Anthropic. Three Anthropic engineers walking through the most common pitfalls they see — agents that don't know when to stop, over-prompting in the system prompt instead of fixing the environment, the cost of multi-agent designs nobody actually needed. Useful right after Barry's talk; you will recognise the same patterns from a different angle.
The pitfalls catalogue doubles as a review checklist: stopping rules, prompt-versus-environment fixes, and whether the multi-agent design was ever needed.
Recognize common agent-building pitfalls before adding multiple agents, complex prompts or hidden state.
Watch the primary pick first — this assumes the same baseline and adds field pitfalls.
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.
Coursera · Vanderbilt UniversityDr. 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 AcademyAnthropic 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.
DeepLearning.AIJoão 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.
