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Agentic AI Engineering

Design, build, evaluate, and deploy production-grade AI agents

10 phases. 51 lessons. 51 labs. 3 capstone projects. From LLM APIs through multi-agent systems, MCP, computer use, memory, production infrastructure, and evaluation. You build and ship real agentic systems that qualify for AI engineer roles.

10 phases · 51 lessons · 51 labs · 3 capstone projects

Take ML & AI Engineering first — this course builds on it.

Outcomes you will have by the end

• 3 capstone projects with eval reports — Customer support agent, research workflow system, and personal AI assistant — each with architecture docs, eval suites, and deployment configs.
• 1 deployed production agent — A real async agent system deployed with FastAPI, Redis, Docker, and distributed tracing. A public endpoint you can demo in interviews.
• Verified AI Agent Engineer certificate — Issued by DeVenture Academy, tied to your completion record. Lists the specific agent engineering skills, phases, and projects you completed.
• Agent evaluation portfolio — RAGAS reports, LLM-as-judge benchmarks, and red team findings — the kind of evidence that gets you past phone screens at AI-first companies.
• Interview-ready agent engineering case studies — Architecture decision records, evaluation reports, and STAR stories for every project. Walk through them in any system design interview.
• 51 labs with real agent implementations — Every lesson has a lab with a production-grade code artifact. You build a model router, a resilient tool executor, a multi-agent orchestrator, an MCP server, and more.

What you will be able to do

Agents · Multi-Agent · RAG · MCP · Evals · Production

Every phase, every lesson, every project

• LLM Fast Track (4 lessons) — free — LLM APIs (OpenAI, Anthropic, Gemini), prompt engineering fundamentals, function calling, embeddings & semantic search
  • LLM APIs in Practice
  • Prompt Engineering Fundamentals
  • Structured Outputs & Function Calling
  • Embeddings & Semantic Search
• Advanced Prompt Engineering for Agents (5 lessons) — System prompt architecture, few-shot for tool use, chain-of-thought & ReAct, prompt versioning, adversarial prompting
  • System Prompt Architecture
  • Few-Shot Prompting for Tool Use
  • Chain-of-Thought & Reasoning Patterns
  • Prompt Versioning & Management
  • Adversarial Prompting — Attack & Defense
• Tool Design & Function Calling (5 lessons) — Tool schema design, error handling in tool execution, async parallel tool calls, streaming responses, tool safety & validation
  • Designing Tool Schemas
  • Error Handling in Tool Execution
  • Async Tool Execution
  • Tool Composition & Dynamic Registration
  • Streaming Tool Outputs
• Agentic RAG (5 lessons) — Agentic vs standard RAG, chunking strategies, hybrid search & reranking, RAG evaluation, GraphRAG
  • RAG as an Agent Tool
  • Query Decomposition & Routing
  • Self-Querying & Recursive Retrieval
  • Multi-Source Retrieval
  • GraphRAG — Knowledge Graphs for Complex Reasoning
• Multi-Agent Systems (6 lessons) — When to go multi-agent, orchestrator-subagent pattern, communication protocols, parallel execution, swarm patterns, reflection loops
  • When to Go Multi-Agent
  • Supervisor / Orchestrator Pattern
  • Swarm Pattern
  • LangGraph for Stateful Multi-Agent Workflows
  • Agent Communication Protocols
  • Reflection & Self-Critique Agents
• Model Context Protocol (MCP) (5 lessons) — MCP architecture, building MCP servers, resources & prompts, authentication, real-world integrations
  • What MCP Is and Why It Matters
  • Building MCP Servers
  • MCP Client Integration
  • Dynamic Tool Discovery & MCP Registries
  • Real Integrations — GitHub, Slack, Postgres
• Computer Use & Browser Agents (4 lessons) — Computer use API, browser agents with Playwright, reliable GUI interaction, safety in computer use
  • Computer Use Fundamentals
  • Browser Automation with Playwright
  • Vision + Action Loops
  • Sandboxing & Safety for Browser Agents
• Agent Memory Systems (5 lessons) — Memory taxonomy, conversation management, external semantic memory, memory consolidation, user profiling
  • Memory Architecture Taxonomy
  • In-Context Memory Management
  • External Vector Store Memory
  • Memory Consolidation & Forgetting
  • Personalization via Memory
• Production Agent Infrastructure (7 lessons) — Async architecture, tracing & observability, cost monitoring, guardrails, fine-tuning for tool use, deployment patterns, incident response
  • Async Tool Execution & Parallelism
  • Queue-Based Agent Dispatch
  • Cost Optimization & Token Budgets
  • Guardrails & Safety Systems
  • Fine-Tuning Agents for Tool Use
  • Agent UX Patterns
  • Deploying Agents with FastAPI and Docker
• Agent Evaluation & Observability (5 lessons) — Eval frameworks, LLM-as-judge, benchmark design, CI/CD for agents, red teaming
  • Trajectory Evaluation
  • LLM-as-Judge
  • Distributed Tracing for Agents
  • Production Monitoring & Alerting
  • Red Teaming Agents

