Building Agentic AI: Definition, Features, Benefits, and Frameworks You Can Use To Create AI Agents

Whether you're looking to streamline operations, scale decision-making, or unlock new value from your data, building agentic AI can redefine what’s possible for your business. 

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AI agents are not just smart assistants. These are decision-makers, problem-solvers, and collaborative goal-oriented systems that learn and adapt in real time. AI agents don’t not just generate outputs from inputs, but they perceive, reason, and act with autonomy. 

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In this blog, we will explore what agentic AI is, why it matters, and the practical frameworks and steps you can use to start building intelligent agents today.

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In the world of agentic AI, there is an element of reasoning and a dynamic way of being able to determine what the next step should be based on the specific context of where you're applying the particular solution. It has a lot of power in terms of being able to adapt, transform and tailor what is needed for each particular situation to extract more value. That's why I view agentic AI as a step function change compared to traditional automation techniques”

‍- Jas Jaaj, Managing Partner of AI, Deloitte Canada
LinkedIn post

What is an AI agent?

An AI agent is a computer program that helps people get things done. It can answer questions, complete tasks, and make life easier. The main goal of an AI agent is to be helpful. AI agents understand and respond to human language, so talking to them feels natural.

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Agentic AI sets its own goals and plans ahead. Instead of just reacting to inputs, these systems are designed to act on their own, adjust to new situations, and work toward complex goals with minimal human input. This shift marks a move from AI that just understands and responds to one that can also act and achieve outcomes independently.

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There are different kinds of AI agents:

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• Assistive agents that help employees with tasks specific to their jobs by working inside the tools they already use. 
• Autonomous agents that handle things on their own, like answering customer questions without needing a person to step in.

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To make an AI agent work well, you need to train it. This means collecting data, teaching the model, testing it, improving it, and then making it available for use. Once it’s up and running, you’ll also need to keep an eye on it and update it as needed. Next, we’ll walk through each step so you can learn how to build one yourself.

What are the features of an AI agent?

The latest wave of AI—driven by large language models (LLMs) like GPT and Bard, excel at answering questions, summarizing information, translating languages, and creating content based on user prompts. However, they are mostly reactive, and respond only when prompted.

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On the other hand, AI works autonomously with minimal human intervention. Its features include: 

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• Autonomy: Operates independently without constant human input initiating and completing tasks as needed to achieve its objectives
  
• Goal-oriented behavior: Designed to understand, prioritize, and execute toward specific goals that are either assigned by users or generated internally or by breaking them into manageable subtasks
  
• Adaptability and contextual decision-making: Responds to changing conditions by analyzing context, weighing trade-offs, and adjusting plans in real time, unlike rule-based systems that follow fixed scripts
  
• Suitability for dynamic environments: Particularly effective in complex, fast-changing scenarios where flexibility and responsiveness are critical

Why should you build agentic AI?

Building agentic AI is about owning your future workflows instead of outsourcing them to generic tools. It’s a strategic move, not just a tech upgrade. Here’s why you should build an AI agent:

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• Stay competitive in a changing market: As AI capabilities evolve, competitors are embedding intelligence into operations. Building agentic AI helps businesses avoid falling behind and prepares them for an AI-first economy

• Off-the-shelf tools aren’t enough: Generic AI tools (Like chatbots or copilots) can’t handle company-specific workflows or decision logic. Building AI agents in-house gives businesses control over competitive assets such as custom logic, domain knowledge, and proprietary data

• Operationalize internal knowledge and data: Agentic AI lets you turn your excess untapped data data into real-time decisions and actions, not just dashboards and reports

• Enable scalable decision-making without hiring: Human teams don’t scale linearly with business growth. Agentic AI lets companies scale capabilities (like support, analysis, coordination) without bloating headcount or burning out employees

• Future infrastructure will be agent-led: The next phase of digital transformation isn’t about dashboards. It’s about agents that observe, reason, and act. Businesses that start building now will have a significant head start in adapting their systems, teams, and processes

How to build an AI agent in 7 easy steps

Developing an AI agent may appear complex at first. But when the process is thoughtfully broken down, it becomes far more approachable, even strategic. This guide outlines seven steps you can follow to build AI agents effectively:

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Step 1: Define the agent’s purpose and scope

Before you begin development, establish a clear understanding of what your AI agent is meant to accomplish. This foundational step will influence every decision moving forward. Ask critical questions like:

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• What specific tasks will the AI agent perform?
• What outcomes are expected? Is it efficiency, automation, or improved customer experience?
• What data will your agent use? 
• What level of autonomy is required? fully automated or human-in-the-loop?
• What compliance, privacy, or ethical considerations must be addressed?

