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AI Agents - 101
RAG 101
AI Agents - 101
What are AI Agents?
What are AI Agents?
What are AI Agents?
The Complete Guide
The Complete Guide
The Complete Guide
The new wave of AI autonomy, where AI agents think, learn, and act to solve critical business challenges - on it’s own.
The new wave of AI autonomy, where AI agents think, learn, and act to solve critical business challenges - on it’s own.
The new wave of AI autonomy, where AI agents think, learn, and act to solve critical business challenges - on it’s own.
Launch AI Agents through our enterprise-ready infrastructure or your private cloud environment.
Launch AI Agents through our enterprise-ready infrastructure or your private cloud environment.
Build agentic workflows that integrate with your data, tech stack and systems.
Build agentic workflows that integrate with your data, tech stack and systems.
Better results – Everytime! with autonomous, contextual, and RAG-powered AI Agents.
Better results – Everytime! with autonomous, contextual, and RAG-powered AI Agents.
Introduction
Introduction
Introduction
AI agents, with their autonomous problem-solving capabilities, can now take action to resolve historically complex use cases in real time without human intervention.
AI agents, with their autonomous problem-solving capabilities, can now take action to resolve historically complex use cases in real time without human intervention.
AI agents, with their autonomous problem-solving capabilities, can now take action to resolve historically complex use cases in real time without human intervention.
Imagine a chatbot coworker - connected to your company data and your everyday applications - capable of not just providing information but "getting work done” - that possibility is here and growing with AI agents.
Imagine a chatbot coworker - connected to your company data and your everyday applications - capable of not just providing information but "getting work done” - that possibility is here and growing with AI agents.
Cited as the Next Frontier of Generative AI by McKinsey. AI Agents are tipping the scales, automating step-by-step and complex tasks that are crucial for business operations, yet considered low-value transactions for human agents when considering the time, availability, and cost factors.
We can use Retrieval Augmented Generation (RAG) to address the limitations of large language models – incorporating real-time, reliable information grounded in the latest internal data.
Combining the power of Generative AI with Large Language Models (LLM), AI agents rely on machine learning and natural language processing (NLP) to perceive their environment, reason, and act accordingly to solve user/customer queries.
We can use Retrieval Augmented Generation (RAG) to address the limitations of large language models – incorporating real-time, reliable information grounded in the latest internal data.
RAG integrates your organization’s vast knowledge base—documents, databases, or any other relevant data source—with the LLM, enabling your AI applications to scour through specific data sets outside of its training domain.
To put it into better perspective. When an AI agent receives a task, it:
- Breaks down complex goals into actionable steps
- Gathers information from multiple systems and tools
- Makes decisions based on real-time context
- Takes coordinated action to achieve objectives
- Learns and improves from every interaction
To put it into better perspective. When an AI agent receives a task, it:
- Breaks down complex goals into actionable steps
- Gathers information from multiple systems and tools
- Makes decisions based on real-time context
- Takes coordinated action to achieve objectives
- Learns and improves from every interaction
To put it into better perspective. When an AI agent receives a task, it:
- Breaks down complex goals into actionable steps
- Gathers information from multiple systems and tools
- Makes decisions based on real-time context
- Takes coordinated action to achieve objectives
- Learns and improves from every interaction
AI agents though nascent, are quickly maturing – integrating into the physical (think self-driving cars) and digital fabric of how we work and operate. This guide will get you up-to-speed with what AI agents are, how they work, and factors to consider when building your very own AI Agent.
AI agents though nascent, are quickly maturing – integrating into the physical (think self-driving cars) and digital fabric of how we work and operate. This guide will get you up-to-speed with what AI agents are, how they work, and factors to consider when building your very own AI Agent.
Discover What’s Possible
Your enterprise operations now intelligent, autonomous and agile with SearchAI Agents.
Discover What’s Possible
Your enterprise operations now intelligent, autonomous and agile with SearchAI Agents.
Discover What’s Possible
Your enterprise operations now intelligent, autonomous and agile with SearchAI Agents.
What are AI Agents?
What is RAG?
What is RAG?
AI agents empower your enterprise with autonomous digital workers capable of understanding context and taking intelligent action to achieve business goals.
