Best Agentic AI Course with Certification

Transform a command-line LLM application into a production-ready AI service by integrating APIs, databases, user interfaces, Docker, and deployment workflows.

Topics

• Recap & Architecture Overview
• Version Control & Project Structure
• Databases with MySQL
• Building APIs with FastAPI
• API Testing
• Async & Streaming
• Building User Interfaces
• Docker & Containerization
• Deployment
• Observability

Project

Deployable AI Support Assistant

Take the command-line AI Support Assistant built and evolve it into a deployable, full-stack application. Build a FastAPI backend, persist conversations in MySQL, create multiple user interfaces, containerize the application with Docker, and deploy it to a remote server while monitoring performance using LangSmith.

Command an LLM with precision: understand tokens, context windows, and model limits, then drive reliable behavior across three SDKs using zero-shot, few-shot, CoT, and ToT prompting, structured JSON output, and function calling.

Topics

• Traditional AI vs. Generative AI — the paradigm shift
• Tokens as the atomic unit: tokenization, token budgets, cost implications
• Context windows as working memory: the "lost in the middle" effect, linear cost scaling
• Three hard truths — hallucination, knowledge cutoff, context degradation — and their mitigations
• Three levers compared: prompting vs. retrieval vs. fine-tuning, and the order to use them
• One concept, three SDKs: OpenAI vs. Gemini vs. Claude — capability and behavior map
• Zero-shot, few-shot, and Chain-of-Thought (CoT) — when to use each
• Tree-of-Thought (ToT) and self-consistency — trading compute for reliability
• Role / persona prompting — shaping style without unlocking false knowledge
• Structured output: JSON Schema enforcement and typed responses (Pydantic)
• Function calling: the model decides, your runtime executes — and the four reuse patterns (single, parallel, sequential, conditional)

Hands-on

• OpenAI Python SDK
• Google Gemini SDK
• Anthropic Claude SDK
• tiktoken
• Pydantic
• Visual Studio Code / Google Colab

Project

Multi-SDK LLM Playground

Build a provider-agnostic client wrapping OpenAI, Gemini, and Claude behind one interface. Send the same prompt to all three, compare responses side by side, switch between zero-shot / few-shot / CoT modes, enforce JSON-schema output, run a basic function-calling loop, and capture token + cost telemetry per provider. Deliverable: a comparison report of 5 prompts × 3 providers scored on accuracy, cost, and latency.

Give the LLM a memory that is fresh, private, and provable. Build a production hybrid-RAG pipeline end to end — extraction, chunking, embeddings, hybrid search, two-stage reranking, advanced retrieval, and full observability.

Topics

• Why pure LLMs fail in production: stale knowledge, private-data blind spots, no citations
• Anatomy of a RAG system: offline indexing pipeline vs. real-time query pipeline
• Data extraction: PDFs, web, Office docs — why 60% of quality is decided before embedding
• Embeddings deep dive: vector geometry, cosine similarity, choosing a model (and benchmarking on your own data)
• Keyword (BM25 / TF-IDF) vs. semantic search vs. hybrid
• Vector databases: HNSW / IVF / ScaNN, ANN trade-offs, the landscape (Chroma, FAISS, pgvector, managed)
• Chunking strategies: fixed, recursive, semantic, document-aware — and universal sizing/overlap rules
• Metadata filtering — the cheapest accuracy gain in RAG; tenant, time-window, and doc-type isolation
• Two-stage retrieval: bi-encoder retrieve → cross-encoder rerank
• Advanced retrieval: Multi-Query expansion, HyDE, RRF fusion, Raptor RAG
• Observability with LangSmith and RAG failure-mode diagnosis (retrieval miss, context overflow, hallucination-despite-context, latency blowout)

Hands-on

• LangChain (RAG chains)
• ChromaDB / FAISS
• OpenAI Embeddings API
• BM25 + RRF hybrid retrieval
• Cohere / Voyage / BGE rerankers
• LangSmith
• Flowise

Project

Company Knowledge-Base Chatbot

Build a production-grade KB chatbot over a real corpus: ingest PDFs, web pages, and Office docs with per-type chunking; retrieve with hybrid BM25 + semantic search fused via RRF; rerank top-50 → top-5 with a cross-encoder; add metadata filtering and Multi-Query / Raptor modes; trace every step in LangSmith. Every answer ships with citations.

Move from answering questions to taking action. Build agents that reason, use tools, remember across sessions, and operate under bounded, auditable autonomy.