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Answering these questions will help you define the scope of your agent and align its capabilities with business  goals. If you face ambiguity at this stage, consult the team at 5X.

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Step 2: Build your team

The quality of your AI agent depends on the quality of your team. Assemble a team with necessary skills that complement each other across machine learning, data engineering, software development, and user experience.

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Key roles may include:

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• Machine Learning Engineer
• Data Scientist
• Software Engineer
• UI/UX Designer
• DevOps Engineer

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Depending on internal bandwidth and budget, consider whether hiring in-house or outsourcing specific roles offers the best return on investment.

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Step 3: Collect, clean, and prepare data

AI is only as good as the data you feed it. You need clean, relevant, and bias-free data to build high performing and reliable AI agents. Gather good-quality data from internal sources and public datasets such as:

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• Internal systems (e.g., CRM, ERP, customer support logs)
• External datasets (e.g., market research, public databases)
• User-generated content (e.g., social media, product reviews)
  
  

Once sourced, clean the data by fixing errors, removing duplicates, normalizing formats, resolving missing values, and removing biases. The better your data, the more accurate your AI outputs.

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Step 4: Select the right tech stack

When building AI agents, choose AI tools based on what your agent needs to do. The tool should be easy to use and should work with your current systems. For example:

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• AI frameworks: TensorFlow, PyTorch, Scikit-learn for model development
• NLP & vision tools: Hugging Face Transformers, spaCy, OpenCV
• Deployment platforms: AWS, Azure, GCP for cloud; edge computing for latency-sensitive use cases
• Development tools: Modern IDEs, data pipeline managers like Apache Kafka, databases like MongoDB
  
  

Choose tools with robust documentation and strong community support. Your choices will determine scalability, maintenance complexity, and integration speed.

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Step 5: Design the agent architecture

Design how your AI agent will operate from end to end. This includes structuring its components, defining workflows, and establishing data and decision pathways.

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Architecture options include:

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• Modular architecture: Build independent modules (e.g., input, processing, output) for flexibility and easier maintenance.
• Concurrent design: For real-time or multi-task agents, concurrent processing ensures efficiency.
  
  

Key elements to define:

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• Input sources and formats
• Processing and decision logic
• Output types and delivery mechanisms
• User interface and feedback loops
• Learning policies, especially if using reinforcement learning
  
  

This design phase ensures alignment between business intent and technical execution.

Step 6: Develop and integrate the agent

Next, start coding. Connect the AI agent to other systems (like databases or APIs). Add learning features so it improves over time. Then test each part, how they work together, and how it performs under real conditions.

Begin coding and system integration. Develop each module in alignment with the architectural blueprint and perform iterative testing.

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Include:

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• Core functionality: Develop logic for classification, prediction, recommendation, etc.
• System integration: Implement APIs and connect to databases and enterprise systems
• Learning capability: Use ML frameworks to enable adaptive performance
• Memory systems: Introduce data recall features for contextual awareness and personalization
  
  

Testing is critical, so conduct unit tests to validate individual components. Also run performance tests to simulate real-world scenarios, and integration tests to ensure modules work together.

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Step 7: Deploy, monitor, and improve

Once the AI agent passes pre-production testing, proceed with deployment. Before launching fully, test it in a safe, trial environment. Start small, gather feedback, and improve. 

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After deployment:

• Monitor key metrics: response time, output accuracy, user satisfaction
• Gather user feedback to iterate and improve performance
• Maintain a schedule for regular updates, retraining, and security reviews
• Keep updating continuously to fix bugs or improve features.
  
  

Sustained performance relies on consistent monitoring, user-centric enhancements, and responsiveness to business and regulatory changes.

Top AI agent frameworks

Agentic AI frameworks are powerful tools for building intelligent, goal-driven systems for organizations that want to automate complex workflows and enhance decision-making with AI.  

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Here are some examples:

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• LangChain: An open-source framework offering a modular architecture for building agents. It builds applications with LLMs that interact with tools, memory, and environments
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  • Key features: Tool use, memory integration, multi-step reasoning
  • Use case: AI assistants, RAG pipelines, autonomous agents

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• Autogen (by Microsoft): Framework for building conversational agents that collaborate with humans or each other to meet a goal
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  • Key features: Multi-agent support, conversational planning, tool integration and code execution‍
  • Use case: Software development, research agents, co-pilot systems

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• CrewAI: Focuses on multi-agent collaboration with defined roles.
  
  • Key features: Role-based agents, coordination, LangChain-compatible
  • Use Cases: Team-based AI workflows (e.g., research + coding + content)

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