Agentic AI and governance: Learn how to make strategic decisions.

The Agentic AI represents a significant milestone in the development of artificial intelligence, elevating the capability of machines from merely responding to commands to acting autonomously, making decisions based on real-time data. This evolution allows AI systems not only to execute predefined tasks but also to dynamically adapt to new challenges and environments, expanding their applicability across various sectors.

According to Gartner consultancy, Agentic AI has the potential to enhance current Artificial Intelligence, and by 2028 it is possible that it will be present in 33% of corporate software, a significant increase from the current 1%. Furthermore, Sequoia Capital estimates that the market associated with Agentic AI could reach $10 trillion, highlighting the economic relevance of this emerging technology.

Large companies are already incorporating Agentic AI into their operations to automate complex tasks and improve efficiency, and the growing adoption of Agentic AI signals a transformation in how companies operate, enabling advanced automation, real-time decision-making, and smarter interactions with customers and internal processes. 

In this article, we explore the concept of Agentic AI, its key differences from traditional artificial intelligence, its practical application possibilities, and the challenges involved in its implementation.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems designed to operate autonomously, with the ability to make independent decisions, plan actions, and achieve specific objectives.

These agents can understand an environment, evaluate different courses of action, and execute strategies independently, without relying directly on continuous human commands, this autonomy makes them useful for solving complex problems, in addition to increasing efficiency and precision in critical tasks such as medical diagnostics and natural disaster response. 

However, responsible and regulated implementation of this technology is essential to ensure it brings more benefits than risks.

What are the differences between Agentic AI and traditional AI systems?

The primary difference between Agentic AI and traditional artificial intelligence systems lies in the degree of autonomy, proactivity, and adaptability of the agents. While the benefits of traditional artificial intelligence are generally designed to solve specific tasks within a set of predetermined parameters, Agentic AI goes further, allowing the system to be more independent and adaptable.

Let’s explore the main distinctions:

Autonomy

Traditional AI Systems:

• Require continuous human supervision or interaction.
• Operate within clearly defined limits and are highly dependent on external inputs to perform actions.
• Examples: A chatbot that responds only to specific commands or a recommendation AI that suggests products based on historical data.

Agentic AI:

• Acts independently, without relying on direct instructions after its initial setup.
• Can make decisions based on general objectives and contextual cues, without the need for constant human supervision.
• Example: An independent drone that adjusts its rotation in real-time to avoid obstacles and carry out a mission.

Goals and Proactivity

Traditional AI Systems:

• Are specific to executing particular tasks, usually explicitly defined by programmers.
• Do not have the capacity to independently define or pursue new goals.
• Example: A medical diagnostic AI that identifies diseases solely based on provided data.

Agentic AI:

• Capable of setting sub-goals to achieve a primary goal more efficiently.
• Can plan, anticipate problems, and adjust its strategy to achieve results.
• Example: An intelligent personal assistant that manages the user’s schedule, suggests adjustments based on priorities, and even autonomously reschedules appointments.

Adaptation and Context

Traditional AI Systems:

• Operate based on rigid algorithms or machine learning driven by fixed data.
• Their capacity for adaptation is limited to manually made updates or additional training.
• Example: An automatic translation system that relies exclusively on previously trained models, without adapting to new turns of phrase or language changes.

Agentic AI:

• Learn and adapt continuously based on interactions with the environment or users.
• Evaluates new contexts and responds flexibly, even in situations for which it was not explicitly trained.
• Example: A robot on a production line that automatically adjusts its operation to compensate for failures in adjacent equipment.

Decision-Making

Traditional AI Systems:

• Follow rules or patterns defined by humans.
• Do not have the capacity to evaluate multiple options and make strategic decisions independently.
• Example: A traffic control system that works based on fixed rules to adjust traffic lights.

Agentic AI:

• Evaluates multiple scenarios, weighs risks and benefits, and chooses the best action to achieve its objectives.
• Example: An independent car that decides between various routes to avoid congestion or minimize travel time.

How does Agentic AI transform governance in companies?

Agentic AI transforms corporate governance by offering practical solutions to challenges ranging from customer support to risk management and operational optimization, these autonomous tools provide greater efficiency, innovation, and competitiveness in the sector.

