GenAI Agent Engineering
Build autonomous multi-agent systems with planning, reasoning, tool use, memory, MCP/A2A protocols, safety boundaries, and production evaluation.
What a GenAI agent engineer does · GenAI Agent Engineering
An agent is a program that plans, calls tools, remembers, and asks for help when it should — and every one of those verbs is a place it can fail quietly. This discipline is about building agents whose behavior you can explain: LangGraph state machines with explicit tool boundaries, supervisor–worker hierarchies for multi-agent work, MCP servers that expose your systems safely, A2A for agents that must talk across frameworks, and memory in pgvector, Neo4j and Redis. You also learn context engineering — composing prompts, history and tool results so the model sees exactly what it needs. Evaluation is trajectory-level, not answer-level. If you would rather ship the product around the agent, start with application engineering; if you want to keep agents honest in production, see LLMOps.
Start with these 7 free GenAI Agent Engineering lessons
The same content subscribers read — no account required. Browse all 58 free GenAI Agent Engineering lessons
5 courses, each building on the last. The bar shows each course's share of the curriculum. 58 lessons are free to read.
Python Essentials for Agent Builders 8.3% of the curriculum
In scope for GenAI Agent Engineering: chapters 1–13 — 62 of this path’s 744 lessons. 1 free GenAI Agent Engineering lesson
LLM Foundations for Agent Builders 10.8% of the curriculum
In scope for GenAI Agent Engineering: chapters 1–6, 11–20 — 80 of this path’s 744 lessons. 5 free GenAI Agent Engineering lessons
Kubernetes Essentials for GenAI Engineers 6.5% of the curriculum
In scope for GenAI Agent Engineering: chapters 1–4, 9–12 — 48 of this path’s 744 lessons. 11 free GenAI Agent Engineering lessons
Web APIs & Services for GenAI Engineers 8.1% of the curriculum
In scope for GenAI Agent Engineering: chapters 1–10 — 60 of this path’s 744 lessons. 12 free GenAI Agent Engineering lessons
GenAI Agent Engineering 66.4% of the curriculum
- The Dev EnvironmentIsolate your agent's Python environmentSecure API key management
- The Async Foundation
- The Type System
- The Data ValidatorWrite custom Pydantic validatorsParse LLM output into Pydantic modelsConfigure agents with Pydantic Settings
- The Error Handler
- The Test Writer
- The DebuggerProfile agent performanceDebug agent-specific issues
- The HTTP ClientBuild HTTP-based agent tools
- The LLM ClientPractical use cases — security, parameters, observability
- Prompt CachingDesign cache-friendly prompt architectures
- The Tool DefinerUse Pydantic for tool schemas
- The Raw Agent LoopBuild with LangGraph StateGraph
- The Prompt Engineer (Dynamic)Build prompt versioning and A/B testing
- The Pydantic ToolGenerate JSON Schema from Pydantic modelsBuild a Pydantic tool library
- The MCP ServerCreate an MCP server with lifecycle managementDefine MCP toolsImplement MCP resources
- The MCP ClientManage MCP server lifecycleBuild multi-server MCP clients
- Semantic MemoryBuild semantic memory with LangGraph
- The Subgraph (Composition)Build a multi-agent orchestrator
- The Supervisor PatternManage inter-agent communication
- The Hierarchical PatternDesign hierarchical agent architectures
- Input GuardrailsDesign layered guardrail architecturesImplement policy-based guardrails
- Prompt Injection DefenseImplement canary tokens
- Tracing & ObservabilityIntegrate with Langfuse
- Podman & Containerization for K8sBuild multi-container agent pods
- 24 of 81 chapters shownand 57 more
Core responsibilities this discipline prepares you for. Open one to see how you build it.
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
Each group becomes a signed node on your public skill graph — a recruiter-auditable record of the labs you actually passed. Share the URL; they see the discipline, the labs, and the signature.
The irreducible core of an agent: function calling, Pydantic-typed tool definitions, multi-tool and parallel calls, sandboxed/safe tool execution, and MCP servers + clients.
Agent reasoning and planning patterns: ReAct, planner-executor, reflection/self-critique, chain-of-thought, task decomposition, and dynamic re-planning.
The scientific method for non-deterministic agents: trajectory and final-outcome scoring, offline eval sets, LLM-as-judge, regression suites, benchmarks, and test harnesses.
What separates a demo from production: step-level retries, circuit breakers, fallback chains, graceful degradation, structured-output validation + auto-repair, idempotency, and replay/recovery.
Developer-authored agent control flow: state graphs, conditional edges, checkpointers and time-travel, human-in-the-loop gates, streaming, and subgraph composition.
The core craft of long-running agents: context-window engineering (compaction, pruning, managing context rot), long-term/semantic/episodic memory, summarization, and retrieval/RAG as a tool.
Coordinating multiple agents when one isn't enough: supervisor/router patterns, hierarchical organizations, agent-to-agent handoffs and delegation, and result aggregation.
Keeping autonomous agents within bounds: input/output guardrails, prompt-injection defense, PII scrubbing, content moderation, action allow/deny boundaries, and policy enforcement.
Running agents in production: tracing (OpenTelemetry/Langfuse/Logfire), step-level logging, fleet dashboards and alerting, per-agent token budgets, loop-termination on budget, and versioning/rollback.
Agents that act in an external environment — computer-use, web-browsing, and code agents — with screenshotting, action verification, and environment recovery.
Shipping agents as services: HTTP/WebSocket endpoints (FastAPI), containerization, deploy + autoscale, and CI/CD for agent code.
Baseline LLM access from agent code: OpenAI/Anthropic/Gemini SDK calls, auth, structured outputs, sampling control, and unified multi-provider interfaces.
Production-grade Python applied to agents: async/asyncio, type hints, Pydantic, dataclasses, decorators, context managers — the language fluency to ship agent code.