Agentic AI

Business Value Creation with IT (BVC)

Andy Weeger

Neu-Ulm University of Applied Sciences

March 10, 2025

Agents

Rational agents

Figure 1: Rational agents interact with environments through sensors and actuators

A rational agent is anything that is

• perceiving its environment through sensors,
• thinking and deciding on the next actions
  (mapping given percepts to actions),
• and acting through actuators

Rational means, that the agent acts in a way that is expected to maximize its performance measure, given it’s

• built-in knowledge (i.e., the agent function¹),
• perceived experience (i.e., the percep sequence²),
• and acting capabilities

Example

To illustrate these ideas Russel and Norvig (2022, 55) use a simple example—the vacuum-cleaner world, which consists of a robotic vacuum-cleaning agent in a world consisting of squares that can be either dirty or clean. The vacuum agent perceives which square it is in and whether there is dirt in the square. The agent starts in square A. The available actions are to move to the right, move to the left, suck up the dirt, or do nothing. One very simple agent function is the following: if the current square is dirty, then suck; otherwise, move to the other square.

Under following circumstances, the vacuum cleaning agent is rational:

• The performance measure of the vacuum cleaner might award one point for each clean square at each time step, over a “lifetime” of 1,000 time steps (to prevent the cleaner to oscillate needlessly back and forth).
• The “geography” of the environment is known a priori but the dirt distribution and the initial location of the agent are not. Clean squares stay clean and sucking cleans the current square. The Right and Left actions move the agent one square except when this would take the agent outside the environment in which case the agent remains where it is.
• The only available action is Right, Left, and Suck.
• The agent correctly perceives its location and whether that location contains dirt.

Exercise

Under which circumstances does a vacuum cleaning agent act rational?

Performance measure

If we use, to achieve our purposes, a mechanical agency with those operation we cannot interfere once we have started it […] we had better be quite sure that the purpose built into the machine is the purpose which we really desire Wiener (1960, 1358)

It is difficult to formulate a performance measure correctly. This is a reason to be careful.

Rationality vs. perfecteion

Rationality is not the same as perfection.

• Rationality maximizes expected performance
• Perfection maximizes actual performance
• Perfection requires omniscience
• Rational choice depends only on the percept sequence to date

As the environment is usually not completely known a priori and completely predictable (or stable), information gathering and learning are important parts of rationality (Russel and Norvig 2022, 59).

Example

The vacuum cleaner needs to explore an initially unknown environment (i.e., exploration) to maximize its expected performance. In addition, a vacuum cleaner that learns to predict where and when additional dirt will appear will do better than one that does not.

Agent types

Simple reflex agents

Figure 2: A simple reflex agent³

Simple reflex agents select actions on the basis of the current percept, ignoring the rest of the percept history. Thus, these agents work only if the correct decision can be made on the basis of just the current percept. The environment needs to be fully observable (Russel and Norvig 2022, 68).

Example: A thermostat makes decisions based solely on the current temperature reading, without considering past temperatures or future predictions.

Model-based reflex agents

Figure 3: A model-based reflex agent

Model-based reflex agents maintain an internal models of the world, which helps them keep track of the current state and make decisions based on this model. This allows them to handle partially observable environments more effectively (Russel and Norvig 2022, 70).

• The transition model reflects how the world evolves (a) independently of the agent and (b) depending on the agent’s actions.
• The sensor model reflects how the state of the world is reflected in the agent’s percepts (i.e., by its sensors).

Eample: A self-driving car uses its transition model to predict the state of the environment reflected in the sensor model and make decisions accordingly.

Types of representation of states

The representations of states can be placed along an axis of increasing complexity and expressive power (Russel and Norvig 2022, 76–77):

• An atomic representation is one in which each state is treated as a black box with not internal structure, meaning the state either does or does not match what you’re looking for. In a sliding tile puzz_le, for instance, you either have the correct alignment of tiles or you do not.
• A factored representation is one in which the states are defined by set of features (e.g., Boolean, real-valued, or one of a fixed set of symbols). In a sliding puzzle, this might be a simple heuristic like “number of tiles out of place”.
• A structured representation is one in which the states are expressed in form of objects and relations between them (e.g., expressed by logic or probability). Such knowledge about relations called facts.

The more expressive language is much more concise, but makes reasoning and learning more complex.

Goal-based agents

Figure 4: A model-based, goal-based agent

Goal-based agents make decisions based on a set of goals they aim to achieve. They consider the current state, possible actions, and the outcomes of these actions to determine the best path to reach their goals (Russel and Norvig 2022, 72).

