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learn ai agents & automation
tool use, planning, memory, and multi-agent systems — how llms move from answering to doing real work.
the curated path
curatedmixed~4 weeks, part-time
ai agents & automation
how language models stop just answering and start doing — using tools, planning, remembering, and coordinating to automate real work.
4 modules · 12 resources · checkpoint per modulestay current
what's new in ai agents & automation
- Environment-in-the-Loop: Rethinking Code Migration with LLM-based Agentsuses llm agents with runtime feedback to handle code migrations across large codebases, catching subtle errors that static analysis misses. practical for teams managing library upgrades and systematic refactoring.
- kAPR: A coverage-guided, context-aware agent for automated repair of Linux kernel bugsan agent that automatically repairs kernel bugs using coverage guidance and context awareness to navigate complex codebases. demonstrates how agents can handle high-stakes, low-tolerance-for-error domains.
- From Intent to Execution: Composing Agentic Workflows with Agent Recommendationproposes methods to automatically compose multi-agent systems by recommending which agents should handle specific user intents. helps practitioners build flexible, reusable agent families without manual orchestration.
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