Agentic AI is revolutionizing the way businesses operate. This technology learns, adapts, and acts independently. It predicts needs, makes decisions, and solves problems proactively, without waiting for human input.
Such capabilities are transforming industries. They enable companies to respond to challenges in real time and stay ahead of the competition.
To unlock the full potential of this technology, it's important to understand the concept of agency in AI. Mastering this understanding is the first step toward driving innovation and growth.
Understanding Agency
Agency, in the context of human behavior, refers to the capacity of individuals to act independently and make choices. It's the ability to initiate and carry out actions and to exert control over one's environment. Agentic individuals possess a sense of autonomy, self-efficacy, and the belief that they can influence their own outcomes.
In simple terms, agency allows AI systems to make independent decisions to achieve predefined objectives, much like a human manager who has the authority and insight to handle tasks autonomously because of adaptation and contextual superiority. Understanding agency AI for business is crucial, as it highlights how these systems can drive strategic value and innovation.
Agency empowers these systems with capabilities beyond mere automation. Agents can interpret data, learn from patterns, and adapt to new conditions, all while aligning with business goals.
In sectors like supply chain management, customer service, and finance, agency in agentic AI creates real value by enabling faster, more intelligent responses to complex scenarios.
The Core Components of Agentic Capabilities
Agentic capabilities are the psychological and behavioral traits that define agency. These include:
- Self-Efficacy: The ability to develop confidence. For agentic systems, this means improving problem-solving and decision-making skills.
- Autonomy: The ability to act independently. In agentic systems, this is the control they have over their actions.
- Proactive Behavior: Taking initiative and seeking opportunities. Agentic systems must be forward-thinking, planning ahead.
- Resilience: The ability to recover from setbacks. Agentic systems must adapt to obstacles and continue moving forward.
Let’s look at a technical product organization. Initially, the focus is on developing self-efficacy within teams. This allows them to grow and make decisions on their own. As experience and decision-making powers increase, the teams become autonomous. They gain the freedom to choose and execute their plans.
The organization shifts from reactive to proactive. It becomes an initiator, addressing challenges and creating value. When setbacks occur, the organization handles them through risk management and evaluations. Over time, resilience builds.
Once the organization has moved from self-efficacy to resilience, it can act as an agency between different organizations. ISRO and NASA are prime examples of this transformation. They started with independent decision-making and evolved into agencies capable of leading complex projects.
Researchers suggest that agentic AI systems follow a similar path, evolving from simple autonomy to fully realized agency.
Agency Capability in AI
In the world of artificial intelligence, agency capability refers to an AI system's ability to act autonomously and make decisions based on its understanding of its environment and goals. It implies a level of self-direction and initiative that goes beyond simple task execution.
Some of key characteristics of agentic AI are:
- Self-Initiation: The AI system can independently initiate actions without explicit human commands.
- Goal-Oriented Behavior: It has the ability to set and pursue specific goals.
- Learning and Adaptation: It can learn from experiences and adapt its behavior to changing circumstances.
- Decision-Making: It can make choices and decisions based on information and reasoning.
- Environmental Interaction: It can interact with its environment, both physical and digital, and respond to changes.
Now we need to dive further into the blocks of agentic AI systems that constitute different functions and systems of messages integrated together. The messages here are not limited to the data or information but also the context that they carry.
Function calling serves as the building block for more complex AI behaviors, including agency. It provides a structured way to break down complex tasks into smaller, manageable steps. By combining multiple function calls, AI systems can execute sequences of actions, make decisions, and respond to their environment.
However, while function calling is essential for basic AI operations, it doesn't fully capture the essence of human-like intelligence. Agentic AI aims to bridge this gap by empowering AI systems to act autonomously and make decisions based on their understanding of the world. This shift from reactive to proactive behavior requires AI systems to go beyond simple function calls.
Function Calling Vs Agency
Function calling is a fundamental programming concept where specific code is executed to perform a task. It's reactive, task-specific, and controlled by the calling program or user. For instance, calling a function to calculate a square root is a simple example of function calling.
Agency capability, on the other hand, is a more advanced concept where AI systems can act autonomously and make decisions based on their understanding of their environment and goals. These systems are proactive, goal-oriented, adaptive, and capable of making decisions and interacting with their surroundings. An AI agent exploring a virtual world and learning from its interactions is a prime example of agency capability.
The key differences are:
- Initiation: Function calling is reactive, while agency capability is proactive.
- Goal Orientation: Function calling is task-specific, while agency capability is goal-oriented.
- Learning and Adaptation: Function calling has limited learning, while agency capability involves significant learning and adaptation.
Many opinions and reviews have been published worldwide. They discuss the dimensions of Agency in AI systems, including rationality, freedom of choice, and automaticity.
