Agents

At kis.ai, agents are designed to be autonomous, long-living code that work persistently towards predefined goals. Unlike traditional request-response mechanisms, agents have agency and continue to operate beyond a single request, making them a crucial component for building complex, intelligent and dynamic systems.

Goals and Persistence

Agents are assigned with one or more goals, which they continuously work towards achieving. These goals drive the agent’s actions and decisions. An agent’s life cycle revolves around these objectives, adjusting strategies as needed to progress towards them. The persistence of agents allows them to maintain focus and adapt over time, ensuring they remain effective and aligned with their intended purposes.

Memory and Learning

A key feature of agents is their memory. Agents keep track of what they have tried before, what they are currently doing, and how close they are to achieving their goals. This historical context allows them to avoid repeating ineffective actions and to refine their strategies. By learning from past experiences, agents can optimize their performance and become more efficient over time.

External Triggers and Responsiveness

Agents are responsive to external triggers. These triggers can come from various sources, such as API calls, changes in the state of a monitored system, or new data availability. For instance, an agent might be triggered by an update to a website, a change in application status, or the arrival of new data. When triggered, agents can perform a range of actions, such as updating an application, sending an email, or summarizing text. They can also use large language models (LLMs) to generate new intermediate goals or actions, enabling them to adapt to new information and circumstances dynamically.

Autonomy and Proactivity

One of the defining characteristics of agents is their autonomy. They are not solely reactive to external requests but can also act independently based on their internal state and goals. This autonomy allows agents to initiate actions even in the absence of external triggers, contributing to their proactive nature. For example, an agent might autonomously decide to check the status of a monitored system at regular intervals and take preemptive actions based on its findings.

Representation and Delegation

Agents often work on behalf of users, possessing some of the user’s privileges to carry out their tasks. This delegation ensures that agents can perform actions that the user would otherwise need to handle manually. Moreover, agents are capable of notifying users about significant events or changes in their progress, keeping users informed and engaged with the agent’s activities.

Integration with Multi-Agent Systems

kis.ai agents are designed to be collaborative, where multiple agents collaborate to achieve complex objectives. Each agent operates within the narrow confines of its goals, triggers, and actions. However, with multiple agents working simultaneously towards their goals, it can lead to emergent behavior. Emergent behaviors are complex outcomes that are not explicitly programmed but arise from the interplay of individual agents. This phenomenon is akin to natural systems like ant colonies or bee hives, where the collective behavior of individual agents results in sophisticated structures and processes.

Examples of Multi-Agent Systems

In nature, swarms of ants and bees exhibit emergent behaviors by working towards their individual goals, which contribute to building complex structures like ant hills and bee hives. Similarly, in a software environment, multiple kis.ai agents can collaborate to manage a large-scale system, handle intricate workflows, or automate extensive business processes. For instance, in a customer service scenario, one agent might handle customer inquiries, another could manage follow-up actions, and a third might analyze customer feedback to improve service quality. The combined efforts of these agents lead to a cohesive, efficient system that enhances overall performance and user satisfaction.

In summary, agents in kis.ai are powerful, autonomous entities designed to work persistently towards their goals. With features like memory, responsiveness to external triggers, autonomy, and integration capabilities, these agents form the backbone of intelligent, multi-agent systems. By leveraging the unique strengths of each agent, kis.ai enables the creation of dynamic, resilient, and efficient systems that can adapt and thrive in complex environments.