Did you know that the agentic AI market is expected to jump from US$1.5 billion in 2024 to a staggering US$1.5 billion by 2030?1 These numbers are impressive, but what is really happening behind this expansion?
Today, we have a new form of artificial intelligence that goes beyond what we're used to seeing. Unlike traditional AI, which simply responds to our requests, agentic AI makes its own decisions and performs tasks automatically, without needing detailed instructions for each step.23It's as if she thinks for herself – although we know that's not quite the case.
You can look around and you'll notice that organizations have already started using these AI virtual agents in a variety of ways—from simple tools that enhance what we already do to systems that automate entire workflows.4The interesting thing about these systems is that they can learn from their experiences and create multi-step plans to solve complex problems.5.
Key characteristics include autonomy, goal orientation, adaptability, ability to improve themselves, and interactivity3But the question remains: how can we use this in practice?
In this guide, you'll discover concrete examples of how to implement these technologies, strategies for dealing with the challenges that will arise along the way, and a complete roadmap for creating custom workflows that can transform the way your business operates.
Fundamental Concepts of Agentic AI and Personalized Workflows
Image Source: Simform
Agentic AI represents a fundamental shift in how we think about artificial intelligence. To truly understand what it offers, we must first understand how it differs from what we already know.
Difference Between Agentic AI and Traditional AI
When we interact with traditional AI, we're essentially talking to something that only responds to what we ask. It operates under a reactive model, responding to specific commands or analyzing data based on instructions someone programmed in advance.6It's like having an assistant who only does exactly what you tell him to do, without taking any initiative of his own.
Agentic AI works completely differently. It's proactive, taking initiative based on its analysis of complex environments. While generative AI focuses on creating content based on learned patterns, agentic AI goes further—it applies these findings to achieve specific goals.6.
The main difference is in the autonomy and decision-making capacityTraditional systems rely on algorithms or rules that someone has previously defined, requiring constant human oversight. Agentic AI, on the other hand, demonstrates autonomous behavior, can assess situations, and determine the path forward with little or no human input.6.
The interesting thing is that it can learn and operate on its own, optimizing workflows and executing complex tasks without needing someone to control every step. This way, it can autonomously manage business processes, such as reordering supplies or optimizing supply chain operations.6.
What are autonomous agents and how do they work?
Autonomous agents are AI systems designed to perform tasks independently, without needing someone to hold their hand all the time. They make decisions, plan actions, and adapt to different situations to achieve specific goals.7.
The operation of these agents follows a four-step process that works like this:
- Perception: Collect and process data from the environment – sensors, databases, user interfaces8.
- Reasoning: Analyze collected data to understand the context and identify possible solutions, often using a Large Language Model (LLM) as an orchestrator6.
- Action: Take concrete action based on analysis, performing tasks, making decisions, or interacting with other systems8.
- Apprenticeship: Continuously improve through the feedback received, refining their future actions8.
These agents use advanced techniques such as natural language processing, machine learning, and real-time data analysis. Key features include autonomy, adaptability, interactivity, and continuous learning.9.
Overview of agentic workflows
Agentic workflows are AI-driven systems that operate autonomously, adapt to changing conditions, and perform tasks intelligently without constant human supervision.10. They represent sequences of well-defined work dynamically executed by AI agents as part of a complete business process automation11.
Here it is important to understand that these workflows are based on three fundamental pillars:
- Data collection and processing: Agents collect information from the environment to inform decision-making10.
- Analysis and determination of actions: Analyze collected data and determine the best course of action based on predefined goals10.
- Execution and learning: Take action and learn from the results for continuous improvement10.
AI orchestration plays a key role in agentic workflows, coordinating and managing systems and agents. Orchestration platforms automate workflows, track progress, manage resources, and handle failure events.12.
With the right architecture, multiple agents can work together, either in a vertical hierarchy with a “driver” model supervising other agents, or in a decentralized horizontal structure with agents working as equals.12.
Steps for Building a Purpose-Built AI Agent
Image Source: Kodexo Labs
Building a truly functional AI agent isn't as simple as it seems in the tutorials we see out there. I've seen many people create agents that seemed impressive in theory, but in practice didn't solve any problems.
To create something that truly meets your business needs, you need to follow some very specific steps. And believe me, skipping any of these steps will cost you time and money later.
Defining the agent's objective
The first mistake I see happening is trying to create an agent that does “everything.” Without well-defined objectives, your AI agent will be like those employees who do a little bit of everything, but none of them do it well. Goals need to be specific, achievable, measurable, and quantifiable to ensure success.13.
