AI agents are like well-trained digital team members that help your business by answering questions and solving problems. They work by combining three main parts: a brain that understands questions (language models), a smart filing system that quickly finds relevant information (knowledge processing), and a systematic way of creating accurate answers (RAG pipeline). Imagine having an assistant who has instant access to all your business knowledge and can provide consistent, accurate responses around the clock – that's what AI agents do.
Core Components
Language Model Foundation
Large Language Models (LLMs) serve as the brain of AI agents, enabling them to:
Understand natural language queries
Generate accurate, context-aware responses
Adapt to different user tones and styles
Continuously improve with usage
Example: When a customer asks about product features, the LLM understands the query's intent and formulates a coherent response.
Knowledge & Data Processing System
Vector Embedding & Processing
Transforms information into a format AI can understand:Converts text into mathematical representations
Enables precise information matching
Captures meaning relationships between different pieces of content
Example: Converting company policies into vector format allows quick, accurate policy lookups.
Vector Database
Acts as the AI's organized memory system:Stores information efficiently for quick retrieval
Maintains connections between related information
Enables fast, accurate information search
Updates dynamically with new information
Example: Storing customer interaction history for quick access during support conversations.
Structured Data Retrieval
Supports direct querying of tabular data from tools like Excel, databases, CRMs, etc.
Enables agents to generate summaries, compare values, or filter data based on context
Example: A sales manager asks for the top-performing products in the last quarter—the AI Agent queries the connected sales database and delivers the summary.
RAG Pipeline (Retrieval-Augmented Generation)
Retrieval Phase
Efficiently finds relevant information:Converts questions into searchable format
Searches through knowledge base
Identifies most relevant information
Ranks results by relevance
Example: When asked about leave policy, quickly finding specific relevant policy sections.
Augmentation Phase
Enriches queries with context:Combines user question with retrieved information
Adds relevant background context
Ensures comprehensive understanding
Prepares complete context for response
Example: Adding current policy updates to historical policy information for accurate responses.
Generation Phase
Creates accurate, helpful responses:Formulates clear, contextual answers
Ensures response accuracy
Applies appropriate formatting
Maintains consistent tone and style
Example: Generating a complete policy explanation with relevant examples and current guidelines.
OOTB Tool-Calling Capabilities
Unify AI Agents go beyond passive responses—they can act. Using built-in connectors and in-house APIs, agents can:Trigger workflows in both UnifyApps and external systems
Integration with enterprise apps via OOTB connectors
Support for custom connectors to your home grown applications
Example: You can enable agents to take action on your systems securely using your APIs without the need for MCP servers.
Default Productivity Tools
With prebuilt AI skills, Unify Agents can handle:Web Search & Deep Research
Chart & Visualization Generation
Document Understanding & Summarization
Example: An agent can look up recent market trends via web search, generate a bar chart of competitor performance, and email it to your team.
These capabilities empower agents to support everything from customer support to business analytics—autonomously.
Unify AI Agents combine the power of LLMs, advanced data processing, and action-based integrations to create a flexible, intelligent, and truly helpful digital workforce. Whether answering questions, retrieving structured data, or acting on your behalf, they’re designed to grow with your business.
