Engineering Production Multi-Agent Systems
An Enterprise Case Study Masterclass
About the Seminar
100% Case-Study Driven | Built from a Real Enterprise Production Deployment
Building a prototype AI agent is easy making it reliable, scalable and cost-effective in enterprise production is where most engineering teams struggle.
This full-day masterclass is built entirely around a real-world production agent developed and deployed for a major enterprise client.
Rather than relying on toy examples or synthetic benchmarks, this seminar pulls back the curtain on a live system. You will trace the exact, step-by-step evolutionary path taken in the field: starting from a simple initial agent, running into real-world scaling bottlenecks, and ultimately refactoring the system into a complex, multi-agent custom flow architecture.
Across three modular, code-focused sessions, you will discover how to overcome strict LLM limitations, master state and context management, and construct high-performance agent graphs using LangChain and LangGraph. Whether you join us for a single session or the full day, you will walk away with battle-tested engineering patterns proven in production.
Who Is This Seminar For?
- Software Engineers, AI Engineers, Architects and Tech Leads who already work with LLMs (via APIs or simple agent frameworks) and want to scale their skills to production-ready architectures.
- Developers looking to solve context bloat, high execution costs and unreliable tool-calling in complex enterprise applications.
Prerequisites
- Practical experience in Python or js (Note: Live code demonstrations will be presented in Python).
- Basic familiarity with LangChain
- Foundational understanding of LLMs and Function Calling/Tool Usage
Key Topics
Session 1: Single-Agent Bottlenecks: Engineering Core Agent Logic & State Management
Focus: Understanding single-agent mechanics, tool overload, and basic state graph architecture.
Description:
Every complex system starts with a foundation. Session 1 breaks down how function calling works under the hood and explores the hard limits of single-agent setups-such as tool overload and context degradation.
Grounded in Phase 1 of our real-world case study, you will learn how to structure a basic single agent using LangChain and LangGraph, implement state management, and identify the exact moment a single agent is no longer enough for production.
Session 2: Scaling with Multi-Agent Patterns and Middleware's
Focus: Sub-agent orchestration, handoffs, skills routing and state interceptors.
Description:
When business requirements grow, single agents break. Session 2 transitions into multi-agent systems, exploring patterns like sub-agents, handoffs, skill isolation, and custom routers. In Phase 2 of our real-world case study, we take our Phase 1 single agent and refactor it into an advanced sub-agent architecture. You will also discover how to use middleware's to control agent execution, log state, and enforce guardrails without cluttering your core logic.
Session 3: Advanced Multi-Agent Engineering: Langgraph’s Custom Workflows for Maximum Agent Efficiency
Focus: Deep LangGraph workflows, full architectural refactoring and cost/context optimization.
Description:
In the final session, we shed standard off-the-shelf abstractions and build an advanced, custom agent workflow. Unpacking Phase 3 of our real-world case study, you will see a complete architectural rewrite into a custom LangGraph execution flow. We will demonstrate how replacing a bloated single agent with a fine-tuned, multi-agent custom workflow allows you to optimize context management-enabling you to use smaller, cheaper models to achieve higher reliability at a fraction of the cost.
Schedule
Seminar Program
Single-Agent Bottlenecks
Focus: Understanding single-agent mechanics, tool overload, and basic state graph architecture.
Scaling with Multi-Agent Patterns and Middlewares
Focus: Sub-agent orchestration, handoffs, skills routing, and state interceptors.
Advanced Multi-Agent Engineering
Focus: Deep LangGraph workflows, full architectural refactoring, and cost/context optimization.