AI agents empower your enterprise with autonomous digital workers capable of understanding context and taking intelligent action to achieve business goals.
AI agents empower your enterprise with autonomous digital workers capable of understanding context and taking intelligent action to achieve business goals.
AI agents (artificial intelligence agents) are at their core reasoning engines that understand context, plan workflows, connect to external tools and data, and execute actions to achieve defined goals.
AI agents (artificial intelligence agents) are at their core reasoning engines that understand context, plan workflows, connect to external tools and data, and execute actions to achieve defined goals.
The old-fashioned ruled-based agents – designed to follow predictable, user-defined workflows, often breaks down when prompted for outcomes outside it’s explicit rules.its
The old-fashioned ruled-based agents – designed to follow predictable, user-defined workflows, often breaks down when prompted for outcomes outside it’s explicit rules.its
The GenAI Agent, built on Large Language Models, puts into use an intricate set of AI tools, from natural language processing, machine learning, and Retrieval Augmented Generation (RAG). Working together to pull accurate information from data even outside its training sources, adapt/reason in context when workflows get unpredictable, work with other tools and platforms to broaden its digital knowledge ecosystem, and improve performance from a continuous loop of learning.
The GenAI Agent, built on Large Language Models, puts into use an intricate set of AI tools, from natural language processing, machine learning, and Retrieval Augmented Generation (RAG). Working together to pull accurate information from data even outside its training sources, adapt/reason in context when workflows get unpredictable, work with other tools and platforms to broaden its digital knowledge ecosystem, and improve performance from a continuous loop of learning.
The GenAI Agent, built on Large Language Models, puts into use an intricate set of AI tools, from natural language processing, machine learning, and Retrieval Augmented Generation (RAG). Working together to pull accurate information from data even outside its training sources, adapt/reason in context when workflows get unpredictable, work with other tools and platforms to broaden its digital knowledge ecosystem, and improve performance from a continuous loop of learning.
How AI Agents Work
How AI Agents Work
AI agents operate as hyper-efficient coworkers, effectively planning, reasoning, and learning to handle complex tasks – without human intervention.
AI agents operate as hyper-efficient coworkers, effectively planning, reasoning, and learning to handle complex tasks – without human intervention.
AI agents operate as hyper-efficient coworkers, effectively planning, reasoning, and learning to handle complex tasks – without human intervention.
Goal initialization and planning
Goal initialization and planning
Goal initialization and planning
AI agents are built to operate autonomously, but they start with human-defined goals and boundaries.
AI agents are built to operate autonomously, but they start with human-defined goals and boundaries.
Their behaviour is shaped by three key groups: the development team that builds, trains, and connects agents with tools, the deployment team that implements agents to the appropriate environment, and end-user prompts that initiate the conversation flow to complete specific tasks.
Their behaviour is shaped by three key groups: the development team that builds, trains, and connects agents with tools, the deployment team that implements agents to the appropriate environment, and end-user prompts that initiate the conversation flow to complete specific tasks.
Their behaviour is shaped by three key groups: the development team that builds, trains, and connects agents with tools, the deployment team that implements agents to the appropriate environment, and end-user prompts that initiate the conversation flow to complete specific tasks.
Reasoning Using Available Tools
Reasoning Using Available Tools
Reasoning Using Available Tools
AI agents work intelligently by recognizing their knowledge gaps and actively seek information, pulling from various sources – even outside their training data.
AI agents work intelligently by recognizing their knowledge gaps and actively seek information, pulling from various sources – even outside their training data.
AI agents work intelligently by recognizing their knowledge gaps and actively seek information, pulling from various sources – even outside their training data.
They tap into external databases, web resources, APIs, and even other agents to gather necessary data. This allows them to continuously update their understanding and adjust their approach as they work towards their goal.
They tap into external databases, web resources, APIs, and even other agents to gather necessary data. This allows them to continuously update their understanding and adjust their approach as they work towards their goal.
They tap into external databases, web resources, APIs, and even other agents to gather necessary data. This allows them to continuously update their understanding and adjust their approach as they work towards their goal.
Action Execution
Action Execution
Action Execution
AI agents translate information into action, executing tasks based on their reasoning and gathered information.
