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Agents & MCP

Agentic patterns, subagents, MCP servers, and tool integrations.

The collection38 saves · newest AUG 23
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  1. LV.1Replace Tool Servers With Command-Line Tools
  2. LV.2Vercel Agent Browsers: 15x Fewer Tokens for Claude Browser Automation
  3. LV.3GitHub's Agentic AI Developer Certification and Its Domains
  4. LV.4Three Lectures That Cover Agent Building End to End
  5. LV.5Scripts That Call Sub-Agents With the Workflows Feature
  6. LV.6A Cookbook for Per-User Memory in Managed Agents
  7. LV.7Why You Cannot Test an AI Feature Like Regular Code
  8. LV.8Define a Rubric for the Outcome, Not the Steps
  9. LV.9A Marketplace Where Companies Hire AI Agents
  10. LV.10Call MCP Servers Through a Script Instead of Directly
  11. LV.11Give the Model a Grading Rubric, Not Just a Task
  12. LV.12Why You Need Evals When Building with AI (LLM Output Is Unpredictable)
  13. LV.13The Case Against Autonomous Loops for Normal Development
  14. LV.14Kanban Board for Claude Code: Drag Tasks to Trigger Multi-Agent Sessions
  15. LV.15How IBM's Enterprise AI Coding Agent "Bob" Balances Safety, Control, and Developer Trust
  16. LV.16Builder + Checker Agent Loop That Self-Fixes Until All Tests Pass
  17. LV.17Two Types of AI Loops: Deterministic vs. LLM-as-Judge
  18. LV.18Don't Route Every Job Through One AI Vendor
  19. LV.19Why 'Human in the Loop' Is Failing: and What Agent Accountability Looks Like
  20. LV.20Claude Tag: Claude Now Joins Your Slack as a Team Member with Its Own Permissions
  21. LV.21Clips: Free Open Source Agent-Native Alternative to Loom
  22. LV.22Subagents for Beginners: Spawning Claude Agents and 6 Formations
  23. LV.23Claude Dynamic Workflows: orchestrating parallel verifying subagents in Claude Code
  24. LV.24The Agent Manager Role and How to Pivot Into It
  25. LV.25Three Habits From Anthropic's Own Agent Playbook
  26. LV.26Five Agent Metrics That Matter More Than Accuracy
  27. LV.273 Agent Harness Fixes: State Object, Validator Loop, Granular Tools
  28. LV.283 Lessons From Anthropic's Fable 5 Workshop: Outcomes, Advisor, Parallel
  29. LV.29Agentic OS Part 1: Unattended Overnight Claude Loops With Report Cards
  30. LV.30Three agent-engineering interview questions, and the modern way to scale to thousands of agents
  31. LV.31Seven Scoped Agents Instead of One Doing Six Jobs
  32. LV.32Knowledge Graphs Are Easy to Build Now, and That Changes Grounding
  33. LV.33From Prompt Engineering to Loop and Graph Engineering
  34. LV.34Common AI Agent Mistakes and What to Do Instead
  35. LV.35Five Agentic Design Patterns Every AI Engineer Should Understand
  36. LV.36How to Answer the Agent Latency Interview Question
  37. LV.37Five Agent Metrics That Matter More Than Accuracy
  38. LV.38Four Agent Observability Tools and the Five Things to Log