Description
Building Adaptive AI Agents is a course offered by DeepLearning.AI in collaboration with Oracle. It addresses the common issue of AI agents repeating mistakes due to lack of session-to-session learning. The course provides learners with strategies to build AI agents that carry forward experiences, making them faster and more reliable.
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The course is structured around three adaptation layers: behavior, knowledge, and model. For behavior adaptation, learners build a skill induction pipeline that converts agent traces into reusable skills, with human approval for each skill. Knowledge adaptation involves constructing a code knowledge graph to enhance retrieval in large codebases, surpassing traditional keyword searches. Model adaptation teaches when and how to fine-tune models for optimal performance.
Participants will gain a mental model for adaptive AI agents, understanding the cost of forgetting and identifying areas for improvement. The course includes hands-on labs, requiring familiarity with Python, and offers additional learning features through the DeepLearning.AI Pro plan.
Targeted at individuals building AI agents, the course aims to equip them with the skills to create agents that learn from their own experiences rather than starting from scratch each session. By the end, learners will be able to develop agents that improve measurably over time, reducing repetitive errors and enhancing efficiency.
- CategoryEducation & Research
- Pricing ModelPRO
Building Adaptive AI Agents's Core Features
Skill induction pipeline development
Code knowledge graph construction
Model fine-tuning techniques
Human-approved skill reuse
Graph-based retrieval enhancement
Behavior adaptation strategies
Knowledge adaptation methods
Model adaptation insights
How to use Building Adaptive AI Agents?
Build: Develop skill induction pipeline
Construct: Create code knowledge graph
Adapt: Fine-tune models as needed
Implement: Apply reusable skills
Building Adaptive AI Agents's Use Cases
- Skill Induction
- Code Retrieval
- Model Fine-Tuning
- Behavior Adaptation
- Knowledge Graphs