AI agents translate information into action, executing tasks based on their reasoning and gathered information.
This includes: generating text, creating documents, sending emails, troubleshooting, and interacting with other software applications. They bridge the gap between internal analysis and external action, producing tangible results.
This includes: generating text, creating documents, sending emails, troubleshooting, and interacting with other software applications. They bridge the gap between internal analysis and external action, producing tangible results.
This includes: generating text, creating documents, sending emails, troubleshooting, and interacting with other software applications. They bridge the gap between internal analysis and external action, producing tangible results.
Learning and Reflection
Learning and Reflection
Learning and Reflection
AI agents grow smarter through continuous feedback loops. They learn from every interaction, storing successful approaches and user feedback to improve future performance.
AI agents grow smarter through continuous feedback loops. They learn from every interaction, storing successful approaches and user feedback to improve future performance.
AI agents grow smarter through continuous feedback loops. They learn from every interaction, storing successful approaches and user feedback to improve future performance.
This learning process is enhanced when multiple agents collaborate, sharing insights and reducing the need for human intervention while maintaining alignment with user goals and preferences.
This learning process is enhanced when multiple agents collaborate, sharing insights and reducing the need for human intervention while maintaining alignment with user goals and preferences.
This learning process is enhanced when multiple agents collaborate, sharing insights and reducing the need for human intervention while maintaining alignment with user goals and preferences.
Hyper-Efficient AI Agents
Hyper-Efficient AI Agents
Give SearchAI a try. Free for 30 days.
Give SearchAI a try. Free for 30 days.
Unify Enterprise Search, Chatbots and AI Agents with our Integrated RAG and Search Platform.
Types of AI agents
Types of AI agents
Types of AI agents
A precursor to the future of work, agentic workflows in all their forms, are set to rapidly transform user experiences across digital and physical frontiers.
A precursor to the future of work, agentic workflows in all their forms, are set to rapidly transform user experiences across digital and physical frontiers.
A precursor to the future of work, agentic workflows in all their forms, are set to rapidly transform user experiences across digital and physical frontiers.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding the fastest delivery routes, suggesting perfect products for customers, or finding the best ways to save energy. The agent picks the most valuable option looking at all factors, comparing benefits, and choosing what works best.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding the fastest delivery routes, suggesting perfect products for customers, or finding the best ways to save energy. The agent picks the most valuable option looking at all factors, comparing benefits, and choosing what works best.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding the fastest delivery routes, suggesting perfect products for customers, or finding the best ways to save energy. The agent picks the most valuable option looking at all factors, comparing benefits, and choosing what works best.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding the fastest delivery routes, suggesting perfect products for customers, or finding the best ways to save energy. The agent picks the most valuable option looking at all factors, comparing benefits, and choosing what works best.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding fastest delivery routes, suggesting perfect products for customers, finding best ways to save energy. The agent picks the most valuable option - looking at all factors, comparing benefits, and choosing what works best.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding fastest delivery routes, suggesting perfect products for customers, finding best ways to save energy. The agent picks the most valuable option - looking at all factors, comparing benefits, and choosing what works best.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding fastest delivery routes, suggesting perfect products for customers, finding best ways to save energy. The agent picks the most valuable option - looking at all factors, comparing benefits, and choosing what works best.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding fastest delivery routes, suggesting perfect products for customers, finding best ways to save energy. The agent picks the most valuable option - looking at all factors, comparing benefits, and choosing what works best.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding fastest delivery routes, suggesting perfect products for customers, finding best ways to save energy. The agent picks the most valuable option - looking at all factors, comparing benefits, and choosing what works best.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding fastest delivery routes, suggesting perfect products for customers, finding best ways to save energy. The agent picks the most valuable option - looking at all factors, comparing benefits, and choosing what works best.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding fastest delivery routes, suggesting perfect products for customers, finding best ways to save energy. The agent picks the most valuable option - looking at all factors, comparing benefits, and choosing what works best.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
Simple reflex agents
A simple reflex agent operates solely on current perceptual input, executing actions based on immediate environmental conditions without retaining context. These agents function through a predefined set of condition-action rules, where specific user prompts trigger predetermined responses - mapping them directly to output actions minus the decision-making complexity.
