The demo that delighted ten people falls over — or bills $30K a month — at ten thousand. Learn each move before you need it.
Your agent works. Then a hundred people arrive, and a prompt edit quietly breaks answers that nobody notices for a week. Then a thousand, and your model provider starts refusing requests. Then ten thousand, and the invoice reads $30,000 a month. This course walks that growth curve one milestone at a time and hands you the fix before you hit each wall. You'll stand up a day-one stack with login, audit logs, and hard spend caps; move prompts into git behind a continuous-integration gate that blocks regressions; add shared rate limits across servers with Redis; cut spend by half or more using prompt caching, model routing, and batch APIs; push slow work onto queues with safe retries; run two model providers with real failover; scale vector search past the point where pgvector stops coping; and build the operations layer — capacity math, budget alarms, on-call rotations, reliability targets, canary prompt releases, rollback drills, incident severities, multi-region. Each module ends in a shipped artifact, not a summary.
Built by Lakshya Kumar
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One heavy customer can starve everyone else, and provider 429s cascade fast. Shared limits and async writes keep the hot path quick.
At 10,000 daily users the same workload costs $30K or $5K a month at identical eval scores. Four levers decide which one you pay.
A three-minute research task should never hold a connection open. Queue it, and retry safely without ever doing the work twice.
One provider's outage should not become your outage. Send cheap steps to small models, keep a tested fallback, A/B every upgrade.
Most teams ride pgvector for years. Learn the four signals that say it's time to shard, cache, or move — before p99 latency doubles.
Work out how much you need before traffic tells you, and promise only the reliability targets a customer would actually notice.
Code, prompts, models, and configs each need their own release path — and a rollback you have rehearsed, not one you invent at 2am.
At this size trouble arrives as incidents, abuse, and auditors. This is the cadence that keeps quality flat for the next year.
Complete 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.