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Documentation Index

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Kinesis is SWAI’s execution layer — enabling agents to interact with external systems and trigger real-world outcomes. Like acquiring new skills, Kinesis allows agents to expand their capabilities without rewriting core logic. These skills allow agents to connect with external platforms, interact with web services, and communicate with users.
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Connection to Cortex

Kinesis works in tandem with Cortex, the decision-making brain of SWAI agents. When Cortex determines an action should be taken, Kinesis executes that decision through its skill system. Kinesis and Cortex operate in a loop: Cortex makes decisions, Kinesis executes them — and then sends performance feedback back for ongoing adaptation.
1

Capability Awareness

Kinesis informs Cortex about available skills, ensuring the agent only attempts actions it can actually perform
2

Action Execution

When Cortex decides on a course of action, Kinesis translates that decision into concrete steps
3

Feedback Loop

After executing actions, Kinesis reports results back to Cortex, allowing for learning and adaptation
This skill-based architecture enables agents to develop specialized capabilities while maintaining a coherent decision-making process. As new skills are added to an agent’s Kinesis module, its action possibilities expand automatically without requiring changes to the core reasoning system.

AI Models Integration

Can integrate with domain-specific models (e.g. audio, vision, LLMs) to expand action types and precision

Agentic Frameworks

Connecting with Eliza and other agent ecosystems

Direct Actions

Executing external operations like API calls, web interactions, and on-chain actions

Internal Actions

Managing memory operations, state transitions, and self-monitoring processes

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Creating Agent Swarms

One of the most powerful aspects of Kinesis is its ability to integrate with other specialized agents and AI models. This capability enables the creation of agent swarms - collaborative networks where multiple agents with different skill sets and configurations work together seamlessly. In these swarms, each agent contributes its unique capabilities and expertise, handling specific tasks according to its configuration. This distributed approach to problem-solving creates a collective intelligence that is significantly more effective and versatile than any individual agent could be on its own.
Agent swarms enable problem-solving at scale — where each agent contributes specialised actions to move the system forward