Utility-based agents
A utility-based agent works as an intelligent decision engine - carefully analyzing choices to find the best result. These systems tackle complex decisions - finding fastest delivery routes, suggesting perfect products for customers, finding best ways to save energy. The agent picks the most valuable option - looking at all factors, comparing benefits, and choosing what works best.
Goal-based agents
With goal-based agents, every action is driven by clear objectives and calculated pathways to success. These intelligent systems analyze their environment - selecting moves that progress toward set targets. By monitoring results and adjusting strategy, the agent makes precise decisions that achieve intended outcomes.
Model-based reflex agents
Model-based reflex agents use both their current perception and memory to maintain an internal model of its environment. Their continuous adaptation enables intelligent decision-making based on both current conditions and accumulated knowledge, delivering superior performance compared to basic reactive systems.
Learning agents
Learning agents hold the same capabilities as the other agent types but are unique in their ability to learn. New experiences are added to their initial knowledge base, which occurs autonomously. This learning enhances the agent's ability to operate in unfamiliar environments. Learning agents may be utility or goal-based in their reasoning and are comprised of four main elements: learning, critic, performance, and problem generator.
The Role of AI in building Artificial Intelligent Agents
The Role of AI in building Artificial Intelligent Agents
The Role of AI in building Artificial Intelligent Agents
Delivering action across changing business scenarios, the AI agent frameworks is built both adaptive and transformative – from the ground up.
Delivering action across changing business scenarios, the AI agent frameworks is built both adaptive and transformative – from the ground up.
Delivering action across changing business scenarios, the AI agent frameworks is built both adaptive and transformative – from the ground up.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Deep Learning & Neural Networks
Powers advanced pattern recognition, predictive capabilities, and complex data processing, enabling agents to handle sophisticated tasks and understand intricate relationships.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Deep Learning & Neural Networks
Powers advanced pattern recognition, predictive capabilities, and complex data processing, enabling agents to handle sophisticated tasks and understand intricate relationships.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Deep Learning & Neural Networks
Powers advanced pattern recognition, predictive capabilities, and complex data processing, enabling agents to handle sophisticated tasks and understand intricate relationships.
Retrieval Augmented Generation (RAG)
Enhances agent responses by pulling accurate information from knowledge bases and real- time data sources, grounding decisions on accurate, current information.
Machine Learning (ML)
Enables continuous learning from experience while processing human language, improving response accuracy and maintaining natural conversations through feedback loops.
Large Language Models (LLMs)
Forms the intelligence core of agents, enabling natural language understanding, contextual reasoning, and sophisticated decision-making for complex problem-solving.
Deep Learning & Neural Networks
Powers advanced pattern recognition, predictive capabilities, and complex data processing, enabling agents to handle sophisticated tasks and understand intricate relationships.
SearchBlox Enterprise Search
Powering Intelligent Operations with AI Agents
Powering Intelligent Operations with AI Agents
Powering Intelligent Operations with AI Agents
- Launch AI Agents on-premise or the cloud.
- Launch AI Agents on-premise or the cloud.
- Launch AI Agents on-premise or the cloud.
- Scalable data infrastructure
– Create Prebuilt or Custom Agent workflows
– Create Prebuilt or Custom Agent workflows
– Create Prebuilt or Custom Agent workflows
AI Agent vs AI Assistants
AI Agent vs AI Assistants
AI Agent vs AI Assistants
AI agents can be incorporated into AI Assistants - yet serve different operational needs.
AI agents can be incorporated into AI Assistants - yet serve different operational needs.
AI agents can be incorporated into AI Assistants - yet serve different operational needs.
Feature
Feature
Core Function
Autonomy
Decision-Making
Learning & Adaptation
Tool Integration
Workflow Execution
Examples
AI Agent
AI Agent
Focuses on autonomous execution of complex, multi-step tasks.
High; can operate independently with minimal human intervention.
Employs advanced reasoning and decision-making capabilities.
Continuously learns and adapts to new information and situations.
Seamlessly integrates with a wide range of tools and APIs to perform actions.
Can orchestrate and execute complex, multi-step workflows.
