Agentic AI Engineering

What AI architecture should I use — and why? Learn to choose between plain code, a single model call, tool calling, RAG, workflows, agents and multi-agent systems; then build, measure, secure and operate them.

From deterministic software to agentic systems
Each step hands the model more decisions — and hands you more engineering responsibility. Click a stage.
→→→→
InputCode (if / loops / functions)Output

Deterministic instructions: the same input always produces the same output.

Who decides what

The programmer decides every branch in advance.

Example

Validate a form, compute an invoice, route an HTTP request.

Reach for it when

Whenever the rules can be written down. This is most software — and the correct default.

Determinism
Model decides wording
+ your data
+ model decides actions
+ limits, memory, evals, approvals
Read this before the first lesson

If you're nothing without the LLM, then you shouldn't have it.

This is not an argument against AI-assisted engineering. It is an argument for understanding the systems AI helps you build.

The strongest engineer is not the one who refuses AI. It is the one who can use AI aggressively while remaining capable of evaluating, debugging, correcting and replacing its output.

The distinction is not how much AI you use. It is whether you could tell if it were wrong.

Amplified engineer
Engineer+
Knowledge+
Judgment+
LLM↓
Leverage
Blind dependency
No understanding+
LLM↓
Production
Bad

“I don't need to understand this because the AI can do it.”

Good

“I understand the system well enough to use AI to work much faster.”

AI should amplify engineering ability, not replace the understanding underneath it. Everything in this domain assumes you will use LLMs and agents aggressively — and stays able to evaluate, debug, correct and replace what they produce.

AI should amplify judgment, not replace it.Generation is cheap. Verification requires understanding.Review the LLM's answer →Debug without AI →

The judgment we teach

Mirror of the DSA chain. The core skill is picking the right abstraction, not memorizing frameworks.

  1. Problem→
  2. Requirements→
  3. Risk→
  4. Context→
  5. Architecture→
  6. Model→
  7. Tools→
  8. State→
  9. Evaluation→
  10. Production
The rule that matters most
Do not build an agent when deterministic software is sufficient. Escalate only when the previous rung cannot do the job reliably: code → LLM call → structured output → tool calling → RAG → workflow → agent → multi-agent.
What "done" means here
Measured with evals, observable with traces, bounded by budgets and step limits, guarded against injection, gated by humans for risky actions. A demo is not a system.

Start here

Learn
77 lessons across 15 modules, each with diagrams, tradeoffs and failure modes.
Architecture Finder
Plain software, LLM call, tool calling, RAG, workflow, agent, multi-agent — decided from your requirements, with reasons.
Architecture Playground
Drag-and-connect components; get strengths, risks, latency, cost and simplifications.
Framework Explorer
10 frameworks incl. Option 0: no framework.
Practice
12 investigation-style debugging challenges: broken RAG, looping agents, wrong tools, context overflow.
Interview
26 questions with beginner / strong / expert answers and green & red flags.
Hiring Guides
For interviewers: Agentic AI and AI-Native engineering. 5-minute screens, scoring levels, tiered questions, role expectations, practical assessments.

Modules

0/77 lessons mastered
Fundamentals0/6
What makes a system agentic, the agent loop, and what agentic engineers actually build.
Agent Architecture0/6
Single agent, agent + RAG, supervisor, workflow graphs — and the tradeoffs between them.
Tool Calling0/8
Schemas, structured arguments, validation, errors, retries, permissions.
RAG Engineering0/10
Ingestion, embeddings, storage, retrieval, reranking, grounding, evaluation.
Context Engineering0/5
Constructing the information an agent sees: selection, compression, ordering, budgets.
Memory & State0/3
Context vs short-term vs long-term; semantic, episodic, procedural; why more is not better.
Planning0/2
Direct execution, plan-then-execute, replanning, ReAct — and when planning only adds cost.
MCP — Model Context Protocol0/4
Clients, servers, tools, resources, prompts, discovery, auth; MCP vs direct integration.
Multi-Agent Systems0/7
Supervisor, pipeline, hierarchical, swarm; agent-to-agent communication; when it is unnecessary complexity.
Human-in-the-Loop0/4
Approval gates, risk assessment, escalation, confidence thresholds.
Evaluation & Testing0/6
Testing probabilistic systems: golden datasets, judges, deterministic evaluators, regression.
Observability0/3
Traces, spans, tokens, cost, latency, errors, state changes.
Guardrails & Security0/7
Prompt injection, tool misuse, exfiltration, least privilege, input/output guardrails.
Reliability Engineering0/4
Failure scenarios and their mitigations: retries, fallbacks, limits, budgets, caching.
Frameworks0/2
What each framework solves, its abstraction level, weaknesses — and Option 0: no framework.

Four domains, one question

A learner who finishes all four can answer:

DSA
“Why is Binary Search appropriate here instead of a Hash Map?”
Database
“How should I store and reach this data — and what am I giving up?”
Architecture
“Why does this system need microservices instead of a modular monolith?” Tool calling is external integration; agents are workflow architecture; RAG is retrieval infrastructure.
Agentic
“Why does this system need an agent at all instead of a deterministic workflow?”