#Models & agents
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- Learning path: language models and agents
- Tokens, probabilities and sampling
- Attention, feed-forward networks and MoE
- Inference, KV cache and memory
- Inference-time compute, candidates and verification
- Prompts and output contracts
- Context engineering
- Retrieval, RAG and evidence
- Agent loops and executors
- Tool interfaces and MCP
- Skills and reusable task methods
- Memory, state and recovery
- Authority, trust and tool boundaries
- Multi-agent orchestration and integration
- Evaluation, trials and observability
- Cost, capacity and reliability
- Release validation, canaries and recovery