Automated customer service agents, complex process automation systems, intelligent research assistants.
AI Assistant
AI Assistant
Primarily provides information and completes simple tasks.
Limited; requires frequent human input and guidance.
Often relies on predefined rules or scripts.
May have some learning capabilities, but often limited.
May have limited integration with external tools and systems.
Typically handles single-step tasks or simple workflows.
Chatbots, virtual assistants, basic task automation tools.
Core Function
AI Agent
Focuses on autonomous execution of complex, multi-step tasks.
AI Assistant
Primarily provides information and completes simple tasks.
Autonomy
AI Agent
High; can operate independently with minimal human intervention.
AI Assistant
Limited; requires frequent human input and guidance.
Decision-Making
AI Agent
Employs advanced reasoning and decision-making capabilities.
AI Assistant
Often relies on predefined rules or scripts.
Learning & Adaptation
AI Agent
Continuously learns and adapts to new information and situations.
AI Assistant
May have some learning capabilities, but often limited.
Tool Integration
AI Agent
Seamlessly integrates with a wide range of tools and APIs to perform actions.
AI Assistant
May have limited integration with external tools and systems
Workflow Execution
AI Agent
Can orchestrate and execute complex, multi-step workflows.
AI Assistant
Typically handles single-step tasks or simple workflows.
Examples
AI Agent
Automated customer service agents, complex process automation systems, intelligent research assistants.
AI Assistant
Chatbots, virtual assistants, basic task automation tools.
SearchAI Agents
SearchAI Agents
SearchAI Agents
Build Task-Specific Agent Workflows – Fully Customized to your Enterprise Use Cases
Build Task-Specific Agent Workflows – Fully Customized to your Enterprise Use Cases
Build Task-Specific Agent Workflows – Fully Customized to your Enterprise Use Cases
Optimized for RAG
Optimized for RAG
Optimized for RAG
Deploy enterprise-grade RAG capabilities, delivering contextually precise responses with deep domain expertise.
Fixed Cost
Fixed Cost
Fixed Cost
Select Fully Managed or Self Managed plans with predictable costs optimized for enterprise scalability.
Flexible Deployment
Flexible Deployment
Flexible Deployment
Choose our pre-built agents or build a custom agent. Instantly integrate agents across diverse operational requirements.
Secure & Private
Secure & Private
Secure & Private
Keep your model, your inference requests and your data sets within the security domain of your organization.
AI Agents Use Cases
AI Agents Use Cases
AI Agents Use Cases
Automate typically complex processes. Employ intuitive, agile, and intelligent agents to resolve customer queries and caddy your workforce workload.
Automate typically complex processes. Employ intuitive, agile, and intelligent agents to resolve customer queries and caddy your workforce workload.
Automate typically complex processes. Employ intuitive, agile, and intelligent agents to resolve customer queries and caddy your workforce workload.
01
01
01
Customer Service Chatbots
Customer Service Chatbots
AI agents have revolutionized customer service by providing comprehensive, round-the-clock support across multiple channels. These agents serve as intelligent virtual assistants that can understand complex queries, analyze customer sentiment, and provide personalized responses.
AI agents have revolutionized customer service by providing comprehensive, round-the-clock support across multiple channels. These agents serve as intelligent virtual assistants that can understand complex queries, analyze customer sentiment, and provide personalized responses.
02
02
02
Healthcare Management and Patient Care
Healthcare Management and Patient Care
In healthcare settings, AI agents perform critical functions that enhance patient care while reducing the administrative burden on healthcare professionals. These agents assist in patient diagnosis by analyzing symptoms and medical histories, monitor vital signs in real-time, and manage medication schedules.
In healthcare settings, AI agents perform critical functions that enhance patient care while reducing the administrative burden on healthcare professionals. These agents assist in patient diagnosis by analyzing symptoms and medical histories, monitor vital signs in real-time, and manage medication schedules.
03
03
03
Information Technology and Support Operations
Information Technology and Support Operations
AI Agents can scale operational support for IT teams. From automated helpdesk responses to proactive system maintenance, these intelligent solutions optimize IT operations through continuous monitoring, predictive analytics, and automated issue resolution.
