Context
Airtable wants to support asynchronous AI workflows in Interfaces and Automations: users trigger an AI task from a record (e.g. summarize notes, draft outreach, extract fields from attachments), and the result should appear back in the right base reliably even if generation takes seconds or retries are needed.
Constraints
- User-facing acknowledgment in under 300ms after trigger
- p95 end-to-end completion under 12s for standard jobs; p99 under 30s
- Cost ceiling: $0.03 per completed job on average at 5M jobs/month
- Hallucination / unsupported-claim rate under 2% on a labeled eval set
- Prompt-injection success rate under 0.5% for adversarial attachments and linked records
- Must be idempotent: duplicate triggers must not write duplicate outputs
- Must respect Airtable record permissions and avoid leaking data across bases or fields
Available Resources
- Airtable records, comments, attachments, linked records, and field metadata
- Airtable Automations as a trigger surface and Airtable Interfaces as a result surface
- Approved LLM APIs, embedding models, and a managed queue / workflow engine
- Historical AI job logs with prompt, inputs, outputs, retries, latency, and user edits
- 2,000 labeled examples across summarization, extraction, and drafting tasks
Task
- Design the asynchronous architecture from trigger to durable result write-back, including queueing, retries, idempotency, status tracking, and user notifications in Airtable.
- Define the prompting and structured-output strategy for multi-step jobs that may read record context, attachments, and linked records while minimizing hallucinations.
- Propose an evaluation plan first: offline quality/safety evals and online reliability/product metrics, including how you measure bad writes and stale or duplicated outputs.
- Estimate cost and latency at target volume, and explain how you would tier models or route jobs by complexity.
- Identify major failure modes, especially prompt injection from record content or attachments, partial writes, stale jobs, and permission mistakes, and describe mitigations.
