18 minutesAWS re:Invent 2025 - Implementing Human-in-the-Loop Controls for Multi-Agent AI Systems (CNS428)
See how approval gates can be implemented as explicit workflow checkpoints rather than informal manual review after something goes wrong.
OpenAI. The actual launch demo of Operator, where the team books restaurants, orders groceries, buys event tickets, and lets Operator stall on a redirect or hand control back when it hits a login. It is the clearest picture you will find of how a browser agent feels in practice — the screenshot-and-click loop, the confirmations before "stateful" actions, the prompt-injection guard rails — which is exactly the texture the article is trying to set expectations for.
Product names and availability may change. Operator as a standalone product was folded into ChatGPT's agent mode in 2025 — watch this for the interaction patterns, not the product. The durable lesson is the control model: narrow tasks, visible state, confirmations, takeover paths and prompt-injection defenses.
Recognize the browser-agent action loop, where it helps, and where human confirmation is still required.
Basic understanding of agents and why live web tasks are harder than text generation.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
18 minutesSee how approval gates can be implemented as explicit workflow checkpoints rather than informal manual review after something goes wrong.
4 minutesUnderstand why multilingual embeddings matter for private internal search and where local retrieval can reduce data-exposure risk.
17 minutesSet up a multi-agent coding workflow with explicit review boundaries so generated changes stay small, tested and owned.
Hand-picked external courses that go deeper on this topic.
Dr. Jules White
The clearest path from "I use ChatGPT in a tab" to "my AI handles my inbox while I sleep." Three-course specialization built around Zapier — no Python required. By the end you'll have agents that summarise emails, update spreadsheets, and trigger workflows when conditions are met.
Anthropic Academy
MCP is the protocol that's quietly replacing one-off tool integrations across the AI tooling ecosystem. Learn it from the source. By the end you'll have built and deployed your own MCP server, connected an LLM client to it, and understood why this standard is the closest thing the field has to USB-C.
João Moura (Founder, CrewAI)
Doubles as our sales and customer-support vertical pick and a genuinely practical agent-building course: you build an agentic sales pipeline (lead scoring, personalized outreach) and a customer-support data-insights pipeline as two of the five hands-on projects, taught by CrewAI's own founder. Requires basic Python, so it sits with our other builder-track courses rather than the no-code picks.