AI in autonomous agents: when technology resolves conflicts on its own

Introduction

Would you trust artificial intelligence (AI) to resolve a conflict between two systems that don't understand each other? This is a question many companies are asking themselves, and increasingly, answering with a "yes."

In 2024, a global survey by PagerDuty showed that 94% of organizations plan to adopt agentive AI or autonomous agents, faster than traditional generative AI models . More than half of them already see this technology as a strategic priority. This indicates that we are facing a new stage of digital transformation, in which machine autonomy becomes an essential part of the operation.

It's inevitable : as systems multiply and data flows become more complex, conflicts arise between information, business rules, and processes. Divergent information, stalled decisions, or integrations that don't communicate efficiently generate delays, rework, and operational risks .

In this context, autonomous AI agents emerge as an intelligent solution . Unlike traditional automation, these agents analyze the context, interpret variables, and make decisions independently, based on continuous learning.

In this article, we will explore how this technology is being used to resolve digital conflicts more quickly, accurately, and autonomously . Here, you will understand what autonomous agents are, how they work in practice, and how Skyone applies them to resolve conflicts with greater agility and intelligence .

Enjoy your reading!

What are autonomous agents with AI?

The word "autonomous" carries weight. It suggests independence, decision-making, and responsibility. But in the world of technology, what exactly does it mean to give a system autonomy?

Autonomous agents with AI are software programs that can act independently, making decisions based on dynamic contexts, defined objectives, and prior learning. They are not limited to executing programmed commands: they interpret variables, evaluate scenarios, and choose the best possible response in real time.

This logic marks an important turning point in how we understand automation. If before the focus was on efficiency in repetitive tasks, now we talk about applied intelligence to solve complex problems with greater precision and speed . And that changes everything.

The term may seem distant, but the examples are closer than you might imagine: from the virtual assistant that resolves demands without depending on scripts to the logistics system that readjusts routes in the face of unforeseen events. In all these cases, there is a common point: the ability to act without waiting for orders .

Understanding what makes an agent truly autonomous is the first step in differentiating promise from real application. And for that, we need to understand the different types of agents and how they operate with varying levels of intelligence and independence.

Shall we begin?

Types of autonomous agents with AI

Before exploring how these agents work in practice, it's important to understand that not all operate with the same level of intelligence or independence . Autonomy can vary considerably, depending on the ability to perceive the environment, interpret situations, and make adaptive decisions.

The most classic, and still very useful, way to classify these agents was proposed by Stuart Russell and Peter Norvig in the book " Artificial Intelligence: A Modern Approach " originally published in 1995. This work, constantly updated and widely used in universities and technology companies, defines the types of agents based on their degree of sophistication and autonomy . Even almost three decades later, this framework remains a solid reference for those seeking clarity and strategic vision regarding the role of AI in business.

That said, here are the main types of autonomous AI agents:

• Simple reflex agents : react to direct stimuli with predefined actions. For example, a system that automatically responds to a keyword in an email . There is no analysis, only an immediate response;
  
• Agents with limited memory : they use recent data to make more informed decisions. A chatbot that remembers the last question asked to maintain the context of the conversation falls into this category;
  
• Goal-oriented agents : make decisions guided by goals. A logistics system that reorganizes deliveries to avoid delays operates with this type of logic, even if it needs to change the original plan;
  
• Utility-based agents : evaluate different options to choose the most advantageous one. A recommendation agent that considers the customer's history and conversion potential before suggesting an offer is a good example;
  
• Multi-agent systems (MAS) : operate in a network, with multiple agents interacting with each other, whether to cooperate, compete, or negotiate decisions. MAS stands for Multi-Agent Systems , or systems composed of multiple agents that, even with distinct objectives, act in a coordinated manner. This model is common in corporate platforms that integrate areas such as customer service, logistics, and sales, seeking optimized decisions in real time.

These types are not rigid blocks. The same agent can evolve over time, gaining complexity as it collects data, interacts with users, and learns from its own decisions. Thus, understanding these categories is important to recognize how and where autonomous AI can be applied safely and with impact.

In the next section, we will learn how these agents operate: how they perceive the environment, interpret variables, and make decisions that previously required human intervention.

How they work: from input to decision

Now that we understand the main types of agents, it's time to open the "black box" and look inside: how do they actually operate?