During this phase, ask the right questions:
- What specific problems will the agent solve?
- What tasks will it perform autonomously
- Which target audience will it serve?
- What situations or use cases should it cover?
Here it is essential to establish clear KPIs that allow you to measure agent performance and use this data to improve the model over time.13For example, if you're building a customer service agent, metrics like resolution time and user satisfaction will tell you whether it's working or not.
Choosing the foundational model (e.g. GPT-4, Claude)
Choosing a foundational model is like choosing the engine for your car—it determines what it can do. Today, we have options like Claude Opus 4 and GPT-4, which work well for autonomous agents.
The Claude Opus 4, for example, can work autonomously for up to 7 hours according to Anthropic – practically a self-employment for up to 7 hours full schedule14This is impressive, but also a little scary when we think about the implications.
When choosing your model, consider:
- Context processing capability (some models can process up to 150,000 words)
- Quality of reasoning and decision-making
- Specific skills (such as programming, document analysis)
- Operating cost per token
Configuring specific tools via MCP
The Model Context Protocol (MCP) is like a “USB-C port for AI applications”15 – allows agents to connect to external systems in a standardized way. This capability is essential for truly functional implementations.
With MCP, your agent can access:
- Corporate APIs and legacy systems
- Specific knowledge bases
- Search and research tools
- Services like Google Calendar or Slack
To configure MCP, you define which tools the agent will need and use the appropriate syntax to connect it. For example, to access repositories on GitHub: MCPTool(name="GitHub MCP", url="https://api.github.com/mcp")16.
Practical example: Resume screening agent
I'll tell you about a real-life case study that works: automated resume screening. This type of agent can analyze hundreds of resumes in seconds, matching keywords, technical skills, and professional history with the job requirements.17.
The process works like this:
- You set clear goals (reduce time to hire, find better candidates)
- Prepares clean and organized data to train the model
- Choose a suitable template for document analysis (Claude processes large PDFs well)
- Configure integrations with HR and ATS systems
- Test the agent in a controlled environment before using it for real
The result? The agent identifies patterns, compares candidates to job criteria, and automatically ranks them, freeing the HR team for more strategic work.17. But it's important to remember that it still needs human oversight for final decisions.
Multi-Agent Workflow Orchestration with Control and Scalability
Image Source: Microsoft Learn
Here we come to an interesting point: when you have multiple AI agents working together, things get much more complicated. Systems that rely on a single agent with access to multiple tools often can't handle the complexity of modern workflows.
It's like trying to do everything yourself instead of having a dedicated team. It might work for simple tasks, but when things get complicated, you need different people handling different parts of the problem.
Flow design with specialized agents
Implementing multiple agents allows you to divide complex problems into specialized work units. Each agent can focus on a specific domain or resource, significantly reducing complexity of the code and the prompt18This approach mirrors strategies found in human teamwork.
I realize this works exactly like a work team. You have the sales specialist, the finance specialist, the logistics specialist. Each person does what they do best.
Agentic workflows are built on intelligent automation, enabling AI-driven automated processes that are secure and regulated.11However, designing these flows requires considering how components such as RPA, NLP, AI agents, and integrations will work together to create dynamic processes.
Using orchestrator agents and task agents
Orchestrator agents act as conductors, coordinating multiple systems to achieve complex goals. They use predefined rules, hierarchical structures, or real-time data to delegate tasks to specialized agents.19.
But the question remains: how do you decide whether you want a boss coordinating everything or whether you prefer to let agents work more independently?
The system architecture can be vertical, with a “driver” model that supervises other, simpler agents (ideal for sequential flows), or horizontal, with agents working in a decentralized manner (more flexible, but potentially slower).12. The choice depends on the specific needs of the application.
Meanwhile, task agents focus on specific functions like data analysis, document processing, or API interaction. It's a very clear division of responsibilities.
Parallel vs. Sequential Task Execution
There are two main execution patterns you need to understand:
Sequential orchestration: Chains agents in a predefined linear order, each processing the output of the previous one, creating a pipeline of transformations. Ideal for step-by-step processing with clear dependencies.18It's like an assembly line – each step builds on the previous one.
Simultaneous orchestration: Runs multiple agents simultaneously on the same task, enabling independent analysis from unique perspectives. Reduces execution time and provides comprehensive problem coverage.18Here, several agents work in parallel, each with their own perspective.