The technologies you will use

Anthropic SDK · OpenAI SDK · LangGraph · FastAPI · Redis · Chroma · MCP SDK · Playwright · Pydantic · Langfuse · asyncio · Docker

Roles this course prepares you for

• AI Engineer (Agents) ($130k–$200k) — Build and ship production AI agents — tool-using systems, multi-agent workflows, RAG pipelines, and autonomous task executors. You design agent architectures, implement safety guardrails, write eval suites, and maintain agents in production.
• LLM Systems Engineer ($140k–$210k) — Design and build the infrastructure layer for LLM-powered systems — prompt registries, agent orchestration, evaluation pipelines, cost optimization, and distributed agent execution.
• Agentic AI Researcher/Engineer ($150k–$220k) — Work at the frontier of agent capabilities — multi-agent coordination, long-horizon planning, memory systems, and agent evaluation. Found at labs (Anthropic, OpenAI, Google DeepMind) and frontier AI companies.
• AI Platform Engineer ($135k–$195k) — Build the internal platform that other engineers use to build agents — MCP servers, tool registries, evaluation infrastructure, deployment pipelines, and the monitoring stack.

What agentic AI engineering actually is

Agentic AI engineering is the discipline of building production systems where LLMs act autonomously — using tools, planning multi-step workflows, maintaining memory, and coordinating with other agents. It is not "prompt engineering" — it is the engineering layer that makes autonomous AI reliable, observable, and safe in real products.

What you do every day

You build agent loops that plan and execute tasks, tool integrations that let agents interact with APIs and databases, memory systems that persist context across sessions, and multi-agent orchestration with LangGraph. You debug why an agent took an unexpected action, trace tool-call failures, and write eval suites that catch regressions before users do.

Why companies hire for this

Every company wants AI agents that can actually do work — not just chat. Very few engineers can build agentic systems that are reliable enough for production. The gap is in orchestration, tool design, memory architecture, and evaluation. Companies need people who can take an agent prototype and make it dependable.

What this course is not

It is not a "use LangChain" course. You will not just call high-level abstractions. You will build the agent loop from scratch, implement tool schemas, design memory systems, and ship multi-agent architectures — and you will prove it with a portfolio of working agents.

Common questions

What background do I need for the Agentic AI Engineering course?

Python proficiency. You either completed Phase 01 of the ML & AI Engineering course, or you're a self-taught engineer comfortable writing Python functions, classes, and async code. No ML math required.

Is this course standalone or does it build on Course 1?

Fully standalone. Phase 00 (LLM Fast Track) is a 4-lesson fast entry ramp covering everything from Course 1 you need. Engineers from Course 1 can skip Phase 00 or use it as a rapid review.

How is this different from the AI Agents phase in the ML & AI Engineering course?

The ML & AI Engineering course covers agents in 4 lessons as part of a broader curriculum. This course goes 10x deeper: 51 lessons on agents specifically — advanced prompt engineering for agents, production infrastructure, MCP, computer use, memory systems, and red teaming. Completely different scope.

How long does this course take?

100–130 hours of structured content. Most engineers complete it in 3–4 months at 8–10 hours per week. Phase 00 can be completed in a single weekend.

Does this course prepare me for AI engineer technical interviews?

Yes. Every phase ends with a hands-on lab that matches a real interview take-home. The capstone projects are deployed portfolio pieces you can walk through in any technical interview.

What does the capstone include?

Three production-grade agent systems: a customer support agent (multi-agent + RAG + MCP), a research workflow agent (computer use + memory + multi-agent), and a personal AI assistant (memory profiling + user modeling + production deployment). Each ships with eval reports and a GitHub repo.

Key terms in this course

MCP (Model Context Protocol) · Agent · RAG (Retrieval-Augmented Generation) · Function Calling · Prompt Engineering · Tool Use · Chain of Thought (CoT) · Chunking

Continue your learning path

ML & AI Engineering · AI Security & Red Teaming · Forward Deployed AI Engineering · Build Your First AI Agent from Scratch · Build an Autonomous Coding Agent · Build a Multi-Agent Research Team

Start the Agentic AI Engineering course

Create a free account — the opening phases of 24 of 30 courses are free, no credit card. Or see Pro pricing.

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