AI Agents can scale operational support for IT teams. From automated helpdesk responses to proactive system maintenance, these intelligent solutions optimize IT operations through continuous monitoring, predictive analytics, and automated issue resolution.
04
04
04
Supply Chain and Manufacturing Operations
Supply Chain and Manufacturing Operations
Set to transform the supply chain and manufacturing landscape, AI agents put into action intelligent automation and predictive capabilities to manage complex inventory systems, predict maintenance needs, and optimize production schedules across multiple facilities.
Set to transform the supply chain and manufacturing landscape, AI agents put into action intelligent automation and predictive capabilities to manage complex inventory systems, predict maintenance needs, and optimize production schedules across multiple facilities.
Feeling overwhelmed?
Feeling overwhelmed?
Feeling overwhelmed?
We can help.
We can help.
We can help.
Schedule a private consultation to see how SearchAI ChatBot will make a difference across your enterprise, distributed teams, and data silos.
Schedule a private consultation to see how SearchAI ChatBot will make a difference across your enterprise, distributed teams, and data silos.
Increased Efficiency
Increased Efficiency
Increased Efficiency
AI Agents manage multiple customer interactions at once, delivering personalized service to each conversation. This concurrent processing reduces customer wait times while efficiently scaling support operations.
AI Agents manage multiple customer interactions at once, delivering personalized service to each conversation. This concurrent processing reduces customer wait times while efficiently scaling support operations.
AI Agents manage multiple customer interactions at once, delivering personalized service to each conversation. This concurrent processing reduces customer wait times while efficiently scaling support operations.
Improved Customer Satisfaction
AI agents deliver prompt, accurate responses and personalized interactions, key drivers of customer satisfaction. Continuous learning ensures ongoing adaptation and service improvement.
Improved Customer Satisfaction
AI agents deliver prompt, accurate responses and personalized interactions, key drivers of customer satisfaction. Continuous learning ensures ongoing adaptation and service improvement.
Improved Customer Satisfaction
AI agents deliver prompt, accurate responses and personalized interactions, key drivers of customer satisfaction. Continuous learning ensures ongoing adaptation and service improvement.
24/7 Availability
24/7 Availability
24/7 Availability
AI agents transcend the limitations of human availability, offering uninterrupted service 24 /7. This ensures prompt responses to customer inquiries irrespective of time zones or business hours.
AI agents transcend the limitations of human availability, offering uninterrupted service 24 /7. This ensures prompt responses to customer inquiries irrespective of time zones or business hours.
AI agents transcend the limitations of human availability, offering uninterrupted service 24 /7. This ensures prompt responses to customer inquiries irrespective of time zones or business hours.
Scalability
Scalability
Scalability
AI agents seamlessly scale to accommodate growing customer interactions, ensuring consistent support and preventing service degradation during peak demand.
AI agents seamlessly scale to accommodate growing customer interactions, ensuring consistent support and preventing service degradation during peak demand.
AI agents seamlessly scale to accommodate growing customer interactions, ensuring consistent support and preventing service degradation during peak demand.
Data-Driven Insights
AI agents capture and analyze customer interactions, providing businesses with valuable data-driven insights into preferences, needs, and trends for informed decision-making.
Data-Driven Insights
AI agents capture and analyze customer interactions, providing businesses with valuable data-driven insights into preferences, needs, and trends for informed decision-making.
Data-Driven Insights
AI agents capture and analyze customer interactions, providing businesses with valuable data-driven insights into preferences, needs, and trends for informed decision-making.
Consistency and Accuracy
Consistency and Accuracy
Consistency and Accuracy
AI agents excel in providing standardized, error-free responses, ensuring customers receive reliable information and experience dependable service, building trust and confidence.
AI agents excel in providing standardized, error-free responses, ensuring customers receive reliable information and experience dependable service, building trust and confidence.
AI agents excel in providing standardized, error-free responses, ensuring customers receive reliable information and experience dependable service, building trust and confidence.
