What is Agentic AI? A Practical Guide

01

What is agentic AI?

Agentic AI is an agent-based AI system that combines chain-of-thought reasoning and continuous planning to independently execute complex tasks and solve complicated problems. It's poised to increase productivity and enhance operations across an entire range of industries.¹

An agentic AI framework aggregates huge amounts of data from multiple source systems to come up with strategies, uncover challenges, and achieve objectives like optimizing supply chains, ensuring data security, and complying with data privacy laws.

Essentially, agentic AI is all about autonomy
It's designed to decide, act, and even learn on its own to achieve specific goals. It’s like having a virtual assistant that can adapt to changing circumstances without needing constant direction.²

Agentic AI has 4 key components:

Perception: The system collects data from the world around it.
Reasoning: It processes that data to understand context.
Action: It takes action based on its understanding.
Learning: It refines itself over time, by learning from experience.

These components will be discussed in greater detail in section 5.

For example, a traveller might ask an airline chatbot: Do you offer discounted rates for emergency flight tickets due to a death in the family?

An LLM using a basic Retrieval-Augmented Generation (RAG) system could answer this very easily, by accessing the airline's vector database and retrieving the policy information in question.

But what if Jake, a frequent flyer whose grandmother suddenly passed away and had to book a flight in minutes in order not to miss the funeral, posed a more detailed question like: Due to a sudden death in the family, I need to book a flight from St. Louis to New York now. How do I purchase tickets online that take your bereavement policy into account?

Answering this questions is much more complex than just looking up the airline's policies. It might involve getting Jake’s personal data from multiple company sources, like Customers, Frequent Flyers, and Finance. But it might also require access to the airline's national flight system as well as to external databases for connecting or return flights via other carriers.

At best, an advanced RAG framework may be able to collect Jon’s company-related data, but it lacks the ability to connect it to the airline's billing apparatus or to sync with national flight systems in real time.

That’s when agentic AI really shines – when queries demand sequential reasoning, planning, and memory, aided by active retrieval-augmented generation.

Agentic AI might divide the task into a series of subtasks, such as:

Retrieve data from enterprise systems to retrieve Jake’s perslonal information from Customers, Frequent Flyers, and Finance.
Access the airline's national flight system to give Jake priority in booking the flight.
Connect to the billing system to ensure that the bereavement discount is applied to the fare on order.
Hook into external flight databases to coordinate Jakes travel plan with other carriers if necessary.

To complete these subtasks without a human in the loop, a structured plan, reliable memory, and access to external tools are required. These components form the backbone of the agentic AI workflow.

02

What are AI agents?

AI agents are designed to handle specific tasks like answering questions, organizing your calendar, or even managing your email. They're great at automating simple, repetitive tasks but don’t have the autonomy or decision-making abilities that agentic AI does. Think of them as virtual helpers that do what they're told, without thinking for themselves.²

Alhough "AI agents" and "agentic AI" have "AI" in common, they have different attributes, and operate in different ways, as summarized below:

Attribute	Agentic AI	AI agent
Level of autonomy	High	Low – Human inputs needed
Methodology	Goal-oriented – Designed to solve problems by itself	Task-specific – Designed to follow instructions
Learning capabilities	High – Constantly learning and improving	Low – Can learn, but only within set rules
Ability to handle complexity	High – Built for complex, dynamic environments	Low – Designed for simpler, more structured tasks
Decision-making ability	Decisive – Based on reasoning and analysis	Pre-programmed – Based on responses to inputs
Interaction with environment	Active – Adapts to surroundings and changes	Reactive – Reacts to set inputs, but doesn't adapt
Responsiveness to change	High – Changes goals and methods autonomously	Limited – Can somewhat adapt to new situations

03

Why agentic AI?

Agentic AI addresses the following enterprise demands:³

Flexibility AND precision

Large Language Models (LLMs) are awesome at understanding and generating human-like text, making it easier for people to interact with computers using GenAI. And the best thing is, you don't need to be a programmer to use them.

Heads-down programming, on the other hand, is structured and reliable, making it ideal for tasks requiring precision and consistency. Programming controls how tasks are executed, ensuring successful completion. It's also more efficient for jobs requiring high performance or specialization.

Agentic AI combines the best of both worlds, using LLMs for more creative and flexible undertakings, and programming for rules-based, high-performance tasks.
Wide reach

LLMs are limited by nature. To begin with, they're trained on static datasets so their information is inherently dated. They can't gather any new publicly available information, or update real-time data from private sources. They can't even monitor data using third-party tools.

