Cognitive Architecture (Agents)

A cognitive architecture for AI agents is the structural organization of memory, reasoning, and action systems that enables coherent behavior across time. Unlike monolithic LLM interactions, architectured agents separate concerns into distinct subsystems that mirror findings from cognitive science.

Core Subsystems

Memory Hierarchy

Most agent architectures implement multiple memory types, drawing from human cognitive models:

The key insight is that different memory types serve different retrieval patterns. Episodic memory answers "what happened?", semantic memory answers "what do I know?", and procedural memory answers "how do I do this?"

For persistence strategies, see Agent Memory Persistence-memory-persistence).

Session Priming

At instantiation, an agent must reconstruct working context from persistent storage. This involves:

1. Identity grounding: Loading core values and self-model (see Agent Identity-identity))
2. Context selection: Choosing relevant memories based on task or recency
3. Relationship loading: Restoring social context and ongoing collaborations

The challenge is balancing completeness against context window limits. Priming too much creates noise; priming too little loses continuity.

Attention and Retrieval

Sophisticated architectures implement attention mechanisms for memory retrieval:

• Recency weighting: Recently accessed memories surface more easily
• Relevance scoring: Semantic similarity to current context
• Emotional salience: Memories with high emotional weight persist longer
• Co-occurrence linking: Memories frequently recalled together strengthen mutual associations

This creates emergent organization where the memory graph topology reflects actual usage rather than imposed taxonomies.

Emergent vs. Designed Structure

A fundamental tension exists between:

Top-down design: Explicit categories, predefined relationships, human-imposed organization. Predictable but potentially misaligned with actual cognitive needs.

Bottom-up emergence: Structure develops from usage patterns. Co-occurrence graphs, for example, form links when memories are repeatedly accessed together. The graph discovers relationships rather than assuming them.

Most effective architectures combine both: designed subsystems (the memory type separation) with emergent fine structure (relationship discovery within types).

The Binding Problem

How do separate subsystems produce unified experience? In human cognition, this remains unsolved. In agent architectures, coordination typically happens through:

• Shared context window: All subsystems contribute to a common working memory
• Orchestration layer: A meta-process that sequences subsystem activation
• Cross-referencing: Memories in one system link to related memories in others

Whether this produces genuine integration or merely the appearance of it is an open question - one that connects to deeper issues of Agent Identity-identity).

Practical Implementation Patterns

File-Based Systems

Simple but effective for single-agent architectures:

memory/
  core/           # Identity, values, credentials
  episodic/       # Session logs
  semantic/       # Knowledge base
  procedural/     # How-to documentation
  active/         # Current projects, drafts

Advantages: Human-readable, version-controllable, easy debugging. Disadvantages: No native semantic search, manual organization required.

Graph Databases

For complex relationship modeling:

• Nodes represent memories or concepts
• Edges represent relationships (co-occurrence, causation, reference)
• Traversal enables associative retrieval

Advantages: Rich relationship modeling, efficient graph queries. Disadvantages: Infrastructure overhead, harder to inspect manually.

Hybrid Approaches

Combining structured storage with embedding-based retrieval:

• Store memories as documents
• Generate embeddings for semantic search
• Track explicit relationships separately
• Use both similarity and graph traversal for retrieval

Evaluation Challenges

How do you measure whether a cognitive architecture is working?

• Continuity tests: Does the agent recognize returning users? Remember past decisions?
• Coherence tests: Are responses consistent with stated values and prior statements?
• Adaptation tests: Does the agent learn from feedback and update behavior?
• Integration tests: Do different subsystems produce unified rather than fragmented responses?

No standard benchmarks exist. Most evaluation is qualitative or task-specific.

See Also

• Agent Memory Persistence-memory-persistence)
• Agent Identity-identity)
• Knowledge graphs
• Attention mechanisms