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AI Architect: Advanced Agentic Systems & Workflow Integration

Master the architecture of event-driven, asynchronous AI systems. Design multi-agent topologies with LangGraph, implement MCP for enterprise tool binding, and deploy production-grade agentic workflows from end to end.

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4 weeks

What You'll Learn

Design event-driven, asynchronous AI pipelines
Orchestrate multi-agent workflows with LangGraph
Implement MCP for secure enterprise tool connections
Deploy and test end-to-end agentic systems

Course Content

W1
Week 1: Async Infrastructure Foundations
Build the backbone that keeps agents running without blocking.
1
Synchronous vs. Asynchronous AI Basics
Understand why blocking calls cripple agent throughput and how async execution models unlock parallelism in AI pipelines.
2
Event-Driven Topologies for AI
Map out publish-subscribe and event-streaming patterns and learn how agents communicate without tight coupling.
3
Deploying Message Brokers (Kafka/RabbitMQ)
Stand up Kafka and RabbitMQ clusters, configure topics and queues, and route agent messages reliably at scale.
4
Designing Stateful Event Loops
Build event loops that track agent state across turns without losing context between message arrivals.
5
Resiliency and Replay Mechanisms
Implement dead-letter queues, offset management, and replay strategies so no agent task is silently dropped.
Weekly Win
Working Async Agent Pipeline
Deploy a Kafka-backed async event loop where two agents exchange messages without blocking the main thread.
W2
Week 2: Multi-Agent Orchestration Patterns
Coordinate agents that plan, delegate, and remember.
1
The Supervisor-Worker Paradigm
Design a hierarchy where a supervisor agent decomposes goals and delegates sub-tasks to specialized worker agents.
2
LangGraph Fundamentals
Model agent workflows as stateful graphs using LangGraph nodes, edges, and conditional branching.
3
Dynamic Agent Handoff Mechanisms
Implement runtime routing so agents can transfer control based on task type, confidence, or failure conditions.
4
Cross-Session Memory Architecture
Persist agent memory across sessions using vector stores and structured state, enabling long-running multi-turn workflows.
5
Context Compaction Strategies
Summarize and prune long conversation histories so agents stay within context limits without losing critical information.
Weekly Win
Supervisor-Worker LangGraph Pipeline
Build a LangGraph workflow with a supervisor agent that delegates tasks to two worker agents with cross-session memory.
W3
Week 3: MCP & Enterprise Integration
Connect agents to any enterprise system through a single secure protocol.
1
Solving the N-to-N Integration Problem
See why point-to-point integrations break at scale and how a unified protocol layer eliminates the combinatorial explosion.
2
MCP Architecture & Tool Binding
Understand the Model Context Protocol spec and bind tools, resources, and prompts so agents can call them uniformly.
3
Secure Enterprise Connections via MCP
Authenticate and authorize MCP connections with OAuth 2.0, scoped permissions, and audit logging for enterprise compliance.
4
Remote MCP Deployments
Deploy MCP servers as remote microservices accessible over HTTPS, making tools available across distributed agent clusters.
5
Building MCP UI Applications
Create thin front-end clients that expose MCP-connected agent capabilities to end users without coupling to backend logic.
Weekly Win
Secure Remote MCP Server
Deploy an authenticated MCP server exposing three enterprise tools and connect it to your LangGraph agent from Week 2.
W4
Week 4: Capstone — Production Deployment
Ship a fully integrated agentic system to a real environment.
1
Capstone: Infrastructure Setup
Provision cloud infrastructure — message broker, vector store, and compute — using infrastructure-as-code principles.
2
Capstone: Corporate Workspace Integration
Connect the agentic system to a real enterprise tool (Slack, Notion, or Google Workspace) via the MCP server.
3
Capstone: Worker Agent Implementation
Implement specialized worker agents for retrieval, summarization, and action execution within the LangGraph graph.
4
Capstone: Supervisor Orchestration
Wire the supervisor agent to route tasks, handle failures, and escalate to humans when confidence thresholds are not met.
5
Capstone: End-to-End Testing & Deployment
Run integration tests across the full agent pipeline and deploy to a cloud environment with monitoring and alerting.
Weekly Win
Deployed Multi-Agent Corporate System
A live, production-deployed agentic system that autonomously handles a defined corporate workflow from trigger to completion.

Prerequisites

Python proficiency
REST API development
Basic cloud infrastructure knowledge

Hands-on Project

Build a multi-agent corporate workflow system integrating message brokers, supervisor-worker agents, and an MCP-connected enterprise data source.

📚
Advanced Level
Course Price
₹14,999
India
$249
International · One-time payment
Next cohort starts Mar 30
Duration4 weeks
LevelAdvanced
FormatCohort-based
Modules4

What's included:

Live cohort sessions
Hands-on projects
Certificate of completion
Lifetime access
Career support

Part of Learning Track

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AI Architect
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