‹ Shelves ■ Agentic AIA 25-lesson course, each with a citable work, a verified quotation and a video lesson: what an agent is and why it is a loop, not a prompt; context engineering; workflows versus agents; tools, MCP and A2A; frameworks and their supply chain; routing between models, small models, LoRA and when fine-tuning pays; evaluations; on-prem or cloud; personal data, prompt injection, least privilege; human oversight and the AI Act; budgets and loops that never stop; RPA or agent; observability.
31 lessons · 44 min
The reading track 31 lessons All With video · 31 All authors Anthropic (documentația Claude Code) · 4
01 An LLM agent is not a longer prompt but a loop: it perceives, decides, acts, then perceives the result of its own action. Artificial Intelligence: A Modern Approach · Stuart Russell, Peter Norvig · 1995 ▶ Video lesson › 02 An agent's behaviour is mostly the shape of its environment: context engineering beats prompt engineering. The Sciences of the Artificial · Herbert A. Simon · 1969 ▶ Video lesson › 03 Do not pack your knowledge into rules the model will outgrow: build the agent so that a better model makes it better, not broken. The Bitter Lesson · Richard S. Sutton · 2019 ▶ Video lesson › 04 First a workflow with fixed steps; an autonomous agent only when the steps cannot be known in advance. Building effective agents · Erik Schluntz, Barry Zhang · 2024 ▶ Video lesson › 05 The thought–action–observation loop is the basic shape of every modern agent: the model reasons, calls a tool, reads the result, then reasons again. ReAct: Synergizing Reasoning and Acting in Language Models · Shunyu Yao et al. · 2022 ▶ Video lesson › 06 Multi-agent systems pay off when the task splits into roles with different contexts, not because more agents sound smarter. The Society of Mind · Marvin Minsky · 1986 ▶ Video lesson › 07 A good tool for an agent has a narrow interface on the way out and a tolerant one on the way in; MCP standardises exactly that contract. RFC 793 — Transmission Control Protocol · Jon Postel · 1981 ▶ Video lesson › 08 Your agent topology will copy the org chart; A2A is the protocol that makes the boundaries between agents explicit, so you can choose them. How Do Committees Invent? · Melvin E. Conway · 1968 ▶ Video lesson › 09 Choose the framework after you have a simple agent that works; the framework must keep your loops visible, not hide them. Systemantics: How Systems Work and Especially How They Fail · John Gall · 1975 ▶ Video lesson › 10 A tool server is code your agent executes with your privileges, and its description is a prompt you never read. Reflections on Trusting Trust · Ken Thompson · 1984 ▶ Video lesson › 11 Do not pay for reasoning where no thinking is needed: route cheap calls to cheap models and keep the reasoning model for the hard decisions. An Introduction to Mathematics · Alfred North Whitehead · 1911 ▶ Video lesson › 12 Inside an agent, most calls are narrow and repetitive; a small specialised model does them cheaper, faster and often just as well. Small Language Models are the Future of Agentic AI · Peter Belcak et al. (NVIDIA Research) · 2025 ▶ Video lesson › 13 LoRA trains only a few small matrices beside the frozen model: that is why it costs one GPU and a few hours, not a cluster. LoRA: Low-Rank Adaptation of Large Language Models · Edward J. Hu et al. · 2021 ▶ Video lesson › 14 The order is prompt, then RAG, then LoRA: fine-tuning pays when the behaviour is narrow and repeated, not when knowledge is missing. LoRA Without Regret · John Schulman et al. · 2025 ▶ Video lesson › 15 Evaluations are an agent's only brake, and an evaluator you optimise against becomes the target itself. «Improving ratings»: audit in the British University system · Marilyn Strathern · 1997 ▶ Video lesson › 16 Cheap tokens mean more tokens; the on-prem or cloud decision rests on sustained utilisation and on where the data may live, not on today's price. The Coal Question · William Stanley Jevons · 1865 ▶ Video lesson › 17 The lethal trifecta: access to private data, exposure to untrusted content and the ability to communicate externally; have all three and you have a leak. The lethal trifecta for AI agents · Simon Willison · 2025 ▶ Video lesson › 18 An agent gets its tools from an allowlist, with minimal rights, in a sandbox, on a budget; whatever is not explicitly allowed is forbidden. The Protection of Information in Computer Systems · Jerome H. Saltzer, Michael D. Schroeder · 1975 ▶ Video lesson › 19 Personal data enters an agent through tools, not through the prompt; pseudonymise before the model, redact tool outputs and keep memory off by default. Regulamentul (UE) 2016/679 — GDPR · Parlamentul European și Consiliul · 2016 ▶ Video lesson › 20 Autonomy is granted in steps, with approval thresholds on whatever is irreversible; the AI Act requires effective human oversight for high-risk systems. Computer Power and Human Reason · Joseph Weizenbaum · 1976 ▶ Video lesson › 21 The most expensive agent failures are badly specified goals and loops that never stop; the step budget and the kill switch are not optional. Some Moral and Technical Consequences of Automation · Norbert Wiener · 1960 ▶ Video lesson › 22 RPA automates the existing steps of a process; an agent can rewrite the process; you choose by how structured and how reversible each step is. Reengineering Work: Don't Automate, Obliterate · Michael Hammer · 1990 ▶ Video lesson › 23 The hybrid stack: RPA or deterministic code runs the steps that can be told; the agent takes the steps people know without being able to describe them. The Tacit Dimension · Michael Polanyi · 1966 ▶ Video lesson › 24 An agent without traces cannot be debugged: every model and tool call goes into a trace with a parent, a cost and content, so you can replay what happened. The Elements of Programming Style · Brian W. Kernighan, P. J. Plauger · 1974 ▶ Video lesson › 25 The 2026 trends say the same thing: capabilities grow fast, and most projects fail on organisation, not on the model. Computing Machinery and Intelligence · Alan M. Turing · 1950 ▶ Video lesson › 26 The context window is the resource every file read and every command output consumes; performance drops as it fills, so productivity in a coding agent is, first of all, context hygiene. Effective context engineering for AI agents · Prithvi Rajasekaran, Ethan Dixon, Carly Ryan, Jeremy Hadfield (Anthropic Engineering) · 2025 ▶ Video lesson › 27 Without a runnable check, "looks done" is the only signal and you become the verification loop; with one, the loop closes by itself and the session can run without you. Best practices for Claude Code · Anthropic (documentația Claude Code) · 2026 ▶ Video lesson › 28 Code that solves the wrong problem is the most expensive code; a plan separates understanding from execution, but it has a cost, so it applies only when uncertainty justifies it. Best practices for Claude Code · Anthropic (documentația Claude Code) · 2026 ▶ Video lesson › 29 Claude Code is configured on six levels, each with a different ratio of context cost to authority; putting an instruction at the wrong level costs either permanent tokens or ignored rules. Steering Claude Code: when to use CLAUDE.md, skills, hooks, and subagents · Anthropic (blogul Claude) · 2026 ▶ Video lesson › 30 Once you are effective with one session, the next step is to multiply: worktrees for isolation, cross-session messages for coordination, headless runs for fan-out — and the summer of 2026 moved exactly these pieces from experiment to default. What's new in Claude Code — weekly dev digest · Anthropic (documentația Claude Code) · 2026 ▶ Video lesson › 31 Most bad sessions do not have a bad model but a polluted context and a prompt that never said what "done" means; the guide names five recurring patterns and gives each a mechanical fix. Best practices for Claude Code · Anthropic (documentația Claude Code) · 2026 ▶ Video lesson ›