AI Agents Benefits
AI Agents Benefits
AI Agents Benefits
Why an Agentic Workforce is a Strategic Investment
Why an Agentic Workforce is a Strategic Investment
Why an Agentic Workforce is a Strategic Investment
Through adaptive processing and systematic actions, AI Agents unlock new possibilities for operational excellence and business transformation
Through adaptive processing and systematic actions, AI Agents unlock new possibilities for operational excellence and business transformation
Through adaptive processing and systematic actions, AI Agents unlock new possibilities for operational excellence and business transformation
AI Agents for Industries
AI Agents for Industries
AI Agents for Industries
AI Agents in Action
AI Agents in Action
AI Agents in Action
Shopping Agent
Shopping Agent
Shopping Agent
Enhance the shopping experience with a personalized touch. Shopping agents curate product recommendations, simplify returns, and provide real-time inventory updates - ensuring customers find exactly what they need while enjoying a seamless and satisfying journey.
Enhance the shopping experience with a personalized touch. Shopping agents curate product recommendations, simplify returns, and provide real-time inventory updates - ensuring customers find exactly what they need while enjoying a seamless and satisfying journey.
Enhance the shopping experience with a personalized touch. Shopping agents curate product recommendations, simplify returns, and provide real-time inventory updates - ensuring customers find exactly what they need while enjoying a seamless and satisfying journey.
Customer Experience Agent
Customer Experience Agent
Customer Experience Agent
Cultivate customer loyalty and satisfaction with proactive care. CX agents analyze feedback, identify at-risk customers, and initiate retention strategies, fostering positive relationships and reducing churn.
Cultivate customer loyalty and satisfaction with proactive care. CX agents analyze feedback, identify at-risk customers, and initiate retention strategies, fostering positive relationships and reducing churn.
Cultivate customer loyalty and satisfaction with proactive care. CX agents analyze feedback, identify at-risk customers, and initiate retention strategies, fostering positive relationships and reducing churn.
HR Agent
HR Agent
HR Agent
Optimize HR operations and empower employees with HR agents. Integrate Agents with your HR software to streamline onboarding, provide instant answers to policy questions, and automate leave requests, ensuring consistency and freeing HR professionals for strategic initiatives.
Optimize HR operations and empower employees with HR agents. Integrate Agents with your HR software to streamline onboarding, provide instant answers to policy questions, and automate leave requests, ensuring consistency and freeing HR professionals for strategic initiatives.
Optimize HR operations and empower employees with HR agents. Integrate Agents with your HR software to streamline onboarding, provide instant answers to policy questions, and automate leave requests, ensuring consistency and freeing HR professionals for strategic initiatives.
IT Support Agent
IT Support Agent
IT Support Agent
Ensure business continuity with round-the-clock technical support. Deliver personalized helpdesk resolution, troubleshoot technical issues, and manage security permissions, maintaining system stability and data protection - around the clock.
Ensure business continuity with round-the-clock technical support. Deliver personalized helpdesk resolution, troubleshoot technical issues, and manage security permissions, maintaining system stability and data protection - around the clock.
Ensure business continuity with round-the-clock technical support. Deliver personalized helpdesk resolution, troubleshoot technical issues, and manage security permissions, maintaining system stability and data protection - around the clock.
Sales Agent
Sales Agent
Sales Agent
Drive revenue growth and optimize your sales funnel. Qualify leads, nurture prospects, schedule product demos, and verify inventory status - maximizing conversion rates and streamlining the sales process.
Drive revenue growth and optimize your sales funnel. Qualify leads, nurture prospects, schedule product demos, and verify inventory status - maximizing conversion rates and streamlining the sales process.
Drive revenue growth and optimize your sales funnel. Qualify leads, nurture prospects, schedule product demos, and verify inventory status - maximizing conversion rates and streamlining the sales process.
Legal Agent
Legal Agent
Legal Agent
Navigate the complexities of legal compliance with efficiency and confidence. Legal agents automate contract analysis, manage document compliance, and schedule legal consultations, ensuring adherence to legal requirements while optimizing workflows.
Navigate the complexities of legal compliance with efficiency and confidence. Legal agents automate contract analysis, manage document compliance, and schedule legal consultations, ensuring adherence to legal requirements while optimizing workflows.
Navigate the complexities of legal compliance with efficiency and confidence. Legal agents automate contract analysis, manage document compliance, and schedule legal consultations, ensuring adherence to legal requirements while optimizing workflows.
