Building the information foundation for intelligent agent systems
By the end of this module, you will be able to:
Welcome to Module 3 of the Agentic AI Systems Architect course. In this module, we'll explore the data structures and knowledge representation techniques that form the foundation of intelligent agent systems.
How agents represent, organize, and access information fundamentally shapes their capabilities. The right data structures enable agents to efficiently store knowledge, reason about complex relationships, make decisions under uncertainty, and retrieve relevant information when needed.
We'll examine several key approaches to knowledge representation in agentic systems, from structured knowledge graphs to distributed vector embeddings, from probabilistic models to symbolic representations. We'll also explore how these approaches can be combined in hybrid systems that leverage the strengths of different representation methods.
How do different knowledge representation approaches influence an agent's ability to understand, reason, and act in its environment?
Keep this question in mind as we explore various data structures and representation techniques in this module.
Data structures serve several critical functions in agentic AI systems:
Providing efficient ways to store and organize the agent's knowledge base, including facts, rules, and experiences.
Enabling the agent to quickly find and access relevant information when needed for reasoning or decision-making.
Capturing the relationships and connections between different pieces of information or entities.
Facilitating different forms of reasoning, from deductive logic to probabilistic inference to analogical reasoning.
The choice of data structures and representation approaches significantly impacts an agent's capabilities, efficiency, and limitations. Different representation methods excel at different aspects of knowledge management:
| Representation Approach | Strengths | Limitations | Typical Applications |
|---|---|---|---|
| Knowledge Graphs | Explicit relationship representation, structured reasoning, interpretability | Scaling challenges, maintenance complexity, limited handling of uncertainty | Domain knowledge representation, semantic search, question answering |
| Vector Embeddings | Semantic similarity, handling unstructured data, efficient retrieval | Limited explicit reasoning, black-box nature, drift over time | Semantic search, recommendation systems, information retrieval |
| Markov Decision Processes | Sequential decision modeling, handling uncertainty, optimization | State space explosion, modeling complexity, computational demands | Planning under uncertainty, reinforcement learning, resource allocation |
| Symbolic Representations | Formal reasoning, explainability, rule-based inference | Brittleness, knowledge acquisition bottleneck, handling ambiguity |