At first glance, the functioning of an autonomous AI agent may seem complex, but it becomes more accessible when divided into three fundamental stages : perceive, interpret, and act.

These stages form the life cycle of an autonomous decision . It is from these stages that an agent can transform data into decisions, often with the agility and precision that human routine could not maintain on a large scale.

Let's take a closer look at each of these phases.

Perception

It all starts with data input . Autonomous agents are "sensitive" to the environment, meaning they capture information arriving through different channels —APIs, sensors, legacy systems, cloud integrations, and/or native connectors, such as those used in Skyone Studio .

This step is key because without reliable and well-connected data, it's impossible to make intelligent decisions. The quality of perception directly impacts the agent's performance , which is why data architecture and integration points are so relevant in the design of these systems.

Interpretation with AI

Once the data is collected, the agent now needs to understand it . This is where artificial intelligence comes in, especially pluggable models like LLMs ( Large Language Models ), which help the agent interpret context, detect conflicts, and evaluate variables.

Instead of following fixed rules, the agent is able to compare scenarios, analyze patterns, consider exceptions, and even mediate decisions based on multiple sources. This is what differentiates an autonomous agent from traditional automation : it not only executes, but also interprets.

Decision and action

With the data understood, it's time for the agent to choose the best course of action . It can correct a discrepancy between systems, prioritize a specific flow, alert a team, or simply take action on its own. Of course, always guided by clear and, ideally, auditable objectives.

At the end of this process, everything the agent did can and should be recorded. This traceability allows it to evolve based on its own results , creating a cycle of continuous improvement. Interestingly, in Skyone Studio , for example, logs lakehouse architecture help maintain this rich and accessible history for future reassessment.

After understanding the step-by-step process of how an autonomous agent perceives, interprets, and decides, it's time to move beyond theory . In the next section, we'll show how all this translates into real-world applications, and how these agents are already operating in scenarios where complexity demands faster, more accurate, and intelligent responses.

Real-world examples of application

However sophisticated the concepts behind AI may be, its value is proven in practical application. Autonomous agents are already in action in various corporate contexts , often invisibly, but operating at critical points to ensure fluidity, precision, and operational continuity.

Here are some concrete examples:

• Customer service with multiple integrations : AI-powered autonomous agents can simultaneously access different systems (such as CRM, order database, and support center) to identify inconsistencies and resolve information conflicts.
  If status differs between platforms, the agent analyzes the history, determines the most reliable version, and updates the records, without the need for human escalation.
  
• Automatic correction of integration errors : In environments with many legacy systems, it is common for data to circulate in different formats.
  An agent can act as a mediator: when it detects an incompatibility between systems, it identifies the source of the problem, applies the necessary transformation, and resends the data in a standardized way, keeping the integration active and reliable.
  
• Reconciliation of financial and operational data : Companies with multiple data sources frequently face discrepancies in values ​​and records.
  AI-powered agents can cross-reference these databases, detect anomalies, and apply decision rules (such as prioritizing sources with lower error rates) to suggest or implement corrections. This speeds up processes such as accounting closing and internal audits.
  
• Preventive monitoring and fault self-resolution : agents can track logs and events in real time to identify patterns that precede technical failures. By recognizing these signs, they can trigger preventive measures, such as restarting flows, isolating processes, or alerting teams with accurate diagnoses. This prevents interruptions before the problem even manifests on the front-end .

These examples show that autonomous agents are already helping companies resolve conflicts before they even become problems , with precision, agility, and scale. But no technological autonomy is neutral. For these systems to act with true intelligence, it is necessary to ensure that they operate responsibly.

Therefore, below, we delve into the pillars that underpin this trust: ethics, security, and governance. Because technology without criteria doesn't solve problems, it compromises them!

What's at stake: ethics, security, and trust

Giving autonomy to a system is, first and foremost, about delegating decisions, and that changes everything.

According to a SailPoint of 353 IT professionals, 98% of organizations plan to expand their use of AI agents in the next 12 months, but 96% already see these agents as a growing security threat . Furthermore, 80% reported undesirable behaviors, such as unauthorized access and improper data sharing, and less than half have formal governance policies to address this.

This data makes it clear that autonomy without structure creates risk . Thus, it is crucial to ensure clarity about who is responsible for each decision, protect sensitive data, and audit the entire process. Otherwise, an agent that performs well today can become a problem tomorrow.

