From your first user to your millionth. Cost, latency, observability, queues, multi-model, vector DBs, on-call, deploys, compliance — at every scale milestone.
A milestone-driven playbook for taking an agentic system from launch to 1M users. Day 0 (stack + cost caps + auth) → first 100 users (prompts in git + eval gates + observability) → 1k users (rate limits + distributed limits + async audit) → 10k users (caching + routing + batch) → queues for async work → multi-model + failover → vector DBs at scale → capacity + cost + on-call → deploy + rollback for prompts/models → 100k+ (incidents, sliced evals, compliance, multi-region, sustained quality). Built on what real production agents look like in 2026: Anthropic prompt caching, MCP, multi-provider routing, modern observability tooling (LangFuse / Phoenix / LangSmith), and the operational discipline behind teams that survive growth without burning out.
Built by Lakshya Kumar
Paste this into any AI chat. Fill in the bracketed parts with your context — you'll get back a straight answer on whether this belongs on your plate.
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Sign in to applyComplete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Take your real agent (or design one from scratch). Write a 12-month scaling plan: starting stack, milestones at 100/1k/10k/100k DAU, capacity math, cost projections, SLOs you'd defend, 3 things you'd do *wrong* on the first try. The plan is the artifact; the agent is the prop.
Compile a complete runbook pack for an agent at growth: incident severity table, cost-spike runbook, prompt-regression runbook, provider-outage runbook, abuse-detection playbook, multi-region failover plan. Submit the pack.
This course assumes you've completed it. If not, the modules on observability, evals, and safety here will feel rushed.
Modules 4 + 7 use this directly. The biggest single optimization you can ship.
I'm taking a "Scaling Agentic Systems" course that covers the journey from 1 user to 1M: Day-0 stack + cost caps, prompts in git + eval gates, rate limits + distributed limits, prompt caching + model routing + batch APIs, queues for async work, multi-model strategy + failover, vector DB at scale, capacity + cost + on-call, deploy + rollback for prompts and models, and the 100K+ discipline (incidents, sliced evals, abuse, compliance, multi-region, sustained quality). My context: 1. My current product / project is: [describe] 2. Current scale: [pre-launch / X DAU / Y queries per day] 3. My biggest scaling worry: [cost? latency? safety? incidents?] 4. Team size: [solo / small / medium] Given that, answer: - Which module should I prioritize? - Name 3 concrete wins this course would unlock for my situation. - Name 1 thing the course won't help with so I don't have wrong expectations. - If I only had 2 hours this week, which single technique gives me the biggest lift?
Pick an existing agent and apply this course's optimization stack: prompt caching, model routing, context trim, batch API, vector DB optimization, async work. Submit before/after dashboards, eval results, and the prioritized roadmap.
Prepare your agent for SOC2 Type II audit. Document audit log coverage, retention policies, access controls, change management, vendor reviews. Submit the doc pack + Vanta/Drata setup screenshots.
Take your agent multi-region (at least 2 regions). Implement: regional read replicas, edge routing, replica-lag handling for read-after-write, DR drill. Submit architecture doc, latency comparison, drill record.
Read it once; the patterns here build on it.