The fan-out/fan-in pattern demonstrates how complex problems can be broken down, solved in parallel, and then combined into a single result, improving efficiency and quality.20It's fascinating to see how this works in practice.
Preview and testing in a sandbox environment
You sandbox environments are essential to safely test agentic implementations before full deployment. These controlled environments offer regulatory flexibility and legal certainty to experiment with innovations.21.
I've seen many people skip this step and regret it later. Sandbox testing is like rehearsing before the final presentation.
In the sandbox, administrators can run sample interactions to evaluate agent responses, verify cited sources, and test how interactions would change if instructions were changed.22The RESMA (Regulatory Sandbox Maturity Assessment) methodology helps identify flaws and test the maturity of the sandbox environment.21.
A clear workflow visualization facilitates adjustments and optimizations, ensuring agents function as expected before deployment to a production environment. It's better to err on the side of caution than to discover problems when everything is already working for users.
Tools and Technologies for Practical Implementation
Today, we have an impressive array of tools at our disposal for creating AI agents. What strikes me most is how accessible these technologies have become to people who don't know how to code.
No-code platforms for creating agents
Have you ever stopped to think about how much the world has changed? Not long ago, creating an intelligent system required years of programming study. Today, no-code and low-code platforms emerged as a response to the growing demand for software development and the shortage of specialized professionals23It's impressive how these tools democratize technology, allowing users without in-depth technical knowledge to create their own AI agents.
Among the options that stand out the most are:
- Dante – Ideal for beginners, with an easy interface and ready-made settings24
- Chatvolt – Uses advanced models like ChatGPT and 39 other LLMs to reduce operational costs24
- Dify – Focused on predictive analysis and process optimization24
- Synthflow – Specialized in natural interactions with users24
Other notable tools include Lang Chain, which lets you build chatbots without code, and OpenAI's recent Agent Builder, which offers a no-code platform with an intuitive visual interface.25.
But it makes me wonder: are we ready for a world where anyone can create AI agents? It's an interesting question.
Integration with APIs and legacy systems
The integration of AI agents with existing enterprise systems has been a true game-changer for intelligent automation. This connectivity allows access to diverse organizational data sources, from CRM and ERP systems to HR tools.26.
Platforms like IBM webMethods Hybrid Integration offer comprehensive development, deployment, and monitoring of diverse integration patterns.27. Furthermore, iPaaS (Integration Platform as a Service) emerges as an efficient solution for connecting agentic AI to data and systems with little code, providing comprehensive connectivity and API management.28.
Google Cloud's Vertex AI features a complete set of tools for training, building, and deploying models, including pre-trained APIs and MLOps tools to manage the entire machine learning lifecycle.8.
Use of generic and specific tools (e.g. RAG, OCR)
One of the techniques that has impressed me most is Retrieval-Augmented Generation (RAG). This tool is essential for AI agents because it optimizes the output of language models by referencing reliable knowledge bases outside of the training sources.29.
The benefits of RAG include:
- Cost-effective implementation without the need to retrain models
- Access to updated information
- Increased user trust through source attribution29
For advanced implementation, frameworks such as LangChain, LlamaIndex, and LangGraph allow you to create agentic RAG systems with different types of specialized agents: routing, query planning, ReAct, and planning and execution30.
Connectivity via Model Context Protocol (MCP)
The Model Context Protocol (MCP) represents a truly significant step in the integration of agentic AI. Serving as a standardized way to provide information to language models, the MCP allows AI programs to go beyond their initial training by incorporating new sources of information.31.
Developed by Anthropic and later made open source, MCP quickly became an industry standard. It adopts a client-server architecture, where the AI agent (client) sends requests to servers, which respond.31.
This standardization is interesting because it transforms a problem of multiple custom integrations into a scalable solution, similar to what the Language Server Protocol did in software development.32. In March 2025, both OpenAI and Google announced support for MCP, confirming it as an emerging standard for agentic AI.33.
It's fascinating to see how these technologies are shaping the future of business, but it also makes us wonder about the long-term impacts of all this accessibility.
Challenges and Considerations in Implementing Agentic AI
Here we come to an important part that we all need to face in practice. Implementing agentic AI isn't just about advanced technology and endless possibilities—there are real obstacles that can make things much more complicated if we're not prepared.
