Most agent demos fall apart the first week they meet real traffic, real attackers, and a real bill. Build one that doesn't.
Your agent works beautifully in the notebook. Then a real user pastes something strange, a tool times out, a retrieved web page tells the model to email your customer list, and the monthly API bill arrives with four digits you didn't plan for. This course covers the whole distance from that prototype to something you can leave running. You'll hand-build the minimal agent loop before touching a framework, design tool schemas the model actually calls correctly, and connect external systems over MCP (Model Context Protocol). Then you add the parts demos skip: four layers of memory, plan-then-execute and ReAct-style reasoning, guardrails on input, output, and scope, and an eval harness that scores whole trajectories and blocks regressions in CI (continuous integration). Later modules cut spend with prompt caching and model routing, wire traces and metrics so you can debug a bad run, defend against direct and indirect prompt injection, and work through beta rollout, onboarding, and pricing. Python-first, with TypeScript where it matters. You finish with an agent shipped to real beta users and the numbers showing it held up.
Built by Lakshya Kumar
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Users quit when they have to re-explain everything. Build memory that recalls the right facts and forgets the ones you shouldn't keep.
Left alone, an agent will loop forever or quit halfway through. Give it a plan, a self-critic, a stop condition, and a human to ask.
One leaked key or one spill of personal data undoes a year of trust. Layer filters, scope limits, sandboxes, and audit logs first.
A prompt tweak that fixes one case quietly breaks five others. Score the whole path the agent took, and gate every merge on it.
An agent that feels cheap in testing can bill five figures a month at real volume. Cache, route, and budget before that happens.
When an agent misbehaves at 2am, logs alone won't tell you which of its twelve steps did it. Instrument the run end to end.
A web page your agent reads can hijack it into turning your own tools against you. Assume it will, and cap what it can reach.
Shipping isn't the finish line — it's when the real feedback starts arriving. Roll out gradually and build the loop that improves it.
Complete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Pick a real or realistic use case. Build the agent end-to-end through every module: tools, memory, plan-execute, guardrails, evals, observability, cost optimization, safety, shipping. Ship to ≥10 beta users for 2+ weeks. Submit the live URL, metrics, traces, and a 5-page writeup of what surfaced in beta.
Build a small reusable toolkit (npm/pypi package) implementing the core patterns: tool dispatcher with authz + audit, multi-layer memory, guardrail middleware, eval harness, tracing wrapper. Open-source it.
I'm taking a "Building Production Agents" course covering the practical 2026 stack: tool calling, MCP, memory layers, plan-then-execute, guardrails, multi-axis evals with CI gates, prompt caching + model routing, full observability, safety against prompt injection, and the launch + improvement cadence. My context: 1. My current product / project is: [describe] 2. Current agent state: [haven't built one / prototype / shipping to users] 3. My stack: [language, model provider] 4. My biggest agent problem: [hallucinations? cost? latency? safety?] 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?
Build the eval harness from Module 6 as a standalone tool: trajectory matcher, LLM-judge with calibration, CI gate, online metric integration. Document a sample integration.
Build the full safety stack (Module 5 + 9) for an agent: input/output filters, tool authz, indirect-injection defenses, abuse detection, kill switches, incident playbook. Plus a 30-prompt red team report.
Take an existing agent and cut cost ≥50% via the techniques in Module 7. Submit before/after dashboards, the eval showing no regression, and a writeup ranking which lever paid off most.
Module 2 reads almost directly from this. Required.