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Production Agentic AI and System Engineering

This course is designed for SDET, software developers, and solution architects who want to build production-grade, agentic AI systems using Claude and modern orchestration frameworks. It’s ideal for professionals with solid Python and API experience who are looking to master LLM integrations, multi-agent architectures (CrewAI, LangGraph), RAG pipelines, and enterprise deployment practices. Technical leads and DevOps/MLOps engineers exploring AI observability and production readiness will also find strong value in this program.

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Course Includes

  • 40 Hrs of Sessions
  • 24/7 Lifetime Support
  • One-on-one doubt clearing
  • Hands-On Driven
  • Certification Based Curriculum
  • Career path guidance
  • Real Time Users
  • Flexible Schedules

Why Choose TM SQUARE?

TM Square is a leading Agentic AI training provider, helping developers, SDETs, and solution architects build production-ready AI agents and multi-agent systems. Our Claude AI training combines prompt engineering, LLM integration, RAG pipelines, and enterprise AI deployment into one hands-on program, covering tools like Claude Code, LangGraph, CrewAI, and AI observability platforms such as LangSmith and OpenTelemetry. Participants build real systems through hands-on labs and a capstone project, including an enterprise RAG pipeline and an autonomous DevOps remediation platform. Backed by a proven track record training professionals at global enterprises like Deloitte, EY, KPMG, Siemens, Bosch, and Samsung, and with offices across India, South Africa, and Australia, TM Square delivers live virtual training designed for SDETs, AI engineers, DevOps/MLOps professionals, and technical leads pursuing real-world AI deployment skills.

 

Course Overview

This comprehensive syllabus is engineered for software engineers, AI architects, and tech leads looking to build, deploy, and scale production-grade AI agents and multi-agent teams. By combining Claude’s reasoning capabilities with LangChain, CrewAI, LangGraph, OpenAPI standards, and Claude Code, participants transition from basic prompt engineering to orchestrating complex, autonomous, stateful workflows.

 

Course Outline

 

What is your return policy?

We offer a 30-day return policy from the date of purchase.

Do I need a receipt to return an item?

Yes, a valid receipt or proof of purchase is required.

Can I return online purchases in store?

Yes, online purchases can be returned in any of our physical stores.

How long does shipping take?

Standard shipping typically takes 3–5 business days.

Do you ship internationally?

Yes, we ship to most countries worldwide. Additional charges may apply.

Which countries are excluded?

We currently do not ship to embargoed countries or regions with postal restrictions.

Module 1: Claude Architecture & OpenAPI Function Calling

Focus: Mastering Anthropic's model suite and converting REST APIs into structured AI actions.
Claude 3.5/3.7 Ecosystem: Deep dive into Sonnet, Haiku, and Opus; prompt design patterns, XML tag structuring, and system instructions.
Structured Data & Vision: Enforcing JSON outputs, schema validation, multi-modal reasoning, and extended context window optimizations.
OpenAPI Specifications for Tool Use: Parsing OpenAPI 3.0/3.1 JSON/YAML schemas into LLM tool definitions.
Dynamically constructing API request payloads from natural language. Handling authentication headers, parameter validation, and status code error recovery.
Hands-on Lab: Build a dynamic tool execution bridge that automatically generates Claude-compatible functions from any OpenAPI spec.
Module 2: Core Infrastructure with LangChain

Focus: Establishing foundational pipelines, memory, and retrieval mechanisms using the LangChain ecosystem.
LangChain Expression Language (LCEL): Compositional syntax, runnable interfaces, pipe syntax (|), and asynchronous execution.
Tools, Loaders, and Vector Stores: Wrapping external services into unified tools, standardizing document chunking, and enterprise RAG setup.
Chat History & Short-term Memory: Managing context windows, token truncation strategies, and conversational state persistence.
Hands-on Lab: Construct an Enterprise Knowledge RAG pipeline equipped with OpenAPI-backed database lookups.
Module 3: Terminal-First AI Engineering with Claude Code

