An AI Agent is a software entity, designed to perform cognitive tasks that involve both AI Skills and contextual actions. It combines the capabilities of AI models with the ability to interact with enterprise systems and data sources (RAG) to automate and accelerate business processes.

Key Benefits:

  • End-to-End Process Orchestration: Seamlessly integrate AI Agents into your existing business processes, spanning multiple systems and applications.
  • Rapid AI Agent Development: Build and deploy AI Agents quickly and easily, leveraging our intuitive development tools and pre-built AI skills.
  • Robust Compliance and Control: Ensure the security, privacy, and ethical use of AI Agents with our advanced governance and compliance features.
  • Scalability and Flexibility: Adapt to changing business needs and scale your AI solutions to meet growing demands.

Unlike personal productivity AI Agents, Automation Anywhere's AI Agents are focused on transforming entire departmental and enterprise processes, driving significant improvements in KPIs, customer experience, and top-line growth. These agents handle cognitive tasks such as responding to customer inquiries or deciding on the best replacement product for a stock outage. They can also collaborate with humans and other AI Agents to accelerate business processes. They are capable of automating critical processes such as customer service, anti-money laundering, healthcare, and finance, unlocking new levels of efficiency and innovation. The below diagram shows different key components of AI Agents.


AI Agent

Key Components of an AI Agent

  1. AI Skill: The AI Skill is the core intelligence of the agent, responsible for processing information and making decisions. It can be powered by various AI models like Large Language Models (LLMs), Retrieval-Augmentation Generation (RAG), and other machine learning techniques. LLMs can be used to enhance the AI Agent's ability to understand and respond to natural language queries and instructions, whereas RAG allows you to access and use your organization's specific knowledge base and data sources.
  2. Actions: The actions enable the AI Agent to interact with the real world. This could involve executing tasks within enterprise systems, triggering workflows, or generating human-readable outputs.
  3. UI & API Automation: This component allows the AI Agent to interact with various enterprise systems through user interfaces and APIs. It utilizes RPA bots and API Tasks to automate actions across different systems.
  4. Human-in-the-Loop: This component allows for human intervention in critical decision-making or validation processes. It provides guardrails and ensures that the AI Agent operates within ethical and legal boundaries.
  5. Orchestration: The orchestration component enables the AI Agent to manage complex workflows, schedule tasks, and make decisions based on events and triggers.
  6. Enterprise Data: This refers to the company's data sources - Retrieval-Augmentation Generation (RAG) that are used to train and improve the AI agent's performance.
  7. Tuned Prompt: This is a carefully crafted prompt that guides the AI skill to generate the desired output.
  8. Security & Governance: This layer ensures the agent operates within ethical and security guidelines. It includes features like validation, monitoring, traceability, and data masking.

Key Features:

  • Orchestration: Break down complex tasks into sequential steps that includes AI and actions for efficient execution.
  • Natural Language Interaction: Interact with the agent using natural language prompts.
  • LLM Power: Leverage the capabilities of large language models for advanced understanding and response generation.
  • RAG Integration: Access and use your organization's specific knowledge base and data sources (Retrieval Augmented Generation).
  • Autonomous Execution: Execute tasks independently, reducing manual intervention.
  • AI Governance: Ensures the ethical, secure, and compliant use of AI Agents, including data privacy, bias mitigation, and transparency.