Companies that adopt Agentic AI are better positioned to face the complexities of an ever-changing global and digital market. See practically how Agentic AI transforms processes within companies:

Automation and Agility

A software development company, for example, can utilize Agentic AI to prioritize tasks in the backlog. The system evaluates the impacts of each feature, deadlines, and available resources, automatically reordering priorities based on strategic objectives and user feedback. As a result, a decrease in delays and dynamic alignment between development teams and the strategic roadmap is expected.

Risk Management

There is an increasing need to manage complex IT infrastructures that require continuous oversight to prevent failures, cyber attacks, and security protection, Agentic AI acts as an independent “bodyguard”, meaning it can monitor servers. The system identifies anomalies in real time, such as suspicious traffic or irregular resource usage, and takes immediate action, such as redistributing loads or isolating affected systems, resulting in fewer outages in services and unexpected responses to cyber threats.

Supply Chain Optimization

For companies that deal with the manufacturing of hardware, such as chips or electronic devices, or that require supply chain management, Agentic AI is a strategic resource. A semiconductor manufacturer uses Agentic AI to predict delays in raw material deliveries and automatically adjust production schedules. The AI also negotiates with suppliers for faster or more economical alternatives, enabling a reduction in production bottlenecks and meeting deadlines more efficiently.

Improvement in Customer Support

Companies that rely on agile customer support to resolve technical problems and retain users can replace traditional chatbot systems with independent and proactive agents using Agentic AI. For example, a software as a service (SaaS) platform implements an Agentic AI assistant in technical support. The agent not only answers questions but also resolves issues directly, such as reconfiguring accounts or updating plans without human intervention.

Data Governance and Compliance

There are companies that deal with large volumes of data and need to comply with protection regulations, such as GDPR and LGPD. Agentic AI can take over the governance of this data by mapping and classifying data in automation format, ensuring regulatory compliance and responding to user data deletion requests. In other words, there is less risk of privacy fines and guaranteed user trust.

How does Agentic AI drive the next generation of independent agents?

Agentic AI is leading the development of the next generation of independent agents designed to operate effectively and adaptively in sound environments, let’s understand how this technology is shaping the future of artificial intelligence.

Continuous Learning and Adaptation

Independent agents powered by Agentic AI have the capacity to continuously learn from experiences, real-time data, and changes in the environment.

How this happens:

• We use advanced neural networks and reinforcement learning algorithms that allow for continuous improvement.

Impact:

• An autonomous agent in a video game learns and adapts its combat strategies based on human player behavior, making the experience more engaging.

Interoperability and Multidisciplinarity

The next generation of independent agents will not be limited to a single domain but will be multidisciplinary and capable of operating in different contexts.

How this happens:

• Agentic AI allows an agent to be trained to apply transferable skills across tasks and sectors.

Impact:

• An Agentic AI designed to optimize public transport networks in a city can also be adapted to manage logistics for e-commerce companies.

Use of Sensors and IoT Connectivity

Integration with sensors and IoT (Internet of Things) devices boosts independent agents, allowing them to understand and interact with the physical world more effectively.

How this happens:

• Agentic AI processes real-time data from sensors, such as cameras, radars, or temperature monitors, to make instantaneous decisions.

Impact:

• Example: An independent car uses sensors to monitor the surrounding environment, and AI adjusts its direction, speed, and route without human intervention.

Scalable and Customizable Solutions

Agentic AI allows independent agents to be scalable for different contexts and easily customizable to meet specific needs.

How this happens:

• The modular design of agents allows new functions or capabilities to be integrated without redesigning the entire system.

Impact:

• Example: An Agentic AI for energy management in buildings can be adapted to monitor electrical networks in entire cities.

Focus on Sustainability

Independent agents based on Agentic AI are a key piece in the search for sustainable solutions, allowing optimizations that reduce resource consumption and carbon emissions.

How this happens:

• Private agents analyze environmental data and implement real-time adjustments to minimize ecological impacts.

Impact:

• Example: In an automated farm, an independent agent adjusts supervision based on climatic conditions and soil moisture levels, saving water and optimizing production.

Ethical Automation and Governance

Agentic AI drives independent agents that operate with a focus on ethical principles and regulations, ensuring transparency and accountability.

How this happens:

• Agents are programmed to follow ethical guidelines and adjust their actions to avoid harm and promote human well-being.

Impact:

• Example: In financial systems, an agent avoids risky decisions that could harm clients, even if it maximizes profits in the short term.

