Models & agents

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• Learning path: language models and agents
• Model Foundations
• Tokens, probabilities and sampling
• Attention, feed-forward networks and MoE
• Inference, KV cache and memory
• Inference-time compute, candidates and verification
• Requests and Evidence
• Prompts and output contracts
• Context engineering
• Retrieval, RAG and evidence
• Agent Execution Systems
• 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 and Production
• Evaluation, trials and observability
• Cost, capacity and reliability
• Release validation, canaries and recovery