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GenAI Agent Engineering

L4-L5 · 354h · 5 courses · 118 chapters

Build autonomous multi-agent systems with planning, reasoning, tool use, memory, MCP/A2A protocols, safety boundaries, and production evaluation.

Role-alignedHands-on labsCapstone project30-day money-back

What you'll own in this role

Core responsibilities this discipline prepares you for.

Design autonomous GenAI agents

using state machines with tool calling, memory, and planning

Build LangGraph agents from scratch: define graph nodes, conditional edges, state schemas, and checkpointing
Progress from simple ReAct agents → planning agents → multi-step agents with persistent memory
Apply state machine theory to design agent graphs for complex, real-world task scenarios

Build multi-agent systems

with supervisor/worker hierarchies, delegation, and parallel execution

Implement supervisor agent patterns that route tasks to specialist worker agents
Construct hierarchical team structures with dynamic agent spawning and swarm coordination
Monitor cross-agent execution with delegation rules and parallel task orchestration

Implement MCP servers and clients

for standardized tool integration

Build Model Context Protocol servers that expose REST APIs as discoverable agent tools
Implement MCP clients in LangGraph agents with dynamic tool registration and schema negotiation
Validate tool selection accuracy across diverse query types and measure invocation reliability

Enable agent-to-agent communication

using A2A protocol for cross-framework interoperability

Implement A2A v0.3 protocol mechanics: Agent Cards, task lifecycle management, and gRPC transport
Build A2A-compatible agents using Google ADK with capability advertising
Verify cross-framework interoperability between independently built agent systems

Build production RAG agents

with iterative retrieval, self-verification, and query decomposition

Add vector search nodes to LangGraph agent graphs with quality-checked retrieval loops
Implement query decomposition for complex multi-part questions with iterative refinement
Benchmark agentic RAG against static RAG pipelines using faithfulness and relevance metrics

Implement guardrails and safety controls

within agent workflows

Integrate NeMo Guardrails for content filtering within running agent execution loops
Add LlamaFirewall middleware with policy-based tool access control and output filtering
Quantify safety-vs-helpfulness tradeoffs using adversarial test suites and scoring rubrics

Evaluate agent performance

with trajectory analysis and cost tracking

Build evaluation harnesses measuring trajectory quality, tool selection accuracy, and task completion
Run agents against standardized test suites and analyze per-task token cost attribution
Track agent quality regressions over time with Langfuse observability dashboards

Design context engineering

— systematic composition of prompts, memory, tools, and history

Structure system prompts, conversation memory windows, and tool result formatting strategies
Optimize context window utilization across multi-turn conversations with token budgeting
Measure agent behavior differences across context designs using controlled A/B evaluations

Tools you'll ship with

Industry-standard stack for current L4–L6 GenAI engineering roles.

LangGraphLangChainCrewAIMCP SDKA2AOpenAI APIAnthropic APIpgvectorNeo4jRedisK8sLangfuseLangSmith

Your learning route

5 courses · sequenced for compounding · 118 chapters · ~354 hours

Step 1 · Foundations

Python Essentials for Agent Builders

13 chapters

Step 2

LLM Foundations for Agent Builders

20 chapters

Step 3

Kubernetes Essentials for GenAI Engineers

17 chapters

Step 4

Web APIs & Services for GenAI Engineers

12 chapters

Step 5 · Capstone

GenAI Agent Engineering

56 chapters

Start the GenAI Agent Engineering discipline today

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