Data Governance and Compliance
It may seem strange, but one of the biggest problems isn't the technology itself, but the data we feed these agents. According to recent research, 46% of Brazilian leaders fear that their database is not prepared to take advantage of the full potential of the technology, while 47% claim that it does not contain the essential safeguards to ensure safety in the use of AI34.
Think about it: if you feed an autonomous agent incomplete or biased data, it will make decisions based on that problematic information. And worse still, it will do so autonomously, without stopping to question whether it's right or wrong.35.
It's no exaggeration to say that data governance has become a central element in the context of agentic AI. Organizations need to establish robust compliance frameworks not only to comply with regulations but also to ensure that agents operate responsibly.
Performance monitoring and cost per token
Everyone gets excited about the capabilities of AI agents, but few stop to consider the real cost of this technology. Each interaction consumes "tokens"—basic units of text processing that include both the prompts you send and the responses generated.36.
The calculation is pretty straightforward: (Entry Tokens/1M) × Entry Price + (Exit Tokens/1M) × Exit Price36It sounds simple, but when you have agents working 24/7, constantly making decisions and processing information, these costs can add up quickly.
That's why monitoring tools are essential for visualizing token usage, performance, and costs by model. Without this monitoring, you could be in for unpleasant surprises at the end of the month.
Technical limitations and integration with legacy systems
The reality is that most companies still work with old systems that were not designed to communicate with modern technologies.37It's like trying to connect a modern smartphone to a 90s television – theoretically possible, but a lot of work.
Solutions like APIs and middleware help bridge the gap between the old and the new, but it's still not a simple process. A phased approach, prioritizing key areas, minimizes risk and costs, but still requires careful planning and considerable investment.38.
Corporate security and authentication requirements
This is a problem that grows with agents' autonomy. The more independent they become, the greater the security concerns. Measures such as encryption and access controls are essential to protect confidential information.26.
And there's an even more worrying issue: adversarial attacks, where criminals develop their own AIs to bypass defense models.39It's a technology race where both sides are constantly evolving.
As one expert put it: “For every single thing agents do, you need to know exactly what’s happening and be able to track and control it.”35Transparency and traceability of actions become fundamental – we cannot simply trust that the agent is doing everything right without being able to verify it.
Conclusion
We've reached the end of this guide on agentic AI, but we're really just beginning to understand what this technology could mean for us. During our conversation here, we saw how these agents are different from what we're used to—they make decisions on their own, learn from what they do, and can work autonomously.
When implementing your own solution, remember the steps we discussed: clearly define what you want the agent to do, choose the right model for your needs, configure the tools via MCP, and test extensively before putting everything into action.
Orchestrating multiple agents working together can really make a difference. It's like having a team where each person has their own specialty—some handle specific tasks, others coordinate the entire effort. This makes processes more efficient and accurate.
What impresses me is that today we have several tools that greatly facilitate this implementation. Platforms like Dante and Chatvolt allow people without deep technical knowledge to create their own agents. This democratizes access to this technology in a way we haven't seen before.
However, we cannot ignore the serious challenges that exist. Issues such as data governance, cost monitoring, technical limitations, and security require careful attention. It is crucial to know exactly what agents are doing and to have control over their actions.
The question remains: how far do we want this automation to go? As agentic AI continues to evolve rapidly, companies that start using this technology now will reap significant benefits. They'll be able to streamline their operations, reduce costs, and deliver better experiences for customers and employees.
But it's important to remember that, ultimately, we're dealing with systems that, however advanced, don't have a life of their own. They are powerful tools, yes, but tools nonetheless. And like any tool, what really matters is how we choose to use them and for what purpose.
Key Takeaways
This guide presents practical strategies for implementing agentic AI and creating personalized workflows that transform business operations:
• Set specific and measurable goals before building your agent – without clear goals, AI will not deliver meaningful results for your business.
• Use no-code platforms like Dante and Chatvolt to democratize the creation of agents, allowing implementation even without advanced technical knowledge.
• Implement multi-agent orchestration with specialized agents working together – this approach reduces complexity and increases operational efficiency.
• Continuously monitor costs per token and performance – each interaction consumes financial resources that need to be optimized for economic viability.
• Prioritize data governance and security from the beginning – 46% of Brazilian leaders fear that their databases are not ready for agentic AI.
Agentic AI represents a fundamental shift from reactive automation to proactive systems that make autonomous decisions. With the market projected to grow from US$1.5 billion in 2024 to US$1.5 billion by 2030, companies that implement these solutions now will gain significant competitive advantages in process optimization and operational cost reduction.
References
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