Advanced AI Agents

Duration: 3 Days

Description

This advanced course is for developers building sophisticated, reliable AI agents and multi-agent systems for what practitioners now call the agent engineering phase of AI development. Building on agent fundamentals, participants explore advanced planning and reasoning patterns, multi-agent coordination, robust tool and memory architectures, and the evaluation and guardrails needed to run agents responsibly. The course emphasizes the hard problems of real agent systems: reliability, observability, safety, and cost, and gives participants the patterns to build agents that work beyond a demo and hold up under real-world, multi-file, autonomous workloads.
Class time is divided between instructor-led explanation (35 percent) and hands-on development (65 percent).

Audience

Experienced developers who have built basic agents and want to design production-grade, advanced, and multi-agent systems. Assumes solid AI development experience, including prior work with a coding agent such as Claude Code.

Objectives

• Apply advanced planning and reasoning patterns for agents
• Design multi-agent systems and coordination strategies
• Build robust tool, memory, and context architectures
• Evaluate agent performance and reliability systematically
• Implement guardrails, safety, and human oversight
• Address observability, cost, and operations for agent systems

Prerequisites

Participants should have built basic agents already. The following are expected:

• Strong Python skills (see Python Foundations)
• Experience building agents (see Introduction to AI Agents)
• Familiarity with an agent framework is helpful (see LangChain & LangGraph)

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

This course builds directly on Introduction to AI Agents. Pair it with AI Security and MLOps & LLMOps to cover the operational side of running agents in production.

Course Outline

• Module 1 – Course Introduction
  • Welcome and objectives
  • From simple agents to advanced systems
  • The hard problems of real agents
• Module 2 – Advanced Reasoning and Planning
  • Planning strategies and decomposition
  • Reflection and self-correction
  • Reasoning patterns and their tradeoffs
  • Hands-on exercise: a planning agent
• Module 3 – Multi-Agent Systems
  • When to use multiple agents
  • Coordination and communication patterns
  • Roles, delegation, and orchestration
  • Hands-on exercise: a multi-agent workflow
• Module 4 – Tools, Memory, and Context
  • Robust tool design and error recovery
  • Long-term and working memory architectures
  • Context management at scale
  • Retrieval and knowledge integration
• Module 5 – Evaluation and Reliability
  • Evaluating agent behavior
  • Testing non-deterministic systems
  • Handling failure, loops, and edge cases
  • Hands-on exercise: build an evaluation harness
• Module 6 – Safety, Guardrails, and Oversight
  • Bounding agent actions
  • Human-in-the-loop and approvals
  • Security and prompt-injection defense
  • Responsible deployment
• Module 7 – Operations at Scale
  • Observability and tracing
  • Cost and performance optimization
  • Monitoring and incident response
  • Maintenance and iteration
• Module 8 – Course Wrap-Up
  • Key takeaways
  • Resources for continued learning
  • Next steps