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

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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