“Agency is here seen as the capability of machines to act autonomously, but on behalf of humans, organizations, and institutions.” – (Ågerfalk, 2020).
Currently, AI algorithms have agency in rationality, freedom of choice, and endorsements but this is still evolving at the system level. For example, a fraud detection module that predicts fraud using transactional and unstructured data is allowed to make a choice based on the learning and adaptations in weights. The algorithm designer or system owner does not intervene in the process.
An interesting point that differentiates Agency from function calling or Task-based RL implementation is a contextual understanding of the task.
Contextualization in Agentic AI
The concept of contextualization in AI reminds us that information systems are always embedded in practical, real-world environments and that the meaning and use of technology vary by context. Rather than treating AI as a mysterious, one-size-fits-all solution, contextualization emphasizes understanding the specific conditions and nuances of each use case.
For instance, distinguishing between an order and an invoice in a data set is about understanding the communicative roles and workflows—such as sales vs. accounting—that define these documents in an organization.
This need for contextual interpretation also highlights the importance of agency in AI. AI systems today can act autonomously, yet they operate under human-designed boundaries. Digital agency refers to AI’s capacity to perform symbolic actions on behalf of human organizations without requiring full consciousness.
This agency is crucial in maintaining the accountability and transparency of AI, as it ensures that AI systems not only make decisions but also do so in ways that respect social and institutional frameworks. Contextual understanding also strengthens the explainable AI in the organization. AI agents operating with a contextual understanding of the data will operate in a more regulated and ethical manner.
An Example of an AI Agent With Agency Capability
Let’s imagine a scenario where an automated agent is in charge of forecasting demand and reordering products to keep a retail chain's shelves stocked at all times. This agent operates with something called agency capability, meaning it can act independently to make key decisions in real time, all in pursuit of one goal: ensuring the right products are available when needed.
So, how does this agent manage such a complex task? Here’s a step-by-step breakdown:
Step 1: Data Collection and Sensing
To make accurate decisions, the agent needs to see the full picture of what’s going on in the business and the market. It constantly collects data from various sources, including historical sales, real-time inventory levels, supplier lead times, and even external factors like seasonal trends, promotions, or major events.
By processing all of this information, the agent builds a current and comprehensive view of the demand landscape, ready to respond if any fluctuations occur.
Step 2: Demand Forecasting
Once it has the data, the agent uses machine learning models to predict future demand. Think of it as the agent’s way of “seeing into the future” based on past and present patterns. If it anticipates a demand surge for a certain item—let’s say sunscreen as summer approaches—it can plan accordingly by increasing order volumes.
Similarly, if demand is predicted to drop, the agent might scale back orders to avoid excess stock that could become costly to store.
Step 3: Decision-Making and Optimization
The agent now evaluates its demand forecasts against specific business rules and constraints, such as budget limits and inventory policies. It’s not just about filling shelves—it’s about doing so in a way that balances costs, maintains efficient stock levels, and meets customer needs.
Using optimization techniques, the agent calculates the ideal reorder quantities, aiming to minimize holding costs (like warehousing) while avoiding expensive last-minute orders to replenish stock.
Step 4: Autonomous Reordering
With the details sorted out, the agent autonomously places purchase orders with the appropriate suppliers. This isn’t a one-size-fits-all process; if any supply chain disruptions come up, the agent is flexible enough to split orders across multiple suppliers or even seek alternative products to fulfill anticipated demand.
This agility helps maintain smooth operations, even in the face of unexpected challenges.
Step 5: Monitoring and Adjustment
Even after placing an order, the agent’s work isn’t done. It tracks the delivery status of incoming products. If the agent senses an issue—say a supplier delay—it may respond by expediting another order or shifting to a backup supplier.
As fresh data flows in, the agent’s forecasting models improve over time. It refines its predictions and adjustments to be even more accurate and timely.
Implications of Evolving Agency of Agentic AI
The ability to operate independently, make decisions, and adapt to changing environments has significant implications for strategic decision-making.
Enhanced Decision Quality
Example: In supply chain management, agents can predict demand spikes and adjust inventory autonomously, allowing companies to strategically avoid stockouts and adapt to seasonal changes.
Scalability and Agility in Operations
Example: E-commerce companies can use autonomous agents to manage global inventories, allowing for rapid expansion into new regions with minimal logistical risk.
Strategic Flexibility and Scenario Planning
Example: An autonomous demand forecasting agent can generate simulations for different economic conditions, helping executives create flexible strategies based on likely future demand scenarios.
Conclusion
Integrating agentic systems with clear agency capabilities gives organizations a strategic edge. These systems leverage real-time, data-driven insights and adapt quickly to changes.
With routine decisions handled autonomously, leaders can shift their focus to high-level strategy and transformative goals. This balance between human leadership and intelligent systems drives efficiency and innovation.