Focus: Integrating Anthropic’s CLI agent directly into developer workflows for automated refactoring, repo analysis, and task execution.
Claude Code Fundamentals: CLI installation, authentication, project navigation, and configuration files (CLAUDE.md).
Repository Analysis & Multi-file Operations: Automated code auditing, test generation, and pull request creation directly from the command line.
Model Context Protocol (MCP): Connecting Claude Code to custom tools, local databases, and enterprise platforms (Jira, GitHub, Slack).
Hooks & Custom Skills: Scripting automated pre/post-execution hooks and building reusable developer agent commands.
Hands-on Lab: Use Claude Code CLI to perform a full security audit, generate unit test coverage, and automate PR generation on a legacy repository.
Module 4: Role-Based Multi-Agent Teams with CrewAI

Focus: Rapidly orchestrating multi-agent collaboration using intuitive role-playing patterns.CrewAI
Architecture: Defining Agents (Role, Goal, Backstory), Tasks, and Crews.
Delegation & Communication: Sequential vs. Hierarchical workflows, task delegation logic, and inter-agent memory sharing.
Integrating Tools: Binding OpenAPI specs and LangChain tools to specific CrewAI roles.
Hands-on Lab: Build an autonomous Market Intelligence Crew consisting of a Research Agent, Data Synthesizer Agent, and Executive Report Writer Agent.
Module 5: AI-assisted automation using Selenium/Playwright

Focus: Leveraging AI and LLMs to generate, self-heal, and execute resilient end-to-end web testing suites.
AI-Driven Test Generation: Prompting LLMs to automatically generate Playwright or Selenium test scripts from user stories, wireframes, or natural language specifications.
Self-Healing Test Locators: Implementing intelligent locator strategies that dynamically adapt to DOM changes, CSS updates, and structural application redresses.
Visual Regression & AI Assertions: Using computer vision and multimodal models to detect unintended UI defects, layout shifts, and rendering anomalies.
Autonomous Test Maintenance: Scripting AI agents to analyze test failure logs, trace files, and screenshots to suggest and apply automated bug fixes.
Hands-on Lab: Build an intelligent Playwright test suite for a dynamic e-commerce web application featuring self-healing locators and automated visual regression detection.
Module 6: API testing with AI

Focus: Automating API discovery, comprehensive test case generation, and mock server orchestration using AI.
AI-Powered Contract & Schema Testing: Using LLMs to parse OpenAPI/Swagger specifications, GraphQL schemas, and documentation to automatically uncover edge cases and security vulnerabilities.
Automated Payload & Mock Generation: Generating realistic test data, complex JSON payloads, and dynamic mock APIs using generative AI models.
Intelligent Fuzzing & Security Auditing: Deploying AI-driven fuzz testing to discover hidden injection flaws, rate-limiting bugs, and authentication bypasses in REST and GraphQL endpoints.
CI/CD Pipeline Integration: Embedding AI-generated API test suites into GitHub Actions or GitLab CI to ensure continuous contract compliance.
Hands-on Lab: Generate a comprehensive AI-driven API test suite for a microservices backend, implement intelligent fuzzing, and integrate automated validation into a CI/CD pipeline.
Module 7: Stateful Agent Graphs with LangGraph

Focus: Designing fine-grained, cyclical, and resilient multi-agent graph workflows for production environments.
Graph Fundamentals: State schemas (TypedDict / Pydantic), Nodes (functions/tools), Edges (conditional routing, cycles).
State Management & Persistence: Checkpointing (PostgreSQL / SQLite), state travel, and session thread isolation.
Human-in-the-Loop (HITL): Breakpoints, interrupting execution for approval, state editing, and dynamic retry mechanisms.
Multi-Agent Systems in LangGraph: Router-based, Supervisor, and Network (Peer-to-Peer) graph architectures.
Hands-on Lab: Construct an end-to-end Autonomous Code Remediation Engine that runs tests, loops on failures, and pauses for human verification before deployment.
Module 8: Production Engineering, Observability & Capstone

Focus: Operationalizing multi-agent architectures for reliability, monitoring, and enterprise deployment.
Framework Selection Guide: Determining when to use CrewAI (fast, team-centric) vs. LangGraph (granular control, cyclical flows).
Observability & Tracing: Monitoring token usage, latency, and agent decision paths using LangSmith and OpenTelemetry.
Guardrails & Security: Input sanitization, output validation, rate limiting, and VPC/Enterprise compliance patterns.
Deployment: Containerizing agents, deploying via FastAPI/LangGraph Platform, and CI/CD integration.
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