AI Agent terminology can intimidate: models, prompts, RAG, MCP, workflows, multi-agent systems and reinforcement learning. Put the terms aside first. Build a judgment map: what does it see, what can it do, how does it choose the next step, and can an error be found and corrected?
Learning agents means designing a reliable observe–act–verify loop.
01 / FORMULASRemember two formulas first
The minimum formula is Agent = LLM + context + tools. A model understands and decides, context provides what it can see, and tools turn a decision into an action. The production formula is Agent = Model + Harness. The harness organizes context and tools, then adds constraints, verification and correction. The first formula says why an agent can do work; the second says why it might keep doing it correctly.
02 / PRACTICESplit Codex into five parts
Open a real task rather than writing code first. You can see a model that understands the goal and chooses a next step; context from the request, repository, project rules and tool results; tools to read, run, edit, build and publish; constraints that protect scope, sensitive information and irreversible actions; and verification plus correction that checks the real result and changes course after failure. Ask those five questions about any Agent product to understand half its architecture.
03 / PRACTICEWhy an agent keeps acting
A normal chat is question, answer, end. An agent receives a goal, observes the environment, decides the next action, calls a tool, checks the result, then continues or stops when a condition is met. This ReAct loop matters because action creates real feedback. Without feedback, an agent only guesses repeatedly; with it, the route can be corrected.
04 / PRACTICEDecide whether an agent is needed
Not every automation needs autonomy. When a path is stable and rules are explicit, a fixed workflow is cheaper, faster and easier to audit: OCR, extract fields, apply a template, then human review. Consider an agent when steps depend on intermediate results: observe materials, identify a gap, select a tool and adjust after feedback. For money, privacy, legal deadlines or external sending, retain deterministic flows and human confirmation. Most real systems mix flexible agent work with rule-controlled critical points.
05 / PRACTICEMove from doing work to doing it reliably
A demo only needs a tool call. A product must ask whether permissions are too broad, input is trustworthy, calls time out, an action took effect, and when repeated failure stops. A harness has four duties: Context lets the model see the right information; Constrain limits what it can and cannot do; Verify checks that an action actually succeeded; Correct retries, rolls back or hands the case to a person. Learn to guide unstable intelligence into a system that can be observed, recovered and held responsible.
06 / ROADMAPA practical-first learning roadmap
First, build the map: components, ReAct, workflow, autonomy and harness. Next, give it eyes through context engineering, memory, knowledge bases and RAG. Then give it hands through tool design, MCP, permissions, events and collaboration. Build one small real agent and keep its trace. Make it reliable with coding agents, evaluation, recovery and comparable measures. Finally expand to multimodality and multi-agent work; leave post-training and continuous evolution until the end. For every chapter, run one contrast experiment: break it, observe it, then fix it.
07 / EXERCISEUse new-case organization as the first exercise
Imagine an agent helping a lawyer organize a new case. Before code, list its context: goal, materials, timeline, rules and current status. List tools: what it reads, searches, generates and writes. List constraints: what cannot leave the system, which actions need human confirmation, and which conclusions remain unverified. List verification: completeness, date consistency, traceable sources and real file saving. Finally write stop conditions: what completion means and when a lawyer must decide. If these answers are unclear, code only amplifies ambiguity.
08 / PRACTICEContinue from ten chapters to twenty site lessons
The source book contains ten engineering chapters. The site adds ten beginner lessons before the engineering path: AI and models, chat, the move to agents, verification, model choice, open and closed models, mainland access, interfaces and CLI, local Ollama, and tokens. Then study context engineering, memory and RAG, tools and MCP, coding agents, evaluation, post-training, continuous learning, multimodality and multi-agent collaboration. The source is not replaced; its entrance is made gentler.
Source book and learning entry points
- bojieli/ai-agent-book — Deep Understanding AI Agent.
- Official learning guidance.
- Chapter 1 — formulas, tools, harness and orchestration.
- This is an applied reorganization, not a replacement for the source book. The repository uses Apache License 2.0; use the text and code for exact claims.
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