Topics

• Agent ≠ chatbot: the observe → think → act → observe loop, goals, and termination conditions
• The five components of an agent: role, goals, tools, memory, autonomy
• Agent archetypes: Researcher, Writer, Planner, Analyzer
• ReAct framework: Thought → Action → Observation, and how to read a trace
• CoT vs. Tool Use vs. ReAct — picking the cheapest pattern that works
• Tool design is API design: schemas, descriptions, validators, return shapes
• Tool orchestration: sequential, parallel fan-out, conditional routing, chaining
• Memory systems: short-term (context), long-term (vector / Redis), episodic
• Context-window budgeting: full history, sliding window, summary buffer, token caps
• External integrations: APIs, databases, browsers, file systems — each a scoped privilege
• The autonomy dial (L1–L4) and blast-radius scoping: tool whitelists, argument validators, budget/loop guards
• Agent frameworks compared: LangChain, LangGraph, OpenAI Agents SDK, Google ADK

Hands-on

• LangChain Agents
• LangGraph
• OpenAI Agents SDK / Function Calling
• Anthropic Tool Use
• Google ADK
• Upstash Redis + Mem0 (memory)
• Flowise

Project

Researcher-and-Summarizer Agent + Customer Care Agent

Build two agents from the same toolkit at opposite ends of the spectrum.

(1) Researcher-and-Summarizer: a from-scratch ReAct loop with web-search / page-fetch / citation tools, short-term buffer + Upstash Redis long-term memory, bounded autonomy, and a cited summary output.

(2) Customer Care Agent: strict tool schemas (get_order, check_refund_eligibility, issue_refund), sequential/parallel/conditional orchestration, argument validators, and human-in-the-loop confirmation gating on every write action — a working example of L2 bounded autonomy.

Standardize how agents reach tools, then coordinate many agents into one system. Build MCP servers, containerize them, and orchestrate specialist ensembles.

Topics

• The problem MCP solves: from one-off integrations to a universal agent-tool protocol
• MCP architecture: client, protocol, server, transport (stdio / HTTP-SSE)
• MCP primitives: Resources (read), Prompts (instruct), Tools (act)
• FastMCP: building a typed, auto-schema'd server in under 10 lines
• Custom MCP tools: web search, deep research, domain-specific — clear docstrings, Pydantic validation, structured returns
• Containerizing MCP servers with Docker for reproducible production deployment
• Debugging with MCP Inspector before connecting a live agent
• Why single agents hit a wall — and the multi-agent answer
• Three core patterns: sequential, parallel, hierarchical (coordinator-dispatcher)
• Specialist vs. generalist agents and how to design specialists
• Delegation and context-passing: structured handoffs, shared schemas, avoiding the #1 multi-agent failure
• Orchestration frameworks: CrewAI, Google ADK; visual builders n8n and Flowise

Hands-on

• FastMCP
• Docker
• MCP Inspector
• CrewAI
• Google ADK
• n8n
• Flowise

Project

Financial News Research Coordinator

Build a hierarchical multi-agent system: a Coordinator decomposes a research goal and dispatches to specialist sub-agents (Financial Analyst, Risk Assessor, Compliance Checker, News Researcher), each exposed as an MCP service via a containerized FastMCP server. Implement the Research → Critique → Refine pattern and mirror the code-first CrewAI / ADK build in a visual Flowise workflow. End-to-end demo: "Brief me on NVDA earnings risk" → a cited research note.

Take any agent from demo to deployment: defend it, measure it, and make it fail gracefully. Each capstone is hardened, evaluated, and made production-ready here.

Topics:

• Prompt injection: direct, indirect, stored, and multi-hop attack vectors — and layered defenses
• Guardrails: deterministic (rules, allow/deny, PII redaction, tool whitelisting) and probabilistic (classifier / LLM-judge)
• Circuit breakers and kill switches: automatic halt conditions and scoped overrides
• Context failures: stale sources, conflicting sources, unverified citations — and how to validate provenance
• Input/output content filtering across the pipeline
• Decision provenance: capturing reasoning, tool calls, sources, and confidence per step
• Retrieval metrics: Precision@K, Recall@K, MRR, NDCG
• Generation metrics: Faithfulness, Answer Relevance, Completeness — with production targets
• RAGAS: automated, LLM-as-judge RAG evaluation
• The Test → Measure → Improve loop: baseline, isolate the weakest link, fix one variable, re-evaluate
• Designing for edge cases: out-of-domain, ambiguous, empty-retrieval, conflicting answers
• Agent drift detection and graceful-degradation fallback patterns (model / retrieval / tool / response / full)

Hands-on

• Llama Guard / OpenAI Moderation API
• Presidio (PII detection & redaction)
• RAGAS
• LangSmith (trace capture & evaluation)
• Golden-dataset drift monitoring
• Guardrails / circuit-breaker implementation

Secure, Evaluated Production Agent

Take a Module 5 or Module 6 agent and make it production-ready: add multi-layer prompt-injection defenses, deterministic + probabilistic guardrails, PII redaction, citation verification, and structured audit logging; establish a golden evaluation set and run RAGAS for faithfulness, relevance, and completeness; measure retrieval quality (Precision@K, MRR, NDCG); and wire fallback behaviors plus scheduled drift monitoring. This is the convergence point — the learner has now built, secured, evaluated, and deployed a full agentic system end to end.