Agentic AI, however, can search the web, call APIs, and query databases to fetch real-time information and updates. Its team of agents can track real-time data and analyze trends. They can also collect data from both public and private sources, providing your LLM with clean, fresh data for better decision-making.

It can also use feedback loops to refine its models and decision-making processes by actively seeking new data, collecting user feedback, and analyzing real-world outcomes. Equipped with these capabilities, agentic AI systems improve over time thanks to continually evolving data.
Autonomy

Agentic AI leverages the intelligence of LLMs and its agents' ability to work independently without a human in the loop. Such systems are great at managing long-term goals, handling multi-step tasks, and keeping track of progress over time. Examples abound:
- Healthcare
  
  Clinical agents can monitor patient data, adjust treatments based on lab results, and offer real-time opinions to care providers.
- 3PL and supply chains
  
  Agentic AI can autonomously place orders with suppliers, or adjust production schedules, to maintain optimal inventory levels.
- Data security
  
  Security agents can continuously monitor network traffic, system logs, and user behavior to identify potential security threats.
- Human resources
  
  HR agents can create personalized onboarding training paths for new hires, adjusting material based on prior experience, role requirements, and learning skills.
- Marketing
  
  Agentic AI can manage a campaign, monitor performance, adjust strategies, and optimize results based on feedback without human oversight.
Intuition

Agentic AI systems can replace or enhance many business functions currently handled by SaaS products by allowing workers to interact with data and perform tasks more efficiently using natural language and simple interfaces.

For example, in a ticketing system used by software developers, finding relevant data often requires navigating reams of menus and tables. Imagine if you could request the data you need in plain English, or create a slide slide with bar graphs showing completed tickets per employee for the current month, going back 3 years, without manually sorting through data.

This process, which might take hours manually, could be done in seconds by agents. For organizations struggling to justify investments in GenAI, agents could quickly show tangible business value.

04

Agentic AI architecture

An agentic AI architecture endows AI agents with agentic behavior. Not all AI agents are agentic. Turning regular agents into agentic ones requires complex orchestration.⁴

An agentic AI architecture encourages AI agents to decide what to do, and act on those decisions without a human in the loop.

In a non-agentic architecture, LLMs are capable of singular or linear tasks, meaning that its outputs are simply a function of its inputs and context.

Without explicit orchestration, LLMs can't be grounded with fresh enterprise data and context with the near real-time latency required for conversational AI.

Choosing the right agentic AI architecture depends on the specifications of your GenAI app and use case. For example, while a single action agent may be great at resolving a specific issue, some challenges might require the services of a specialized agent, while others might call for multiple agents working together as a team.

Types of agentic AI architectures

The following table shows the 3 different types of AI agent architectures and how they compare:

Attribute	Vertical agentic AI architecture	Horizontal agentic AI architecture	Hybrid agentic AI architecture
Structure	Leader-agent model: A centralized system in which agents report to a leader who controls tasks and decisions	Peer collaboration model: A decentralized system in which agents collaborate freely as equals	Flexible model: A system in which control is awarded to either structured leadership or collaborative agents
Key capabilities	Hierarchy: Clearly defined roles Centralized structure: Agent-to-leader reporting	Distributed collaboration: Resources and ideas shared among All agents Collaborative decision-making: Group-driven decisions for autonomous operation	Dynamic prioritization: Control divvied up based on each step in a multi-step task Collaborative leadership: Open interaction between leaders and agents
Pros	Efficiency: Optimized for sequential workflows Accountability: Leader responsible for achieving goals	Dynamic problem solving: Innovation driver Parallel processing: Tasks tackled simultaneously	Versatility: Strengths of both models combined Adaptability: Accomplishment of tasks requiring both structure and creativity
Cons	Bottlenecks: Progress slowed by reliance on a leader Single point of failure: Vulnerable to poor decision-making by the leader	Coordination challenges: Inefficiencies due to mismanagement Slower decisions: Too much time spent on deliberation	Complexity: Reliable controls needed to balance structured leadership vs open collaboration Resource management: Very demanding
Use cases	Workflow automation: Approvals for each step in a multi-step task Document generation: Overseen by a leader	Brainstorming: Diverse ideas generation Complex problem solving: Taking on inter-disciplinary challenges	Versatile tasking: Team projects and strategic planning, for example Dynamic processes: Maintaining the structured vs creative balance

05

Agentic AI components

Agentic AI is based on 4 integral components:⁵

1. Perception

The perception component collects and interprets data from the environment using sensor technologies and data ingestion pipelines – in the same way a self-driving car perceives its surroundings using its camera and and radar systems to gather and process visual and spatial information.

