> ## 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`. # Let an agent drive Autumn > Give a coding agent the whole task surface: generate a spec, start it, and manage the loop. Autumn's caller does not have to be your code. An agent with the [Skill](/docs/skill) installed can run the entire loop itself: turn a user's request into a task spec, start it, wait, and report the rows. This recipe is the shape of that handoff. ## 1. Install the skill ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}} npx skills add https://www.autumn.ai ``` The skill teaches the agent the defaults that matter: `clarify: false`, keep the `task_id`, continue instead of restarting, and read rows before reporting results. Other install paths are on the [Skill](/docs/skill) page. ## 2. Let the agent write the spec, not just the prompt An agent relaying a user's words loses their specifics. Instead, have it fetch `GET /task/metaprompt`, which returns the task spec schema plus a metaprompt for caller-side planning, and generate a clean `task.json`-style spec: ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}} curl -sS "https://api.autumn.ai/task/metaprompt" \ -H "X-API-Key: $AUTUMN_API_KEY" ``` The agent fills in `brief`, the output `schema`, and `target_count` from the user's request, then starts with `POST /task/start`. A spec survives the relay; a paraphrase does not. [Writing task prompts](/docs/best-practices/prompting) is the rubric to hold it to. **Decision:** for a one-off question the user typed, plain `POST /task` with their words passed through verbatim is fine. Reach for the metaprompt when the agent is constructing the task on the user's behalf. ## 3. Wait without hallucinating The two rules that keep an agent honest while a task runs: * A start response only means the task was accepted. Poll `GET /task/{task_id}` with the full terminal check from [Task lifecycle](/docs/concepts/lifecycle) before claiming anything. * Report from the rows, never from status text. `GET /task/{task_id}/output` is the only source of results. ## 4. Route refinements back to the same task When the user says "add a column" or "find more", the agent should continue the existing task, restating the goal as one clear instruction: ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}} curl -sS -X POST "https://api.autumn.ai/task/$TASK_ID/continue" \ -H "X-API-Key: $AUTUMN_API_KEY" \ -H "Content-Type: application/json" \ -d '{"message": "Add each company'\''s founder LinkedIn URL to every existing row."}' ``` A `409` here means a turn is still in flight; the agent should wait and retry rather than starting a duplicate task. See [Errors](/docs/api/errors). ## Guardrails worth writing into the agent's instructions * Default to `clarify: false`; the agent is rarely there to answer a blocking question. * Never print the API key, in output or in generated code. * One task per goal. Refinements continue it; only an unrelated goal starts a new one. ## Related Install paths and the ready-made prompt to pair with it. The one-link version for coding agents building against Autumn.