Account

GenAI Agent Engineering

L4-L5 · 5 courses · 136 chapters

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

What you'll learn

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

Your learning path

5 courses · sequenced for compounding · 136 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate12 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate10 Ch

Step 4

Web APIs & Services for GenAI Engineers

Design, build, and harden HTTP APIs with FastAPI — auth, streaming, rate limiting, OpenAPI contracts.

Advanced81 Ch

Capstone

GenAI Agent Engineering

Build production-grade agents with hosted LLMs — planning, tools, memory, evaluation, and orchestration patterns.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

LangGraph

Build stateful multi-step agent graphs

LangChain

Compose agent chains with tool calling

CrewAI

Orchestrate multi-agent teams with roles

MCP SDK

Implement Model Context Protocol tool servers

A2A

Agent-to-agent messaging between teams

OpenAI API

GPT-4o inside agent reasoning loops

Anthropic API

Claude 4 for long-context planning

pgvector

Agent memory + retrieval-augmented reasoning

Neo4j

Knowledge graphs as structured agent memory

Redis

Short-term scratchpad + conversation state

Langfuse

Trace, debug, replay every agent run

LangSmith

Evaluate agent trajectories and tool use

Start the GenAI Agent Engineering discipline today

7-day money-back guarantee

Subscribe — $27/mo (6-month plan) →Or save with a 4-pack bundle →