AI Agent

AI Agents are intelligent, autonomous systems powered by large language models (LLMs) designed to perform self-directed tasks to achieve specific goals you define. Unlike traditional rule-based automations, an AI Agent can dynamically leverage a combination of tools to navigate complex workflows and even collaborate with other agents.

AI Agents offer a powerful way to automate complex business activities that are difficult or impossible to address with traditional, rule-based automation. They bring adaptability and intelligence to your processes, leading to increased efficiency and improved outcomes.

AI Agents are ideal for scenarios requiring dynamic, goal-oriented automation that goes beyond simple rule-based processes. Consider using AI Agents when your enterprise needs to:

  • Automate complex, multi-step workflows that involve various systems and decision points.
  • Enhance customer service operations by handling diverse inquiries, looking up information, and escalating complex cases.
  • Implement solutions that require adaptability and reasoning, such as data analysis, content generation, or process optimization where the exact steps might not be known upfront.
  • Require human oversight and intervention at critical points while still benefiting from automation's efficiency.

Key benefits

Goal-oriented autonomy
AI Agents autonomously work towards user-defined objectives, freeing up human resources from repetitive or multi-step tasks.
Adaptable execution
Agents access a combination of tools, and sub-agents to dynamically respond to their environment. This means they can adjust their approach based on the specific situation, leading to more effective results.
Interactive engagement
AI Agents can engage in multi-step tasks, ask for clarification when needed, escalate to human intervention for complex issues, and validate outputs to ensure accuracy.

Components

An AI Agent operates by interpreting user-defined goals and then autonomously determining the best sequence of actions to achieve them. They leverage an underlying LLM for reasoning and can access a diverse set of tools and data.

AI Agent

For professional developers, our framework provides:

  • Tool management: Select, configure, and override tool behaviors for precise control.
  • Advanced prompt management: Structured and editable prompts ensure clear task alignment.
  • Variable management: Flexible input/output variables with typing and custom options.
  • Reflection and thinking: Built-in capabilities for enhanced agent reasoning.
  • Human-in-the-loop (HITL): Easy integration of human oversight for quality control.
  • Long-running processes: Agents maintain memory and state across extended durations.
  • Testing and debugging: Robust tools and detailed logs, ensure effective validation and troubleshooting.
  • Security and guardrails: Integrated safeguards prevent issues such as infinite loops and ensure compliance, along with comprehensive logging for auditing.

Prompt generation and optimization

The prompt is crucial for guiding the AI Agent behavior.

  • Generate prompt for your agent - popup: (cloud-only feature): This optional setting assists users in crafting effective prompts by providing a structured approach.
  • Structure: This fundamental structure helps the underlying language model (LM) understand and react correctly.
    • Role: Defines the persona and expertise of the agent (for example, "You are an expert customer service representative").
    • Goal: States the overall objective the agent needs to achieve (for example, "Resolve customer inquiries efficiently").
    • Action plan: Outlines the high-level steps or strategies the agent should follow to reach its goal.
  • Generate vs. Optimize:
    • Generate: Creates an entirely new prompt based on your initial input.
    • Optimize: Reorganizes an existing prompt's role, goal, and action plan to align with best practices, improving clarity and effectiveness for the LM. It takes into account the existing prompt's elements, tool details, and other aspects of your AI Agent configuration.

Tool integration

AI Agents derive their power from their ability to interact with external systems and perform actions.

  • Tools for specific tasks: AI Agents use tools to perform specific tasks, and the platform is designed to treat virtually every response as a potential tool interaction.
  • Available tools: Agents have access to a wide range of tools, including:
    • API Tasks: For interacting with external API-based automations.
    • Bots: To leverage existing bot functionalities.
    • Processes: To trigger and manage predefined workflows.
    • Other agents: Enabling multi-agent collaboration and complex workflows.
    • Forms: For data collection or human interaction points.
  • Tool naming convention: The name of a tool directly corresponds to the name of its associated file in the repository.
  • Variable support: If an automation used as a tool has unsupported variable types, the automation can still be used, but inputs for these variables will not be possible, and outputs for them will not be received.