Deep Dive into Building Anthropic Agents
HeyAmit_ · x · 2026-07-20
This is a 45-minute deep dive into how Anthropic builds AI agents, focusing on how these agents:
- Remember previous information
- Learn from mistakes made during each run
- Gradually improve over multi-turn executions
The post itself is a reshare, recommending bookmarking it to watch the full analysis later.
Related event: Anthropic Shares Methodology on Building AI Agents(4 posts)→
More from coding & agent
- Shopify design lead on AI-era workflow: designers are becoming full-stack editors — KlausCodes · 2026-10-26
- Ultrametric open-sources Open Startup Repo to let AI agents run startup operations — garrytan · 2026-10-26
- MCP fault injection study: agent task scores drop 60% under realistic tool failures, scale doesn't help — dair_ai · 2026-10-26
- General agents burn ~140M tokens/month on LinkedIn outreach; scripts do it 60x cheaper — ChrisHarpon2 · 2026-10-26
- OpenAI upgrades Decisions API: 400 questions per call, native voice and image support — OpenAIDevs · 2026-10-26
- NLI alone rejects 5/6 new agent memories; a 0.45 similarity gate fixes it all — Alternative_Pin9598 · 2026-10-26