Agentic AI Course

Welcome to the Agentic AI Engineering track — your complete, production-grade roadmap to building autonomous AI agents, multi-agent systems, and enterprise AI automation from sc…

Artificial Intelligence (AI) is the field of building software that performs tasks normally requiring human intelligence — understanding language, recognising images, making dec…

Agentic AI is software where one or more LLMs autonomously plan and execute multi-step tasks by calling tools, retrieving knowledge, reflecting on results and adjusting their pl…

Classic AI answers questions; Agentic AI achieves goals.

AI agents are older than ChatGPT.

A neural network is layers of simple math units ("neurons") that learn to transform inputs into outputs by adjusting weights during training.

Large Language Models (LLMs) like GPT-4, Claude, Gemini and Llama predict the next token (a word piece) given the previous tokens.

Two foundations of every LLM and agent: tokens (the units the model sees) and embeddings (numeric vectors that capture meaning).

Reasoning is an LLM's ability to break a problem into steps and combine intermediate results into an answer.

Agentic AI is already deployed across coding, support, sales, research, gaming, healthcare, finance, security and government.

An LLM is a transformer trained on huge text corpora to predict the next token.

Every modern LLM is a transformer — a neural net built around self-attention.

Prompt engineering is the discipline of writing instructions that reliably steer an LLM.

Zero-shot means asking the model with no examples — just the instruction.

Few-shot means including 1-5 worked examples in the prompt.

Chain-of-Thought (CoT) asks the model to show its working before the final answer.

The system prompt sets the agent's persona, rules, constraints and tool contract.

The context window is the limited workspace an LLM sees per call.

Prompt optimization turns prompt engineering from art into engineering: define an eval set, measure baseline, iterate with versions, ship the winner.

Output control = forcing the model to return what your code can parse.

An AI agent is a software entity that perceives its environment, decides on an action and executes it — repeatedly — to achieve a goal.

A production agent is more than a while-loop.

Planning turns a high-level goal into a sequence of doable steps.

Agents need memory to be useful beyond one turn.

Reflection is the agent critiquing its own output and trying again if it's not good enough.

Autonomous agents run without a human in the loop for every step — only checked at milestones.

Tool calling lets the LLM emit a structured request to invoke a function — your code runs it and returns the result.

State = everything the agent knows mid-run: plan, step results, scratchpad, memories, tool call history.

When agents talk to each other (or to humans), use a structured protocol: JSON messages with type, sender, payload.

Production agents typically fit one of a handful of workflow patterns: ReAct, Plan-Execute, Router, Reflection, Multi-Agent.

An AI workflow is a directed graph of steps — some LLM, some tool, some classic code.

Task decomposition is splitting a goal into smaller, doable subtasks.

A planning system generates, validates and revises plans.

A sequential workflow runs steps one after another.

A parallel workflow runs independent steps simultaneously and joins their results.

An AI decision tree is a workflow where each node is a classification step that routes to the next branch.

Orchestration = the engine that runs, retries, persists and observes the workflow.

Autonomous execution = running long agent tasks without human ticks.

Agent steps fail — tools time out, JSON breaks, LLMs hallucinate.

Enterprise AI workflows add governance, audit, role-based tools, multi-tenant isolation, SSO, PII handling and compliance.

A multi-agent system is several specialised agents that collaborate to achieve a goal.

Collaboration = agents producing artefacts together (drafts, plans, code).

Coordination = deciding who does what when.

An AI communication protocol standardises how agents (and tools) talk.

Role-based agents embody specific personas — Researcher, Coder, QA, Designer.

A supervisor agent assigns tasks to specialists, evaluates their output and decides next steps.

A research agent autonomously searches sources, reads, synthesises and cites.

A coding agent reads a repo, plans changes, edits files, runs tests and iterates.

When multiple agents share a goal, planning must consider who does what.

Enterprise multi-agent systems run inside large companies with strict requirements: identity, audit, region, cost, SLA, compliance.

Retrieval-Augmented Generation (RAG) grounds an LLM in your private data by retrieving relevant chunks at query time and stuffing them into the prompt.

Embeddings are dense vectors that capture semantic meaning.

A vector database stores embeddings and supports fast nearest-neighbour search.

Semantic search finds results by meaning, not keywords.

Tool calling (a.k.a.

Most useful agents integrate with external APIs — Salesforce, Stripe, GitHub, Slack, internal services.

Agents often need to query external knowledge beyond a vector DB: Wikipedia, internal wiki, SQL DBs, search engines.

Production agents need explicit memory systems — not just a longer context.

Production RAG optimization turns a mediocre demo into a stellar product.

A production retrieval system handles ingestion, embedding, indexing, querying, monitoring and reindexing at scale.

AI automation = running agents on triggers (cron, webhook, queue) to do work without human prompts.

Deploying agents means shipping them as services with autoscaling, queues, secrets, monitoring and rollbacks.

Agents fail in new ways: silent hallucinations, infinite loops, runaway cost.

Agents introduce new attack surfaces: prompt injection, tool abuse, data exfiltration, jailbreaks.

Cost optimization = matching model + prompt + caching strategy to each step.

Scaling agents means handling concurrent users, long runs and bursty traffic without melting your wallet or your provider rate limits.

Autonomous automation is the holy grail — agents that detect work to do, do it, and report back.

Workflow optimization = remove steps, parallelise others, cache repeats, swap models, batch where possible.

Enterprise AI infrastructure includes a model gateway, prompt registry, eval platform, observability, secret management, and data isolation per tenant.

Putting it all together: a production agentic AI system is a hardened, observable, multi-tenant, cost-controlled, safety-vetted product.

Practice cements the theory.

Ten focused prompt-engineering challenges to sharpen your intuition.

Hands-on multi-agent designs — each pushes a different muscle: coordination, debate, swarm, hand-off, supervisor.

Ten workflow design challenges: DAGs, parallelism, retries, durability, human-in-loop.

Sharpen your tool-calling skills: schemas, validation, retries, multi-tool selection, error feedback.

Curated interview questions covering beginner → expert → scenario.

Ten mock interview prompts you can run with a friend or an LLM partner.

Realistic enterprise scenarios you'll face on the job — compliance, multi-tenant, scale, cost, governance.

Debugging agents is its own skill.

Eight production case studies to study deeply — what they shipped, what they learned, what broke.