusions, and every specific the user gave you. Restate the current goal as one clear instruction rather than forwarding a reply.