Goal-based agents often use search and planning algorithms to find the optimal sequence of actions to achieve their goals, best-case considering both immediate and long-term consequences.

Example: A delivery robot is designed to navigate from a starting point to a destination within an environment, often avoiding obstacles and optimizing its route.

Utility-based agents

Figure 5: A model-based, utility-based agent

Utility-based agents make decisions by evaluating the utility (or value) of different possible actions and choosing the one that maximizes their overall utility. These agents consider not only the goals but also the trade-offs and preferences to achieve the best possible outcome (Russel and Norvig 2022, 73).

The utility function is a mathematical function that calculates expected utility for all possible states, weighted by the probability of the outcome. The agent evaluates the utility of different actions and selects the one that maximizes its expected utility.

A utility-based agent has many advantages in terms of flexibility and learning, which are particularly helpful in environments characterized by partial observability and nondeterminism. In addition, there are cases where the goals are insufficient but a utility-based agent can still make rational decisions based on the probabilities and the utilities of the outcomes:

• When there are conflicting goals, the utility function specifies the appropriate tradeoff.
• Likelihood of success (i.e., goal achievement) can be weighed against the importance of the goals

Model- and utility-based agents are difficult to implement. They need to model and keep track of the task environment, which requires ingenious sensors, sophisticated algorithms, and a high computational complexity. There are also utility-based agents that are not model-based. These agents just learn what action is best in a particular situation without any “understanding” of its impact on the environment (e.g., based on reinforcement learning).

Example

A smart thermostat continuously evaluates the utility of different temperature settings and adjusts accordingly to maximize overall utility, balancing comfort and energy savings.

Recap

What are the main differences between the agents?

Learning agents

Figure 6: A learning agent

Learning agents are AI systems designed to improve their performance over time by learning from their environment and experiences. Unlike traditional AI systems that operate with fixed programming, learning agents adapt, evolve, and refine their actions based on feedback and data. Thus, learning agents have greater autonony.

A learning agent consists of four conceptual components (Russel and Norvig 2022, p- 74-75), as shown in Figure 6:

• Learning element: Acquires knowledge and improves performance by analyzing data, interactions, and feedback. Uses techniques such as supervised, unsupervised, and reinforcement learning.
• Performance element: Executes tasks based on the knowledge acquired by the learning element.
• Performance standard or critic: Evaluates the actions taken by the performance element and provides feedback.
• Problem generator: Identifies opportunities for further learning and exploration. Exploratory actions may be suboptimal in the short term, but can lead to the discovery of better actions in the long term.

Rational agents

A rational agent is one
that does the right thing.

Utility-based learning agents are rational agents as they act so as to achieve the best outcome or, when there is uncertainty, the best expected outcome. This means that for each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has, which evolves over time (Russel and Norvig 2022, 58).

Evolution of agents

The evolution of AI agents

 

 

 

Figure 7: The evolution of AI agents

Agentic AI

Definition

Agentic AI is an emerging paradigm in AI that refers to autonomous systems designed to pursue complex goals with minimal human intervention. Acharya, Kuppan, and Divya (2025, 18912)

Core characteristics of Agentic AI are

• Higher autonomy and goal complexity
• Ability to adapt to environmental and situational unpredictabilities
• Independent decision-making

• Autonomy and goal complexity, as agentic AI systems
  • can handle multiple complex goals simultaneously,
  • can operate independently over extended periods,
  • can shift between tasks to achieve higher-order objectives, and
  • makes decisions with minimal human supervision
• Environmental and situational adaptability, as agentic AI systems
  • opperate effectively in dynamic and unpredictable environments
  • adapt to changing conditions in real-time
  • make decisions with incomplete information
  • handle uncertainty effectively
• Independent decision-making, as agentic AI systems
  • can learn from experience and improve over time
  • use reinforcement learning and meta-learning
  • demonstrate flexibility in strategy selection
  • reconceptualizes approaches based on new information

Agentic AI systems need to have the ability to

• gather information from the environment,
• maintaining the execution context over long periods,
• develop strategies to achieve goals (i.e, independent decision-making),
• communicate plans and goals at appropriate abstraction levels,
• perform operations that can influence the environment’s state,
• learn and adapt to their environment, and
• coordinate with other agents or humans in response to current situations (Anthrophic 2024).