2. Reasoning

The reasoning component employs chain-of-thought reasoning and techniques like heuristic decision trees based on predefined rules, or heuristics, to arrive at decisions. The rules, which are based on experience or knowledge, may not always lead to optimal outcomes, but they can be useful in complex situations.

3. Action

The action component interfaces with real or simulated environments to execute on decisions. For example, in IT systems this might mean initiating processes or communicating with other software components. The effectiveness of this component depends on the precision and pace of the actions.

4. Learning

Agentic AI learns through various machine learning techniques:

a. Supervised learning

Supervised learning uses labeled data to train your agentic AI system to recognize patterns to predict the most appropriate response.

b. Unsupervised learning

Unsupervised learning identifies hidden structures in unlabeled data to discover underlying patterns.

c. Reinforcement learning

Reinforcement learning suggests optimal actions by interacting with the environment and receiving feedback through rewards or penalties.

Using these learning techniques is an iterative process that allows agentic AI to refine its behavior and improve its performance over time.

06

Role of functions in agentic AI

Agentic AI systems makes extensive use of functions, which are executable units of programming logic built to accomplish specific objectives. Functions can be embedded in your LLM or called upon when needed.

Intrinsic functions are built into your LLM

Intrinsic functionality includes:

Text processing

Text processing includes turning LLM text to SQL, tagging of figures of speech, data tokenization, and Named Entity Recognition (NER) which can detect and classify entities like names, dates, or events.
Natural Language Understanding (NLU)

NLU includes intent recognition, which tries to understand the context of the query, sentiment analysis, which attempts to comprehend the emotional tone of the text, and semantic parsing, which transforms natural language into structured data or commands.
Natural Language Generation (NLG)

NLG includes text generation, designed to write human-sounding text based on prompt engineering techniques like paraphrasing, which conveys the same meaning but in different words, and summarizing, which shortens long texts while retaining the main information.

External functions are built to interact with other systems

Examples of external functions include:

Database queries

Database queries are SQL functions that write and execute SQL queries to collect or use data from databases.
API integration

API integration includes HTTP requests to external APIs to ensure relevant data is available, and service integrations, which connect to various external services (e.g., financial market information, weather data, etc.).
Custom logic

Custom logic leverages rules-based systems, which use pre-defined rules to make decisions or take actions, and specialized algorithms for tasks like recommending, sorting, etc.

Hybrid approaches combine intrinsic and external functions

Common examples of hybrid functions are:

Workflow automation, in which an agent extracts data from text, intrinsically, and then goes on to use that same data to update a database, externally.
Dialog management, which controls conversations by using NLU, NLG, and a variety of external functions.

Agentic AI function calling considerations

Below are the 3 main considerations for LLM function calling:

Security

When interacting with external systems or databases, make sure that secure methods and protocols are used to safeguard sensitive data according to your LLM guardrails.
Efficiency

Optimize function calling to minimize conversational latency and compute overheads.
Scalability

Build functions to handle varying loads and scalable interactions, especially for apps with high user engagement.

By leveraging different types of agentic AI function calling, AI agents can perform a wide variety of tasks, making them highly effective for many generative AI use cases.

07

Types of agentic AI agents

There are many different types of agentic AI agents at your disposal, including:

Single-action AI agents

Task-oriented AI agents complete tasks like answering questions, scheduling events, or supporting customers.
Conversational AI agents converse with users, via RAG chatbots, for example.

Multi-action AI agents

Collaborative AI agents work together to accomplish a common objective, especially in multi-step problem-solving situations.
Competitive AI agents train models to operate effectively under real market conditions.

Reactive AI agents

Event-driven AI agents react to a specific event like a real-time alert or notification.
Rules-based AI agents are typically used to monitor systems and react to triggers based on predefined rules.

Proactive AI agents

Predictive AI agents predict user needs or future events based on historical data.
Preventive AI agents avoid problems by analyzing patterns and taking corrective measures in advance.

Interactive AI agents

Question answering AI agents reply to user queries based on context, typically employing knowledge bases.
Advisory AI agents offer suggestions and advice after analyzing user preferences and past behavior.

