Ontology-driven distributed agent memory mesh

1 . A non-transitory computer-readable storage medium comprising instructions for maintaining agent memory of artificial intelligence (AI) agents stored thereon, wherein the instructions when executed by at least one data processor of a system, cause the system to:

receive, from a requestor autonomous AI agent that operates on a computing device, a memory storage request to store operational data of the requestor autonomous AI agent,

wherein the operational data comprises a token set that defines one or more operational state variables for one or more tasks historically executed by the requestor autonomous AI agent;

classify the operational data using a hierarchical ontological data structure that organizes a predefined taxonomic node set associated with the requestor autonomous AI agent into multiple tiers by:

mapping the operational data to a subset of the predefined taxonomic node set using a distance between (a) a vector representation of at least a portion of the operational data with (b) a vector representation of each taxonomic node of the predefined taxonomic node set, and

generating an ontological reference pointer for the operational data that defines a position of the taxonomic node subset within the hierarchal ontological data structure,

wherein the ontological reference pointer encodes a hierarchical path from a root node of the hierarchical ontological data structure to the taxonomic node subset, and

wherein the position of the taxonomic node subset is represented using one or more directed graph connections within the hierarchical ontological data structure that links the taxonomic node subset to one or more of: (a) a higher-level taxonomic node positioned in a higher tier than the taxonomic node subset within the hierarchical ontological data structure or (b) a lower-level taxonomic node positioned in a lower tier than the taxonomic node subset within the hierarchical ontological data structure;

construct a memory capsule data structure that compresses the operational data by substituting one or more tokens of the operational data with a representation of the ontological reference pointer;

generate a unique blockchain transaction identifier for the memory capsule data structure by applying a cryptographic hash operation to a concatenated representation of the memory capsule data structure; and

store the memory capsule data structure in a distributed network accessible to multiple autonomous AI agents that include the requestor autonomous AI agent,

wherein each autonomous AI agent is represented by a corresponding computing device that participates in the distributed network, and

wherein the memory capsule data structure is stored by broadcasting the unique blockchain transaction identifier to each computing device.

2 . The non-transitory computer-readable storage medium of claim 1 , wherein the memory capsule data structure is a first memory capsule data structure, and wherein the instructions further cause the system to:

receive, from a retrieval autonomous AI agent, a memory retrieval request to retrieve a particular memory capsule data structure that satisfies a similarity threshold value with an ontological parameter set;

identify one or more memory capsule data structures stored in the distributed network that match the ontological parameter set by comparing the similarity threshold value against a distance between a vector representation of the ontological parameter set and a vector representation of the stored one or more memory capsule data structures; and

transmit, to the retrieval autonomous AI agent, a representation of the one or more memory capsule data structures.

3 . The non-transitory computer-readable storage medium of claim 1 ,

wherein the hierarchical ontological data structure is represented using Web Ontology Language (OWL), and

wherein the ontological reference pointer defines the position of the taxonomic node subset within an instance layer of the hierarchal ontological data structure.

4 . The non-transitory computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

encrypt the operational data within the memory capsule data structure prior to storing the memory capsule data structure in the distributed network;

determine an encryption key associated with the requestor autonomous AI agent;

store the encryption key in a distributed database; and

using a retrieval autonomous AI agent, reconstruct the encryption key and decrypt the operational data.

5 . The non-transitory computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

receive, from a retrieval autonomous AI agent, a memory retrieval request to retrieve a particular memory capsule data structure that satisfies a similarity threshold value with an ontological parameter set;

generate a memory summary representation for each memory capsule data structure stored in the distributed network;

compare each memory summary representation against the ontological parameter set to generate a score for the memory summary representation; and

transmit, to the retrieval autonomous AI agent, one of more memory capsule data structures associated with a score that exceeds the similarity threshold value.

6 . The non-transitory computer-readable storage medium of claim 1 , wherein the computing device is a first computing device, and wherein the instructions further cause the system to:

identify a priority classification for the memory capsule data structure based on the taxonomic node subset; and

in response to the priority classification satisfying a particular value, transmit the memory capsule data structure to a second computing device that is configured to broadcast the memory capsule data structure to multiple computing devices communicatively connected to the second computing device.

7 . The non-transitory computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

generate a zero-knowledge cryptographic proof for the memory capsule data structure;

receive, from a verifying autonomous AI agent of the multiple autonomous AI agents, a validation request that includes verification parameter set; and

transmit, to the verifying autonomous AI agent, the zero-knowledge cryptographic proof, wherein the zero-knowledge cryptographic proof is configured to validate the verification parameter set against the memory capsule data structure.