What are the challenges in implementing Agentic AI systems?

The implementation of Agentic AI systems presents significant technical, ethical, and organizational challenges. Although these technologies bring impressive advancements, their autonomous, adaptable, and proactive nature creates complexities that must be addressed to ensure their effectiveness, safety, and alignment with human values. 

Here are the main challenges:

Technical Complexity

Building Agentic AI systems requires advanced infrastructure, robust algorithms, and integration with various existing systems, with key challenges including:

• Continuous Learning: Developing models that can learn and adapt without frequent human supervision, without introducing biases or errors over time.
• Real-Time Processing: Ensuring that systems process data and make instant decisions in sound environments.
• Interoperability: Integrating independent agents with legacy systems and heterogeneous technologies in organizations.

Security and Reliability

Independent systems need to be reliable under normal and unforeseen conditions, without compromising security. Hence, it is necessary to be aware of the following specific challenges: 

• Unforeseen Failures: Identifying and mitigating scenarios where AI may make decisive or unexpected decisions.
• Cyber Attacks: Ensuring agents are not vulnerable to hackers who may manipulate or exploit them for malicious purposes.
• Resilience: Creating systems that can continue operating or recover after failures.

Ethics and Responsibility

The autonomy of Agentic AI systems raises ethical and practical questions about control and responsibility, such as:

• Value Alignment: Ensuring that agents’ decisions align with human values and organizational goals.
• Legal Responsibility: Determining who is responsible for an independent agent’s actions, especially in cases of error or harm.
• Moral Decision-Making: Avoiding allowing AI to make decisions with complex ethical implications without human supervision.

Cost and Scalability

Creating and maintaining Agentic AI systems may require significant investments, especially at the outset, including:

• Initial Costs: Expenses related to development, infrastructure, and system integration.
• Continuous Maintenance: Ensuring that agents continue to operate efficiently and without failures after implementation.
• Scalability: Making systems accessible for smaller companies or different contexts.

Transparency and Explainability

The autonomous decision-making of Agentic AI systems needs to be specific to humans, especially in regulated sectors, with the main points of attention being: 

• Black Box: Many AI systems operate as “black boxes,” making it difficult to understand how a decision was reached.
• Justifications: Creating mechanisms for agents to explain their actions clearly and accessibly.
• User Trust: Gaining the trust of users and stakeholders through transparency and explainability.

How can companies integrate Agentic AI into their existing systems?

Integrating Agentic AI into existing systems requires careful planning, from adapting infrastructure to defining clear objectives and monitoring processes. With a focus on contextual autonomy, efficient connectivity, and continuous learning, companies can leverage the full potential of this technology while maintaining control and strategic alignment.

Here are the main steps:

Identify Areas for Autonomy Application

Agentic AI is especially useful in processes that require independent decision-making and adaptability.

Build Communication Interfaces

As Agentic AI operates as an “independent agent,” it needs to integrate with other systems via APIs and middleware, so developing APIs that connect the Agent AI to the company’s ERP (Enterprise Resource Planning) or CRM is necessary, allowing it to access operational data and make specific decisions within them.

Training with Real and Contextual Data

Agentic AI needs to learn from the company’s operational context to act effectively; for this, use the company’s historical data, such as sales records, performance analyses, or usage patterns, to train AI models.

Set Goals and Constraints

The autonomy of Agentic AI should be guided by clear goals and pre-defined limits, so it’s necessary to configure parameters that limit the AI in critical decisions, for example, in financial systems, AI can be configured not to perform transactions above a certain amount without human execution.

Adapt Systems for Real-Time Reaction

Agentic AI is designed to act in real-time, but this may require adjustments in existing technological architecture; it is necessary to upgrade infrastructure to support low-latency communication and real-time data processing capabilities.

Pilot Tests Focused on Scalability

Start with localized implementations to validate the impact before expanding; therefore, test Agentic AI in a single department or project to evaluate its performance before broadening its application.

Having a specialized partner in integrating technologies like Agentic AI is essential for companies looking to explore emerging trends safely and effectively. 

Objective brings technical expertise and strategic vision, helping organizations identify the best applications for their contexts, ensure regulatory compliance, and minimize risks during implementation. Additionally, a planned partnership allows companies to stay updated with technological evolution and seize new innovation opportunities, maintaining competitiveness in a constantly changing market. Contact our specialists and learn how we can help you.