Backend integration AI agents

SQL AI agents connect to databases to execute SQL queries, retrieve data, and manage data.
API AI agents interact with programming interfaces to gather information or cause other systems to react.

Domain-specific AI agents

Healthcare AI agents involve themselves with patient records, interactions, and medical data.
Educational AI agents are involved with teaching, tutoring, and customizing educational content to individual needs.

Autonomous AI agents

Self-learning AI agents improve performance by learning from feedback via reinforcement learning techniques.
Self-repairing AI agents diagnose and fix errors in their systems all by themselves.

Hybrid AI agents

Multi-functional AI agents integrate different agentic capabilities to offer broader solutions, such as a chatbot that can also do transactions.
Context-aware AI agents adapt their behavior to the context of the query, to quickly adapt to dynamic scenarios.

08

Benefits of agentic AI

By powering AI agents to perform a wider variety of tasks than ever before, agentic AI gives new meaning to automation, while enhancing the quality of interactions between agents and humans. Here are the key benefits for enterprises:⁶

Greater efficiency and productivity

AI agents can now handle complex, decision-making tasks that were beyond the reach of machines. Agentic AI allows businesses to focus on strategic initiatives, solve problems more creatively, and build stronger customer relationships.
Increased customer satisfaction

Agentic AI is enhancing the customer experience by providing much more personalized and meaningful interactions at the scale and speed of AI. Specialized AI agents can better understand customer intent, anticipate needs, and provide solutions 24/7.
Better human-machine interaction

Agentic AI actually enhances human performance, productivity, and engagement. It acts as a bridge between people and machines by easily integrating with your existing systems and processes. It lets you quickly address complex challenges, automate decision making, and drive efficiency across your organization.

09

Challenges of agentic AI

While agentic AI systems can be unbelievably useful, they also face considerable challenges, including:

Risks of accessing data from operational systems
Direct access to live systems often results in unmanageable spaghetti code, excessive load problems on production data sources, and security issues, like controlling user access to data – with each function having to deal with access controls on its own. Agentic AI function calling can be a fantastic asset but must be reined in.
Low scores for context-awareness
AI agents can only handle a relatively small amount of data at any given time, so they may not remember important details from earlier conversations. A graph database may be able to access to more information, but it doesn’t solve the problem.
Limited planning ability
Agentic AI can’t plan long-term because it can’t anticipate unexpected scenarios. So human oversight is often needed.
Inconsistent outputs
Agentic AI depends exclusively on human language to interact with other tools and databases, so it sometimes produces AI hallucinations. It's also been known to make formatting mistakes and to not follow instructions very carefully.
Prompt attention needed
Agentic AI relies on effective AI prompt engineering, but the resultant prompts aren't always as precise as they could be. Minute mistakes can lead to massive errors, so serious attention must be paid to prompt creation and refinement.
Difficulty defining roles
Agentic AI is tasked with matching its AI agents to different tasks, but fine-tuning them – to take on unusual roles or sympathize with human emotion – is an almost impossible task.
Unreadiness for data readiness
Data readiness can make or break agentic AI effectiveness. Making your real-time enterprise data AI-ready – compliant, complete, and current – is a tall order. Agentic AI relies on fresh, trusted data to make decisions and act on them. Unfortunately, bad data leads to bad decisions and actions.
Questionable cost efficiency
Agentic AI systems can be a drain on your resources. When you need to process large amounts of data quickly, your costs tend to rise and your system's performance tends to falls if your agentic AI isn't managed properly.

Addressing these challenges is critical for improving the effectiveness and reliability of agentic AI in all its forms.

10

Getting the most out of agentic AI with K2view

K2view is rethinking enterprise data and how we organize it for agentic AI. Instead of thinking big – fishing big data out of a big data lake, and then having to hardcode hundreds of functions to address an infinite amount of queries – it's time you started thinking small.

A Micro-Database™ that stores and manages all the data for a single entity (a customer, for instance) can be queried in less than a second to answer any question related to that entity securely and accurately.

LLM agent builder

GenAI Data Fusion, the market-leading RAG tool by K2view, features:

Chain-of-thought prompting
Automatic text-to-SQL, data retrieval, and data summary
Over 200 embedded data processing functions
Framework optimized for Agentic

K2view enables you reap all the benefits of agentic AI and overcome all its challenges. Immediate access to your fresh enterprise data lets you field any GenAI question safely and accurately.

