> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swai.app/llms.txt
> Use this file to discover all available pages before exploring further.
# SWAI Kinesis
> The action layer for AI agents
**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.
## Connection to Cortex
**Kinesis** works in tandem with [Cortex](/cortex/overview), 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.
**Kinesis** informs Cortex about available skills, ensuring the agent only attempts actions it can actually perform
When Cortex decides on a course of action, **Kinesis** translates that decision into concrete steps
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.
Can integrate with domain-specific models (e.g. audio, vision, LLMs) to expand action types and precision
Connecting with Eliza and other agent ecosystems
Executing external operations like API calls, web interactions, and on-chain actions
Managing memory operations, state transitions, and self-monitoring processes
## 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
