What Do We Mean by AGI?
- Definitions
and misconceptions
- Narrow
AI vs general intelligence
- Current
state of AGI research
- Practical
enterprise implications
Architectures for General-Purpose AI Systems
- Modular
AI architectures
- Service-oriented
vs monolithic AI systems
- AI
platform vs AI application
- Layered
intelligence architectures
Cognitive Architecture Concepts
- Perception,
memory, reasoning, and action loops
- Symbolic
+ neural hybrid systems
- Blackboard
architectures
- Cognitive
pipelines
Agent-Based System Design
- Single-agent
vs multi-agent architectures
- Planner–executor–critic
patterns
- Tool-using
agents
- Long-running
agent processes
Memory Systems for General Intelligence
- Short-term
vs long-term memory
- Vector
memory and episodic memory
- Knowledge
stores and retrieval systems
- Memory
governance and lifecycle
Planning, Reasoning & Decision Systems
- Task
planning architectures
- Hierarchical
planning
- Reasoning
chains and verification
- Goal
management systems
World Models & Environment Interaction
- Simulated
environments
- Tool
and API interaction as environment
- Feedback
loops
- Learning
from interaction
Multi-Modal Intelligence Architectures
- Vision,
language, and audio integration
- Cross-modal
reasoning
- Multimodal
perception pipelines
- Sensor-to-action
architectures
Learning Systems & Adaptation
- Continual
learning concepts
- Feedback-driven
system improvement
- Online
vs offline learning
- Guarded
adaptation strategies
Evaluation & Benchmarking for General Systems
- Measuring
generalization
- Task
diversity benchmarks
- Robustness
and stress testing
- Regression
testing for intelligent systems
Safety-by-Design for AGI-Oriented Systems
- Alignment
concepts
- Human-in-the-loop
architectures
- Oversight
and control mechanisms
- Containment
and capability boundaries
Governance, Ethics & Compliance
- AI
governance frameworks
- Documentation
and auditability
- Accountability
models
- Responsible
research practices
Platform Engineering for AGI-Oriented Systems
- Building
extensible AI platforms
- Plugin
and tool ecosystems
- Model
abstraction layers
- Versioning
and lifecycle management
Research-to-Production Pathways
- From
prototypes to platforms
- Managing
experimental features
- Risk
assessment for advanced capabilities
- Controlled
rollout strategies
Hands-on Exercises
Summary and Conclusion
