Agentic AI: Evaluating, Deploying, and Scaling

Learn to evaluate and deploy production-ready AI agents

On this three-week course, you’ll explore the final stage of the AI agent lifecycle, transforming prototypes into fully operational products. You’ll learn how to evaluate AI agents, deploy them to production environments, and optimise their performance at scale.

Through hands-on projects, you’ll develop practical skills building end-to-end workflows for AI agent evaluation, deployment, monitoring, and continuous improvement.

Explore AI agent evaluation and observability

This course will help you design automated evaluation pipelines using LLM-as-a-Judge and create regression test suites that identify quality issues before it affects production systems.

You’ll explore key evaluation metrics while learning how to implement monitoring, tracing, and alerting using LangSmith dashboards. This will help you gain the skills needed to continuously assess and improve AI agents.

Deploy and monitor Agentic AI systems

Next, you’ll gain hands-on experience containerising AI agents with Docker and deploying them.

Through practical exercises, you’ll learn how to monitor deployed agents, configure alerts, and build resilient production environments that support reliable AI agent deployment.

Build secure and scalable AI systems

Successful Agentic AI systems must be safe, efficient, and capable of operating at scale.

You’ll implement AI guardrails to ensure agents behave responsibly in production environments. You’ll also design human oversight processes and apply scaling strategies.

This course is for technology enthusiasts, software developers, data professionals, product managers, and business leaders who want to understand agentic AI.

Learners should be familiar with AI agent workflows, Python, APIs, tool integration, and basic command-line usage. Some awareness of Docker, testing, and monitoring concepts will be helpful.

Learners need a computer (Windows, macOS, or Linux) with at least 8 GB RAM and a stable internet connection. Python 3.10 or later should be installed, along with a code editor such as VS Code. No paid software or specialised hardware is required.

This course is for technology enthusiasts, software developers, data professionals, product managers, and business leaders who want to understand agentic AI.

Learners should be familiar with AI agent workflows, Python, APIs, tool integration, and basic command-line usage. Some awareness of Docker, testing, and monitoring concepts will be helpful.

• Apply evaluation and deployment techniques to agentic AI systems.
• Assess AI agent behaviour through workflow execution, tool usage, and task completion.
• Evaluate the reliability, safety, and performance of AI agents in production.
• Create production-ready AI agent workflows using deployment, monitoring, and optimisation practices.
• Design scalable AI agent systems with testing, guardrails, and cost optimisation strategies.