16 Hrs
Total Duration
6
Training Modules
6
Hands-on Labs
5+
Roles This Fits
Course Overview
This program is built for professionals who want to move beyond LLM basics and learn how to design, deploy, and monitor real, production-ready AI agents — covering the full lifecycle from architecture and planning to deployment, observability, and enterprise-scale optimization.
Who Should Attend
- AI/ML Engineers
- Data Scientists and Data Engineers
- Software Developers building automation or copilots
- Solution Architects and Technical Product Managers
- Innovation, R&D, and Automation Teams
Prerequisites & System Requirements
Software / Tools
- Python 3.10+
- VS Code or Jupyter Notebook
- LangChain or OpenAI Assistants framework
- Docker (optional) for containerized deployment
Hardware
- Laptop with minimum 8 GB RAM (16 GB recommended)
- Stable high-speed internet connection
Prerequisites
- Basic Python programming knowledge
- Understanding of APIs and JSON data formats
- Foundational concepts of LLMs and machine learning
- Familiarity with command line and web applications (preferred)
Learning Objectives
Participants will be able to:
Understand the fundamentals of agentic AI architecture
Design AI agents with planning, reasoning, and tool-use capabilities
Integrate memory systems and vector databases
Deploy agents as APIs or services
Implement monitoring, logging, and guardrails for safe agent operations
Optimize agent performance and ensure reliability in real-world environments
Training Outline
1Understanding Agentic AI
- What makes an AI agent "agentic"
- Reasoning, planning, autonomy, and tool use
- Key components of an agentic system
- Real-world applications and industry examples
2Designing AI Agents
- Defining goals, constraints, and workflows
- Planning mechanisms (ReAct, plan-and-execute, toolformer logic)
- Tool selection and API integration
- Memory design: short-term vs long-term
3Building Agentic Workflows
- Framework overview: LangChain, OpenAI Assistants, custom architectures
- Creating structured system prompts
- Adding tools, functions, and external APIs
- Implementing memory with vector embeddings
4Deploying AI Agents
- Packaging agents using FastAPI or Flask
- Exposing an agent as an API endpoint
- Containerization basics with Docker
- Deployment approaches (cloud, on-prem, hybrid)
5Monitoring & Observability
- Logging and monitoring best practices
- Tracking agent actions, decisions, and tool calls
- Guardrails: validation, safety layers, rate limits
- Troubleshooting failures and debugging agent behavior
6Optimization, Scalability & Governance
- Latency and cost optimization strategies
- Parallelization and batching
- Secure API handling and compliance
- Scaling agents for enterprise workloads
Ready to build production-ready AI agents?
Join Cogent University's 16-hour Agentic AI workshop and go from concept to deployed, monitored agent.