Sensory memory

As explained in AI Agents in Action, Second Edition

Sensory memory in AI is multimodal retrieval of information from modality-specific indexes such as image or audio stores.

Sensory memory describes multimodal retrieval: finding relevant images, audio, or other non-text data through modality-specific indexes. It is not a separate memory mechanism; it applies the same embed, index, search, and retrieve pattern used for text. section 6.4.1

Figure 6.8 Memory is broken down into various forms, from sensory (visual, auditory, and touch) to short-term conversational memory and long-term episodic, semantic, and implicit/procedural memories

Use the memory taxonomy to contrast sensory inputs with conversational, episodic, semantic, and procedural memory categories. section 6.4.1

A basic sensory-retrieval workflow is:

• Encode an image, audio clip, or query into a modality-appropriate embedding. section 6.4.1
• Store embeddings in a vector-indexed image, audio, or other modality-specific database. section 6.4.1
• Search for similar or relevant items and retrieve them into the agent’s context. section 6.4.1

Similar-image retrieval

Given a current image, an agent searches a vector-indexed store of past images and retrieves the stored image that most resembles it. Audio retrieval follows the same general pattern against a sound database. section 6.4.1

Treat sensory memory as useful when text alone is insufficient, but expect less mature retrieval quality, cross-modal alignment, and tooling than in text retrieval. Production multimodal RAG exists but remains uncommon. section 6.4.1

How AI Agents in Action, Second Edition covers it

How deep it goes

Explains embedding, indexing, searching, and retrieval, while stopping short of mature production guidance for multimodal systems.

Examples

Uses similar-image retrieval and audio lookup as concrete examples of modality-specific search.

What it assumes

Assumes familiarity with text retrieval’s embedding, indexing, and search mechanics, while treating the cognitive analogy as optional shorthand.

What you can do after

Apply multimodal retrieval by embedding, indexing, searching, and returning relevant images or audio to agent context.

How it connects

Connects sensory memory with multimodal retrieval, vector databases, embeddings, text retrieval, and multimodal RAG.

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Related concepts

• memorypart of · Memory is a storage resource or mechanism that retains data or computational state for access during computation or later use.
• cachinghas part · Caching is the practice of storing reusable data or computation results so later access can avoid repeating retrieval or computation.
• retrieval-augmented generationuses · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.

This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action, Second Edition. The section links point to the parts of the book it draws on.