Gain hands-on skills in taking AI agents from prototype to production, learning how to test, deploy, monitor, and improve systems.
Duration
3 weeksWeekly study
3 hours100% online
How it worksIncluded in an ExpertTrack
Course 4 of 4
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.
Syllabus
Week 1
Assess Agent Quality, Safety and Reliability
Step Into Agent Evaluation and Deployment
Learn what this course covers and why evaluation, deployment, monitoring, safety and cost control matter when agentic AI systems move toward production.
Evaluate Agent Behaviour and Output Quality
Learn how to assess agentic AI systems beyond final answers. Review agent workflow quality, tool usage, decision-making, safety and task completion, then build an LLM-as-a-judge evaluation pipeline.
Test Agents for Behaviour Changes
Learn how regression testing protects important agent behaviours across releases. Use golden tests to check routes, statuses, tools used, escalation, blocked inputs and failure signals.
Week 2
Deploy and Monitor Agentic AI Applications
Move from Prototype to Production
Learn what changes when an agent moves from a controlled prototype to a real production environment. Focus on unpredictable inputs, tool failures, secrets, latency, cost, scaling and observability.
Containerise and Validate Agent Applications
Learn how Docker packages an agent, dependencies and runtime into a consistent environment. Then validate the containerised application to confirm that core agent behaviours still work after deployment.
Monitor Agent Behaviour with LangSmith
Learn how observability helps teams inspect what happens inside an agent run. Review traces, tool calls, latency, token usage, errors and evaluation results using LangSmith.
Week 3
Scale Agents with Safety and Cost Controls
Add Guardrails for Safer Agent Workflows
Learn how guardrails protect agentic AI systems from unsafe requests, prompt injection, sensitive data exposure, out-of-scope questions and risky generated outputs.
Optimise Agents for Cost and Scale
Learn how cost-aware agent systems use model routing and caching. Route simple requests to cheaper models, send complex requests to stronger models and reuse cached responses when safe.
Prepare Production-Ready Agentic AI Systems
Learn how evaluation, testing, deployment, monitoring, guardrails and cost optimisation work together as a production-readiness framework for agentic AI systems.
Prove you're job ready
Highlight the new, job-relevant skills you’ve gained and supplement existing qualifications with a hard-earned, industry-specific digital certificate – plus one for every course within your ExpertTrack.
- Learn the latest in your chosen industry or subject.
- Complete each course and pass assessments.
- Receive certificates validated by the educating organisation.
- Impress employers with learning outcomes you can add to your CV.
- Make your career dreams a reality.
Download a PDF
Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- 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.
Who is the course for?
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.
What software or tools do you need?
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.
Who will you learn with?
Who developed the course?
Edureka
Edureka is a global online learning platform offering industry-focused training in emerging technologies such as Cloud, AI, GenAI, Data Science, DevOps, and Cybersecurity, helping professionals upskill and advance their careers.
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Learning on FutureLearn
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Join a global classroom
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Map your progress
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- Whenever you’re ready, mark each step as complete, you’re in control
- Complete 90% of course steps and all of the assessments to earn your certificate
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