Discover K2view GenAI Data Fusion,
the RAG tool optimized for agentic AI.

What are LLM agents?

LLM agents are AI systems that leverage Large Language Models (LLMs) trained on enormous amounts of text data, to understand, imitate, and generate human language. The agents use LLMs to perform language-related tasks designed to improve decision-making and user/system (e.g., customer/chatbot) interactions.

LLM agents are designed to provide accurate text responses based on sequential reasoning. Ideally, agents can remember past conversations, think ahead, and adjust their responses to the context of the query.

What are LLMs?

An LLM is a large language model trained externally on vast amounts of textual information (typically billions or trillions of words). An enterprise LLM can also be grounded internally with the trusted private data of your company or organization. By studying all this information and data, the model learns the intricate patterns and complex relationships that exist between words and ideas, enabling it to communicate more effectively with different types of users, like customers, employees, or vendors.

What do LLM agents do and how do they do it?

LLM agents can be used to:

Answer questions, with greater relevance and accuracy.
Summarize texts, preserving only essential information.
Translate texts, with context and nuance.
Analyze sentiment, for social media monitoring, and more.
Create content, where unique and engaging material is required.
Extract data, like names, dates, events, or locations.
Generate code, debug, or even write entire programs.

To do this, they rely on 2 core technologies:

Natural Language Understanding (NLU) enables them to comprehend human language and also deduce context, sentiment, intent, and nuance.
Natural Language Generation (NLG) empowers them to create coherent and contextually relevant text.

What are the key components of LLM agent architecture?

The key components of LLM agent architecture include:

Transformer architecture

Transformers use self-attention, to prioritize the importance of different words in a sentence, and multi-head attention, to allow the model to focus on different parts of a sentence at the same time.
Encoder-decoder structure

The encoder processes the input text, while the decoder generates the output.

While some models use only the encoder (like BERT) or only the decoder (like GPT), others (like T5) use both the encoder and the decoder.
Large-scale pre-training

Models are pre-trained on vast datasets containing diverse text from books, websites, and other sources. Pre-training helps the model understand language patterns, facts, and general knowledge.
Fine-tuning

After pre-training, models often go through fine-tuning on domain-specific data to enhance their performance in tasks like customer service, for example.

How do LLM agents use functions?

An LLM agent framework makes use of functions, which can be defined as executable units of programming logic designed to achieve specific goals. Functions can be intrinsic, embedded in your LLM, external, called upon when needed, or hybrid, a combination of the two.

What are the benefits of using LLM agents?

LLM agents can solve complex problems, learn from mistakes, employ various tools to enhance their effectiveness, and even collaborate with other agents to improve their performance. Their key capabilities include:

Problem solving
Self-evaluation
Performance improvement

What are the challenges of using LLM agents?

While LLM agents can be incredibly useful, they also face several challenges, including:

Risks of accessing live systems
Poor at context
Limited ability to plan
Inconsistent outputs
Difficulty adapting to different roles
Dependence on good prompts
Lack of data readiness
Cost and efficiency

How does K2view overcome these challenges?

K2view closes the generative AI data gap by showing you how to use your enterprise data to power your LLM, making it ready to handle any GenAI question, by anyone, while never compromising on data privacy and security.

AI Data Fusion, the company’s revolutionary suite of RAG tools, features a no-code LLM agent builder enabling:

Chain-of-thought prompt orchestration
Text-to-SQL, data retrieval, and data summary
200+ prebuilt data processing functions
LLM abstraction capabilities
Multi-agent system design
Built-in interactive visual debugger

What challenges are associated with RAG?

Accessing all the information and data stored in internal knowledge bases and enterprise systems in real time
Generating the most effective and accurate prompts for the RAG framework
Keeping sensitive data hidden from people who aren’t authorized to see it
Building and integrating retrieval pipelines into applications

When is RAG most helpful?

Retrieval-augmented generation has various applications such as conversational agents, customer support, content creation, and question answering systems. It proves particularly useful in scenarios where access to internal information and data enhances the accuracy and relevance of the generated responses.

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A practical guide to agentic AI

What is Agentic AI?

Table of Contents

Agentic AI is an agent-based AI system that employs chain-of-thought reasoning and iterative planning to autonomously complete complex, multi-step tasks.

01

What is agentic AI?

02

What are AI agents?

03

Why agentic AI?

Flexibility AND precision

Wide reach

Autonomy

Intuition

04