A conceptual introduction to AI agents — how the perceive–reason–act loop works, when to use agents over simpler automation, and what makes Cotonity agents unique.
Defining an AI agent
An AI agent is a software entity that perceives its environment, reasons about a goal, and takes actions to achieve it — often without requiring step-by-step human instruction. Unlike a simple script that executes a fixed sequence of commands, an agent can adapt its behavior based on the data it receives, the tools available to it, and the outcome of previous steps. On the Cotonity platform, an agent combines a large language model (LLM) for reasoning with a configurable set of actions, memory stores, and trigger conditions that define when and how it runs.
The perceive–reason–act loop
Every agent on Cotonity follows a core loop: it first perceives inputs (a webhook payload, a scheduled event, a user message, or data retrieved from a tool), then reasons about what to do next using its LLM and any instructions you have provided, and finally acts by calling one or more configured actions — writing to a database, calling an API, sending a notification, or invoking another agent. This loop repeats until the agent reaches a terminal step or exhausts its allowed iterations. Understanding this loop is the foundation for designing reliable, predictable agents.
How agents differ from traditional automation
Traditional automation tools execute deterministic workflows: if X, do Y. An AI agent adds a layer of flexible reasoning that can interpret ambiguous inputs, select among multiple possible next steps, and recover from unexpected situations by retrying or choosing an alternative path. This makes agents particularly powerful for tasks that involve natural language, variable data shapes, or decision points that are hard to enumerate in advance. The trade-off is that agent behavior can be less predictable, which is why Cotonity provides sandbox testing, observability dashboards, and rollback capabilities.
When to use an agent vs. a simpler tool
Not every automation task needs an AI agent. If your workflow has a fixed, well-defined sequence with no natural-language interpretation, a standard workflow tool or script is usually faster, cheaper, and easier to debug. Use an agent when the task requires understanding unstructured text, making judgment calls among several options, dynamically selecting which tools to call, or adapting to data that varies significantly between runs. Cotonity lets you mix both approaches: you can embed agent steps inside broader workflows or trigger lightweight scripts from within an agent.