8 . A computer-implemented method for maintaining agent memory of artificial intelligence (AI) agents, the computer-implemented method comprising:

obtain, from a requestor autonomous AI agent, a storage request to store operational data of the requestor autonomous AI agent,

wherein the operational data comprises a token set that defines one or more operational state variables for one or more tasks executed by the requestor autonomous AI agent;

classify the operational data using a hierarchical ontological data structure that organizes a predefined taxonomic node set associated with the requestor autonomous AI agent into multiple tiers by:

mapping the operational data to a subset of the predefined taxonomic node set using a distance between (a) a vector representation of at least a portion of the operational data with (b) a vector representation of each taxonomic node of the predefined taxonomic node set, and

determining an ontological reference pointer for the operational data that defines a position of the taxonomic node subset within the hierarchal ontological data structure,

wherein the ontological reference pointer encodes a hierarchical path from a root node of the hierarchical ontological data structure to the taxonomic node subset;

construct a memory capsule data structure that compresses the operational data by substituting one or more tokens of the operational data with a representation of the ontological reference pointer;

generate a unique blockchain transaction identifier for the memory capsule data structure by applying a cryptographic hash operation to a concatenated representation of the memory capsule data structure; and

store the memory capsule data structure in a distributed network accessible to multiple autonomous AI agents that include the requestor autonomous AI agent by broadcasting the unique blockchain transaction identifier to the multiple autonomous AI agents.

9 . The computer-implemented method of claim 8 , wherein the method further comprises:

assign a temporal weight value to the memory capsule data structure based on a time elapsed since creation of the operational data;

apply an exponential decay function to the temporal weight value to generate a temporal score for the memory capsule data structure; and

modify a priority score of the memory capsule data structure based on the temporal score, wherein the priority score is used to retrieve the memory capsule data structure.

10 . The computer-implemented method of claim 8 , wherein the memory capsule data structure comprises:

a header data block that includes one or more of unique capsule identifier or protocol version number,

a metadata data block that includes one or more of the ontological reference pointer or temporal metadata associated with the operational data, and

a compressed content data block that includes the operational data compressed using the representation of the ontological reference pointer.

11 . The computer-implemented method of claim 8 , further comprising:

maintaining a least-recently-used cache of memory capsule data structures accessed by a retrieval autonomous AI agent within the multiple autonomous AI agents.

12 . The computer-implemented method of claim 11 , wherein the method further comprises:

using the least-recently-used cache, determining an access pattern set of the requestor autonomous AI agent that identifies one or more taxonomic node subsets accessed beyond a threshold value; and

retrieving one or more memory capsule data structures associated with the one or more taxonomic node subsets.

13 . The computer-implemented method of claim 8 , wherein storing the memory capsule data structure comprises:

determining an access pattern set of the requestor autonomous AI agent that identifies one or more taxonomic node subsets accessed beyond a threshold value; and

replicating one or more memory capsule data structures associated with the one or more taxonomic node subsets across multiple computing devices in the distributed network.

14 . The computer-implemented method of claim 8 , wherein constructing the memory capsule data structure comprises:

generating a compressed memory summary using the ontological reference pointer as a compression dictionary; and

storing the compressed memory summary in the memory capsule data structure.

15 . A system comprising:

at least one hardware processor; and

at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:

obtain, from a requestor autonomous AI agent, a storage request to store operational data of the requestor autonomous AI agent;

classify the operational data using an ontological data structure that organizes a taxonomic node set associated with the requestor autonomous AI agent by:

mapping the operational data to a subset of the taxonomic node set by comparing a representation of at least a portion of the operational data with a representation of one or more taxonomic nodes of the taxonomic node set, and

determining an ontological reference pointer for the operational data that defines a position of the taxonomic node subset within the ontological data structure,

wherein the ontological reference pointer encodes a hierarchical path from a root node of the hierarchical ontological data structure to the taxonomic node subset;

construct a memory capsule data structure that compresses the operational data using the ontological reference pointer; and

store the memory capsule data structure in a distributed network accessible to multiple autonomous AI agents that include the requestor autonomous AI agent by broadcasting a representation of the memory capsule data structure to the multiple autonomous AI agents.

16 . The system of claim 15 , wherein the instructions further cause the system to:

receive multiple memory retrieval requests for different memory capsule data structures from a retrieval autonomous AI agent within the multiple autonomous AI agents;

batch the multiple memory retrieval requests into a single network transmission that includes representations of the different memory capsule data structures; and

transmit the single network transmission to the retrieval autonomous AI agent.

17 . The system of claim 15 , wherein the instructions further cause the system to:

receive multiple memory retrieval requests for different memory capsule data structures from a retrieval autonomous AI agent within the multiple autonomous AI agents;

group the different memory capsule data structures into multiple batch transmissions; and

synchronize the multiple batch transmissions across the multiple autonomous AI agents in multiple phases.

18 . The system of claim 15 , wherein the instructions further cause the system to:

store metadata references that identify the memory capsule data structure in a blockchain transaction; and

store the memory capsule data structure in an off-chain distributed storage.

19 . The system of claim 15 , wherein the instructions further cause the system to:

partition the distributed network into multiple blockchain shards based on the ontological data structure; and

distribute multiple memory capsule data structures across the multiple blockchain shards in accordance with a respective ontological reference pointer of each memory capsule data structure.

20 . The system of claim 15 , wherein the instructions further cause the system to:

obtain a network topology state between multiple computing devices in the distributed network; and

select a routing path for transmission of a particular memory capsule data structure between the multiple computing devices based on the network topology state.