What a 2-week AI automation sprint actually delivers
Fixed-scope agent work for India tech teams — audit, one production workflow, guardrails, and a runbook. Not a strategy deck.
Most “AI sprints” fail because the scope is fuzzy: build something with LLMs instead of remove one recurring workflow. Here is what I commit to in a two-week automation engagement through Future Flow.
Week 0 — Discovery (before the clock starts)
We pick one workflow with a measurable before/after:
- Tier-1 support triage
- Internal doc Q&A with citations
- Account research briefs before sales calls
If we cannot name the owner, the approval step, and the success metric, we do not start the sprint.
Week 1 — Design + thin slice
Deliverables you can review async:
- Failure-mode map — where the model will hallucinate, leak data, or bypass humans.
- Tool contract — which APIs, queues, or spreadsheets the agent may touch.
- Thin slice in staging — one happy path end-to-end with logging.
No production traffic yet. Your lead engineer should be able to say “I would merge this approach” before week two.
Week 2 — Production path + runbook
- Human-in-the-loop gate for the first N days (Slack approve, ticket queue, or email draft).
- Observability: prompt version, tool calls, latency, fallback when the provider errors.
- Runbook: how to rotate keys, retrain on new docs, and when to escalate to a human.
What is explicitly out of scope
- Replacing your entire support team
- Unsupervised outbound email to customers
- “Autonomous coding” on production without review
Those are phase-two after trust exists.
Ready to scope?
Related: AI automation for a 20-person India team.