In addition, with agents connected to multiple systems, the attack surface grows . Security requires segregation of flows, access control, and continuous monitoring—not as a final step, but incorporated into the design from the beginning.

At Skyone , we adopt the principle of "trust with security." Therefore, our Skyone Studio comes equipped with logs , granular permission control, and governance that supports both ethics and technical operation.

Next, we want to show you how these elements come together in practice , when we orchestrate agents with AI within Skyone Studio , from construction to continuous evolution!

How does Skyone orchestrate agents with AI?

Autonomy, by itself, is not enough. What transforms autonomous agents into real solutions is orchestration, that is, the ability to coordinate logic, data, and decisions in a secure, auditable, and adaptable environment .

This is what Skyone Studio enables: creating agents that not only execute commands but also understand the context, react to exceptions, and evolve based on their own learnings. All this without requiring a technical revolution on the client's end, but rather by seamlessly connecting the new to the legacy system .

See how we do this in practice.

Creating agents in Skyone Studio using conditional flows

In Skyone Studio , agents are not programmed line by line, but rather architected. The logic is built visually, through conditional flows that outline the agent's behavior in response to events, rules, and exceptions.

This allows for mapping complex scenarios , such as a discrepancy between billing and inventory data, and configuring specific actions : from automatically reconciling data to triggering human approval. In other words, the agent acts as an intelligent mediator, not a passive executor.

Integration with data via native connectors

Acting autonomously requires context, and context requires data. That's why Skyone Studio offers native connectors so agents can access different systems in real time, such as ERPs, CRMs, databases, and proprietary APIs.

These integrations not only feed the agent's logic, but they also allow it to detect conflicts between sources, identify recurring patterns, and make decisions based on what is actually happening, not just what was predicted.

Continuous evolution with centralized logs lakehouse.

A truly intelligent agent isn't born ready: it learns. That's why everything it does is recorded in centralized logs lakehouse . This creates a reliable trail for understanding the past, analyzing the present, and planning the future .

This repository of decisions is what allows agent performance training models based on real-world situations, and refining rules with evidence, not guesswork. It's a cycle of continuous evolution, based on data , as every good decision should be.

If you want to understand how these agents can operate in your scenario, talk to one of our specialists and discover how Skyone Studio connects logic, data, and AI to transform conflicts into intelligent decisions!

Conclusion

Operational conflicts aren't always visible, but their effects are felt every day : mismatched data, stalled integrations, decisions that take longer than they should. In a scenario where complexity grows faster than the human capacity to keep up, relying on systems that autonomously resolve impasses is an increasingly necessary response.

Throughout this content, we've seen that autonomous agents with AI represent more than just advanced automation: they are a new operational logic , capable of understanding context, making decisions, and evolving based on their own learnings. We explored their types, how they work, where they already operate, and how Skyone orchestrates all of this with security and intelligence.

More than a trend, this technology responds to a real demand for greater fluidity, reliability, and scale . And perhaps your operation is already ready to take this next step, with intelligence at the center of everything.

If you want to continue exploring other topics that connect technology and business with depth and clarity, explore the Skyone blog ! There's always something new here that can transform the way you look at your company's operations.

FAQ: Frequently asked questions about AI in autonomous agents

As interest in artificial intelligence (AI)-based solutions grows, so do questions about how this technology works, especially when it comes to agents that make decisions on their own.

To help, below we have compiled answers to some of the most common questions about autonomous AI agents, their uses, and implications.

What is the difference between automation and an autonomous agent?

Automation executes programmed tasks, leaving no room for interpretation. An automation robot repeats instructions without considering changes in context. An autonomous agent, on the other hand, is designed to evaluate scenarios, adapt its response, and even learn from previous decisions. It doesn't just follow rules: it chooses which rule to apply or when to create a new one.

Is it safe to leave decisions in the hands of autonomous agents with AI?

It's possible, provided there is governance. Autonomous agents must operate with traceability, well-defined boundaries, and auditability. Security lies in the design: well-structured workflows, permission controls, and constant monitoring. When well implemented, these agents reduce operational risks instead of creating them.

Can medium-sized companies also use autonomous agents with AI?

Yes, and often these are the companies that benefit the most. Autonomous agents help medium-sized businesses do more with less: avoiding rework, integrating legacy systems, and keeping operations running smoothly with less reliance on human intervention. With accessible and flexible platforms like Skyone Studio , this technology is within reach of those who want to grow intelligently and with control.