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Course Outline
Foundations of Autonomous Agents
- Core concepts behind agentic AI
- Types of autonomous agent frameworks
- Emerging research directions
Inside BabyAGI
- Task generation and prioritization logic
- Execution loops and memory structures
- Strengths and constraints of the BabyAGI design
Comparing BabyAGI with Other Agents
- LLM-based task agents and planners
- Multi-agent orchestration frameworks
- Reactive vs deliberative agent models
Evaluating Autonomy and Control
- Autonomy levels in AI systems
- Human-in-the-loop and oversight models
- Failure modes and risk factors
Real-World Applications and Use Cases
- Research automation
- Enterprise knowledge workflows
- Autonomous exploration and reasoning tasks
Benchmarking and Performance Assessment
- Criteria for evaluating autonomous agents
- Stress-testing and behavioral analysis
- Comparative assessment methodologies
Designing and Deploying Agentic Systems
- Architectural considerations
- Integration with organizational tooling
- Scalability and operational management
Future Trajectories in AI Autonomy
- Evolution of agentic frameworks
- Potential breakthroughs and constraints
- Strategic implications for research and industry
Summary and Next Steps
Requirements
- An understanding of advanced AI concepts
- Experience with machine learning workflows
- Familiarity with autonomous agent architectures
Audience
- AI researchers
- Innovation leaders
- AI strategists
14 Hours