Getting Started
Getting Started
Getting Started
Deploying SearchAI Agents across Enterprise Use cases
Deploying SearchAI Agents across Enterprise Use cases
Deploying SearchAI Agents across Enterprise Use cases
AI agent deployment begins with crystal-clear objectives. Your organization should identify specific goals, whether enhancing customer response times, boosting satisfaction metrics, or optimizing operational costs. This clarity serves as your strategic compass throughout implementation. For SearchAI deployments, these objectives directly inform which pre-built agents best align with your organizational needs.
AI agent deployment begins with crystal-clear objectives. Your organization should identify specific goals, whether enhancing customer response times, boosting satisfaction metrics, or optimizing operational costs. This clarity serves as your strategic compass throughout implementation. For SearchAI deployments, these objectives directly inform which pre-built agents best align with your organizational needs.
AI agent deployment begins with crystal-clear objectives. Your organization should identify specific goals, whether enhancing customer response times, boosting satisfaction metrics, or optimizing operational costs. This clarity serves as your strategic compass throughout implementation. For SearchAI deployments, these objectives directly inform which pre-built agents best align with your organizational needs.
Get started by downloading SearchBlox Enterprise Search Server based on your operating system. The installation guide for each OS will run you through the minimum requirements as well as a step-by-step process in getting started with SearchBlox.
Get started by downloading SearchBlox Enterprise Search Server based on your operating system. The installation guide for each OS will run you through the minimum requirements as well as a step-by-step process in getting started with SearchBlox.
Get started by downloading SearchBlox Enterprise Search Server based on your operating system. The installation guide for each OS will run you through the minimum requirements as well as a step-by-step process in getting started with SearchBlox.
Here's your roadmap to deploying SearchAI Agents with LLMs:
Here's your roadmap to deploying SearchAI Agents with LLMs:
Here's your roadmap to deploying SearchAI Agents with LLMs:
Define Your AI Agent Roadmap
Define Your AI Agent Roadmap
Define Your AI Agent Roadmap
Choose Your Deployment Environment
Choose Your Deployment Environment
Choose Your Deployment Environment
Select and Customize Your AI Agent
Select and Customize Your AI Agent
Select and Customize Your AI Agent
Optimize your knowledge infrastructure
Optimize your knowledge infrastructure
Optimize your knowledge infrastructure
Launch SearchAI Integration
Launch SearchAI Integration
Launch SearchAI Integration
Monitor, Analyze, and Improve
Monitor, Analyze, and Improve
Monitor, Analyze, and Improve
Drive value with automation
Reimagine work with AI Agents by your side
Reimagine work with AI Agents by your side
Reimagine work with AI Agents by your side
Build custom AI agents that understand the context you’re in, and respond naturally to complete tasks.
Build custom AI agents that understand the context you’re in, and respond naturally to complete tasks.
Build custom AI agents that understand the context you’re in, and respond naturally to complete tasks.
Enhance your users’ digital experience.
We build AI-driven software to help organizations leverage their unstructured and structured data for operational success.
4870 Sadler Road, Suite 300, Glen Allen, VA 23060 sales@searchblox.com | (866) 933-3626
Still learning about AI? See our comprehensive Enterprise Search RAG 101, ChatBot 101 and AI Agents 101 guides.
©2025 SearchBlox Software, Inc. All rights reserved.
Enhance your users’ digital experience.
We build AI-driven software to help organizations leverage their unstructured and structured data for operational success.
4870 Sadler Road, Suite 300, Glen Allen, VA 23060 sales@searchblox.com | (866) 933-3626
Still learning about AI? See our comprehensive Enterprise Search RAG 101, ChatBot 101 and AI Agents 101 guides.
©2024 SearchBlox Software, Inc. All rights reserved.
Enhance your users’ digital experience.
We build AI-driven software to help organizations leverage their unstructured and structured data for operational success.
4870 Sadler Road, Suite 300, Glen Allen, VA 23060 sales@searchblox.com | (866) 933-3626
Still learning about AI? See our comprehensive Enterprise Search RAG 101, ChatBot 101 and AI Agents 101 guides.
©2025 SearchBlox Software, Inc. All rights reserved.