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There are 3 modules in this course
Kickstart your journey in AI agent design by mastering foundational architectures and practical implementation tactics. Learn to decompose systems for effective design, map business goals to technical objectives, and visualize agent-environment interactions for stakeholder communication. Build skills in state encoding, action selection, and rigorous agent evaluation, ensuring you can confidently create baseline models for benchmark tasks and rapidly iterate toward practical solutions that address business needs in global contexts.
In this module, you’ll explore the foundational design patterns and tools that power real-world, agent-based AI systems. You’ll learn how to translate complex organizational goals into functional agent architectures by using proven decomposition and modeling techniques. Through hands-on practice with UML diagrams, workflow schematics, and requirements analysis, you’ll gain clarity and control over agent-environment relationships. This module arms you with the skills to bridge technical and business priorities, setting the stage for robust, actionable solutions in any data-driven setting.
What's included
9 videos1 reading2 assignments
Show info about module content
9 videos•Total 19 minutes
Welcome to Agent-Based AI Systems!•3 minutes
Introduction to AI Agent Design•1 minute
Use modular decomposition to design scalable agent systems•2 minutes
Map stakeholder priorities to agent capabilities with requirements analysis•2 minutes
Apply UML and workflow diagrams to clarify agent interactions•2 minutes
Conduct business goal alignment using user story mapping•2 minutes
Select performance metrics tailored for agent-centric tasks•2 minutes
Facilitate cross-functional consensus around agent design proposals•2 minutes
Introduction to AI Agent Design - Summary•3 minutes
1 reading•Total 5 minutes
Action Story: Breaking Down a Messy Agent Design Problem•5 minutes
2 assignments•Total 30 minutes
Agent Architectures and Functional Design•10 minutes
Introduction to AI Agent Design - Exam•20 minutes
Action Selection and State Representation
Module 2•1 hour to complete
Module details
In this module, you will master the critical skills of action selection and state representation—cornerstones of powerful agent-based AI systems. Through hands-on exercises, you’ll learn to transform real-world scenarios into precise state-action frameworks using advanced feature engineering, dimensionality reduction, and state-of-the-art ML algorithms. By simulating and visualizing agent decisions, you’ll build models that are not only highly accurate but also responsive and robust in dynamic environments. This module empowers you to confidently bridge the gap between complex data and high-impact agent behaviors.
What's included
7 videos1 reading2 assignments
Show info about module content
7 videos•Total 10 minutes
Action Selection and State Representation•1 minute
Perform dimensionality reduction for agent state feature selection•2 minutes
Engineer composite states for high-fidelity environmental modeling•1 minute
Utilize PCA and t-SNE to visualize agent state spaces•1 minute
Implement greedy, stochastic, and hybrid action policies•2 minutes
Validate state-action mappings via simulation-based testing•1 minute
Optimize policy search with evolutionary algorithm techniques•1 minute
1 reading•Total 8 minutes
Action Story: Cutting Through the Noise in State Representation•8 minutes
2 assignments•Total 26 minutes
State Representation Strategies and Feature Engineering•6 minutes
Managing Sales Cycles and Pipeline - Exam•20 minutes
Evaluation and Baseline Optimization
Module 3•1 hour to complete
Module details
Move beyond building agents—learn to benchmark, test, and refine them to elevate results in real-world scenarios. In this module, you will build baseline models, apply industry-standard evaluation frameworks, and use data-driven methods to pinpoint and remedy weaknesses in agent performance. By mastering rapid prototyping, agile iteration, and continuous feedback, you’ll transform simple agents into robust solutions that improve with every cycle. Develop the confidence to produce models that not only work but continually outperform expectations.
What's included
8 videos1 reading2 assignments
Show info about module content
8 videos•Total 12 minutes
Evaluation and Baseline Optimization•1 minute
Construct rule-based baseline agents for initial comparison•2 minutes
Measure agent performance with domain-specific evaluation metrics•1 minute
Conduct error analysis to pinpoint improvement opportunities•2 minutes
Develop agile prototyping cycles for agent enhancement•1 minute
Implement A/B testing to optimize agent heuristics•1 minute
Leverage feedback-driven development for incremental gains•1 minute
Next: Dynamic Decisions•3 minutes
1 reading•Total 6 minutes
Action Story: Setting the Right Benchmark Before Scaling•6 minutes
2 assignments•Total 30 minutes
Build and Evaluate Baseline Agents•10 minutes
Evaluation and Baseline Optimization - Exam•20 minutes
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What will I get if I subscribe to this Specialization?
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Is financial aid available?
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