open-aiase-26

Agent architecture fundamentals — ReAct pattern, 4-element agent model, 5 sophistication levels, tool schema design, HITL risk matrix, 4-layer error handling, observability (traces/metrics/logs), framework selection, and deployment models. Load when the user asks about building agents, tool design, agent observability, ReAct, HITL, or agent frameworks.

Harness Engineering — how to build the integrated environment (CLAUDE.md/AGENTS.md, hooks, skills, commands, structured state) that makes AI agents work reliably across sessions. Load when the user asks about CLAUDE.md, AGENTS.md, hooks, harness design, or Claude Code project configuration from the AIASE course.

Multi-agent patterns and team design — Orchestrator/Supervisor/Pipeline/Debate topologies, GAN-inspired Planner-Generator-Evaluator model, agent team coordination (Claude Code v2.1.32+), Skill vs. Sub-Agent decision framework, and the 4-layer agentic stack (L1 Skill → L2 MCP → L3 A2A → L4 CLI). Load when the user asks about multi-agent systems, agent teams, MCP, A2A protocol, or orchestration.

RAG (Retrieval-Augmented Generation) architecture — naive RAG pipeline, advanced techniques (HyDE, reranking, compression), modular RAG, GraphRAG, and practical debugging checklist. Load when the user asks about RAG, vector databases, embeddings, chunking, or retrieval systems from the AIASE course.

Spec-Driven Development (SDD) — how to write specs before coding with AI, the dimensional ascension/descent workflow, and dialogue-to-spec-to-code pipeline. Load when the user asks about writing specs, requirements, AIASE course SDD, or before starting any AI-assisted coding task.

AI era mental models and career strategy — three paradoxes (Agency/Depth/Automation), moat shift from features to data/domain, tacit vs. explicit knowledge, Automation vs. Augmentation career paths, labor market data, and career grid strategy. Load when the user asks about career decisions, how to position themselves in the AI era, competitive advantage, or the philosophical framing behind the AIASE course.

Research-Plan-Implement (RPI) workflow — the three-phase agentic SDLC for reliable AI-driven development, context window management, verifiability framework, and the autoresearch/gstack reference implementations. Load when the user asks about how to structure an AI coding session, how to plan before implementing, or the AIASE RPI framework.

Token economics and LLM API cost optimization — the 6-layer cost stack, 12 concrete reduction techniques, 7 API pricing mechanisms, model routing, semantic caching, and the AgentOpt model-pairing framework. Load when the user asks about LLM costs, token optimization, API pricing, prompt caching, routing, or batching.

AI system security — Security by Design workflow (per-version security checklists), prompt injection attack patterns and defenses, agent trust boundaries, credential isolation, RAG data poisoning, sandbox strategies, and the security-agent role pattern. Load when designing agentic systems, asking about prompt injection, evaluating trust boundaries, handling sensitive data, or setting up multi-agent security review.

CLI-first architecture principle for AI-era software — why all business logic must be reachable via CLI/headless interfaces before adding UI; how Skill/MCP/A2A/CLI map to OS IPC primitives; Agentic OS concept and UI decoupling. Load when designing software architecture, choosing interface layers, discussing agent interoperability, or asking why GUI-first apps fail in agentic pipelines.