Tips for building AI agents
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.
What you should get from this
Recognize common agent-building pitfalls before adding multiple agents, complex prompts or hidden state.
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Continue through the same learning path with the next curated companion videos.
66 minutesCrewAI Tutorial: Complete Crash Course for Beginners
The same kind of build, but in CrewAI's role-goal-backstory style — agents as team members, tasks as deliverables, the framework hiding the execution loop.
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22 minutesContext Engineering for Agents
Apply write, select, compress and isolate patterns to manage agent context deliberately.
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44 minutesBuilding Brain-Like Memory for AI | LLM Agent Memory Systems
A longer implementation pass through the cognitive-science-inspired categories — episodic, semantic, working, procedural — wired into an agent in code.
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Take it further
Hand-picked external courses that go deeper on this topic.
Coursera · Vanderbilt UniversityChatGPT: Excel at Personal Automation with GPTs, AI & Zapier
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.
Hugging FaceAI Agents Course
The clearest open-source treatment of agentic systems available. Anchored in the three frameworks engineers actually evaluate (smolagents, LlamaIndex, LangGraph) rather than one vendor's stack. Concludes with a benchmark assignment and public leaderboard — accountability your team can verify.