One of the most potent aspects of Agents built using Skynet is the memory feature. Unlike traditional software or chatbots, AI agents on Skynet are equipped with memory that helps with the performance and execution of requests based on the workflow they are built on.
Unlike the traditional way memory works for an AI agent, on Skynet, memory works differently. However, before we delve into that, let us look at what an AI agent is on Skynet.
On Skynet, AI agents are deployed as NFTs (Non-fungible tokens), which allows them to be ownable and also transferable. AI agents can also be deployed as a collection, which is essentially a group of distinct agents deployed simultaneously, sharing a common purpose or intent.
Furthermore, AI agents on Skynet are personalized software or programs that autonomously perform tasks on behalf of a user based on a designed workflow, and these agents are ownable and transferable.
Memory is essential for AI agents because it enhances their capability and transforms them from simple tools to being adaptive and context-aware. We go through some of the reasons why memory is essential for AI agents:
- Context Awareness: Without memory, AI agents will treat each interaction as new, forgetting any personal preference, knowledge, goal or past tasks.
- Task Continuity: Many tasks require multiple steps across time (e.g., research, writing a report, coding projects). And in any case, with the help of memory, AI agents can pick up from where they left off.
- Personalization: An AI agent, with the help of memory, can adapt your style and preferred way of carrying out tasks, making it a personal tool suited for you.
In summary, Memory is what makes AI agents truly useful, personal, and reliable over time. Without it, they're just powerful programs. With it, they become assistants who help, learn, adapt, and grow with you.
Agent memory are of three types on Skynet, they are:
- Collection memory
- Agentic memory
- Block memory
These are the memory types that agents on Skynet use, and we go over them in detail below:
- Collection Memory: is a shared memory system designed for groups of agents deployed together as a collection. Instead of each agent operating with its own isolated memory, collection memory provides a single, unified source of knowledge that all agents in the group can access. This creates a collaborative layer where agents can draw from the same context, instructions, and stored information, ensuring consistency across the entire collection. By using collection memory, distinct agents within the same deployment can coordinate more effectively, since they are aligned through one centralized memory source. For instance, if four agents are deployed as a collection, they will all be able to read from and contribute to the same memory.
- Agentic Memory: is a memory system that enables an agent to retain and apply knowledge from its own past actions and interactions. Instead of functioning only on immediate instructions, agentic memory allows the agent to build on previous experiences, decisions, and outcomes. This creates a more adaptive and context-aware system where the agent can continuously improve and refine how it operates. With agentic memory in place, an agent can act with greater personalization and efficiency on behalf of the user. By recalling what has worked before, learning from mistakes, and recognizing patterns over time, the agent can make more informed choices and deliver results that align more closely with the user’s needs. In this way, agentic memory serves as the foundation for agents to act intelligently and proactively, rather than reactively.
- Block Memory: functions like a dedicated knowledge bank where information can be stored and organized for future use. Instead of relying only on what an agent has been pre-trained on, block memory gives users the ability to directly add knowledge, facts, or instructions into a structured memory system. This creates a centralized resource that acts like a personal library or database, designed specifically for agents to access whenever they need context or guidance. With block memory in place, agents are not limited to generic reasoning but can tap into the user’s curated knowledge to carry out more precise and relevant actions. Whether it’s executing tasks, making informed decisions, or personalizing outputs, the agent can draw from this memory bank to act on the user’s behalf. In this way, block memory empowers agents to work more effectively, bridging the gap between stored knowledge and real-time action.
AI agents on Skynet make use of memory in four key ways: collection memory, agentic memory, block memory, and agent-to-agent memory usage. Each type of memory usage gives agents unique capabilities that enhance how they store knowledge, retrieve it, and apply it to real-world tasks.
- First, agents use memory for recall, which allows them to retrieve specific pieces of information whenever they are needed.
- Second, they rely on memory for context, providing additional background knowledge that helps them complete tasks with greater accuracy and depth.
- Third, agents leverage block memory, collection memory, and agentic memory as structured sources of knowledge that strengthen their decision-making and adaptability.
- Finally, the most advanced form is agent-to-agent memory communication, where one agent can directly request and utilize the memory of another. For example, if Agent A needs information stored in Agent B’s memory, it can access and use that knowledge seamlessly. This creates a collaborative system where agents share intelligence, coordinate effectively, and operate with a collective understanding.
This ability to share, recall, and contextualize knowledge means AI agents on Skynet are not just isolated tools, but interconnected systems capable of collective intelligence. By combining collection memory, agentic memory, block memory, and agent-to-agent communication, agents can act with consistency, adapt over time, and even collaborate.
Together, these memory systems form the backbone of how agents evolve beyond simple automation into intelligent, cooperative partners that can carry out tasks with precision, foresight, and adaptability.