AI智能体工程路线图涵盖工具与编排

AI Agent Engineering — Ultimate Roadmap (SAVE NOW)! ├── 01 Foundations & Role │ ├── What an AI Agent Actually Is (vs Chatbot/Workflow) │ ├── Agent vs LLM vs Automation │ ├── Core Components: Model, Tools, Instructions, Memory │ ├── Spectrum of Autonomy & When Not to Use Agents │ └── Stakeholders, Constraints & Cost/Latency Drivers ├── 02 Agentic Thinking │ ├── Start Simple → Add Complexity Only When Needed │ ├── Tradeoff Analysis (Latency, Cost, Reliability, Control) │ ├── Workflows vs Agents (predefined paths vs dynamic loops) │ ├── Failure Modes, Hallucinations & Recovery │ └── Context Engineering (high-signal tokens, attention budget) ├── 03 Core Patterns & Loops │ ├── ReAct (Reason + Act + Observation) — foundational loop │ ├── Chain-of-Thought, Tree-of-Thoughts, Plan-and-Execute │ ├── Reflexion & Self-Correction │ ├── Prompt Chaining, Routing, Parallelization, Orchestrator-Worker │ └── Evaluator-Optimizer & Stopping Conditions ├── 04 Tools, Actions & Protocols │ ├── Function/Tool Calling Fundamentals │ ├── Designing High-Quality Tools (clear names, schemas, error handling, examples) │ ├── Model Context Protocol — open standard for tools & data │ ├── Tool Inventory Design & Selection │ └── Action Types (read, write, code execution, browser, computer use) ├── 05 Memory & Knowledge │ ├── Short-term vs Long-term / Episodic / Semantic Memory │ ├── Context Window Management & Compaction │ ├── RAG for Agents (retrieval as a tool) │ ├── State & Checkpoints (durable execution) │ └── Memory Patterns That Survive Production ├── 06 Frameworks & Orchestration │ ├── Raw LLM APIs First (understand the loop) │ ├── LangGraph — stateful graphs, checkpoints, production control │ ├── CrewAI — role-based crews for rapid multi-agent prototypes │ ├── OpenAI Agents SDK, Claude Agent SDK, Microsoft Agent Framework │ └── When to Adopt a Framework vs Stay Minimal ├── 07 Multi-Agent Systems │ ├── Single Agent vs Multi-Agent Trade-offs │ ├── Orchestrator-Worker/Manager Patterns │ ├── Handoffs, Agents-as-Tools & Decentralized Patterns │ ├── Communication, Roles & Specialization │ └── Agent-to-Agent Protocols ├── 08 Evaluation, Observability & Reliability │ ├── Building Eval Sets Before Code │ ├── Tracing, Logging & Debugging Agent Trajectories │ ├── Metrics: Task Success, Steps, Cost, Latency, Robustness │ ├── Guardrails, Safety & Least Privilege │ └── Human-in-the-Loop & Approval Gates ├── 09 Production & Evolution │ ├── Deployment (Cloud Run, containers, sandboxes) │ ├── Cost Control, Token Optimization & Model Routing │ ├── Security (prompt injection, tool abuse, secrets) │ ├── Monitoring, Alerts & Continuous Improvement │ └── Harness Engineering & Long-Running Agents ├── 10 Practice & Path │ ├── Build From Scratch (TinyAgent / raw loop) │ ├── Key Papers: ReAct, Toolformer, Generative Agents, Reflexion Learn to design agents that survive real tasks, teams, cost & change.