Comparison with traditional AI

Comparison of traditional AI and Agentic AI based on Acharya, Kuppan, and Divya (2025)

Feature	Traditional AI	Agentic AI
Primary purpose	Task-specific automation	Goal-oriented autonomy
Human intervention	High (predefined parameters)	Low (autonomous adaptability)
Adaptability	Limited	High
Environment interaction	Static or limited context	Dynamic and context-aware
Learning type	Primarily supervised	Reinforcement and self-supervised
Decision-making	Data-driven, static rules	Autonomous, contextual reasoning

Comparison of agent types

Comparison between classical agents, reinforcement learning agents, and agentic AI based on Acharya, Kuppan, and Divya (2025)

Feature	Classical Agents	Learning Agents	Agentic AI
Primary Purpose	Fixed-task automation	Reward-driven optimization	Goal-oriented autonomy
Adaptability	Low	Moderate	High
Learning Type	Supervised	Reinforcement Learning	Hybrid, including RAG and Memory
Applications	Static systems	Dynamic environments	Complex, multi-objective tasks

Applications

Key application areas of Agentic AI are, for instance:

• Healthcare — patient monitoring, early warning systems, personalized care management
• Finance — algorithmic trading, fraud detection, personalized financial advice
• Manufacturing — predictive maintenance, optimization of production processes
• Education — assisting learners by tailoring educational content
• Software Engineering — from code completion to autonomous problem-solving

Source: Acharya, Kuppan, and Divya (2025)

Workflow patterns

Anthrophic (2024) discusses five key workflow patterns that can be implemented when designing agentic AI systems:

1. Prompt chaining — sequentially connecting prompts; outputs become inputs; creates complex reasoning flows and multi-step processes
2. Routing — directs tasks to specialized components based on task type; improves efficiency through targeted processing
3. Parallelization — processes multiple subtasks simultaneously; increases throughput and handles independent workstreams
4. Orchestrator-workers — central orchestrator delegates to specialized worker agents; manages coordination, integration, and quality control
5. Evaluator-optimizer — separate components to generate, evaluate, and refine solutions; enables iterative improvement and higher quality outputs

1. Prompt Chaining: This pattern connects multiple prompts in sequence, where the output of one prompt becomes the input to the next. This creates a pipeline for complex reasoning or multi-step tasks. For example, an agent might first analyze requirements, then generate a solution, then check for errors, with each step building on the previous one. This approach is effective for breaking down complex tasks into manageable steps with clear dependencies.

2. Routing: In this pattern, incoming tasks are analyzed and directed to specialized components based on their type or requirements. A router component determines which specialized agent or module should handle a particular subtask. For instance, a general assistant might route code-related questions to a specialized coding agent, research questions to a research agent, and creative tasks to a creative agent. This improves efficiency by ensuring tasks are handled by the most appropriate components.

3. Parallelization: This workflow processes multiple subtasks simultaneously rather than sequentially. It’s particularly valuable when subtasks are independent of each other or when processing large volumes of similar items. For example, an agent might analyze multiple documents in parallel, or generate several different creative options simultaneously. Parallelization increases throughput and can significantly reduce total processing time.

4. Orchestrator-Workers: This pattern involves a central orchestrator agent that delegates tasks to specialized worker agents, then integrates their outputs. The orchestrator manages the overall process, coordinates between workers, and ensures consistent quality across outputs. For example, in a content creation system, an orchestrator might delegate research to one worker, writing to another, and fact-checking to a third, then integrate their work into a cohesive final product.

5. Evaluator-Optimizer: In this workflow, separate components generate solutions, evaluate their quality, and refine them based on that evaluation. This creates a feedback loop that enables iterative improvement. For instance, one component might generate code, another would test it for bugs or efficiency, and a third would refine the code based on the evaluation results. This pattern is especially valuable for tasks where quality can be objectively measured and incremental improvement is possible.

These workflow patterns can be combined and adapted to suit different applications. The choice of pattern depends on factors like task complexity, quality requirements, available compute resources, and time constraints. Effective agent systems often incorporate multiple patterns to handle different aspects of their operation.

Governance and accountability

As agentic AI systems act autonomously, safety and accountability are critical aspects (Shavit et al. 2023).

With the increasing autonomy of AI systems, the need to develop mechanisms for their governance and assignment of responsibility also grows. Shavit et al. (2023) proposes a series of practices aimed at ensuring the safe and responsible use of such systems. These range from careful selection of application areas to limiting freedom of action to the ability to monitor agent behavior and intervene when necessary.

Proposed practices for safe and responsible operation:

• Suitability assessment of the agent for the specific task
• Limitation of scope and potentially approval requirements for certain actions
• Establishment of default behaviors for agents
• Ensuring traceability of agent activities
• Implementation of automatic monitoring
• Possibility of attributability of actions
• Interruptibility of the agent and maintenance of human control

Human-AI interaction

From tools to teammates

Seeber et al. (2020) highlight a fundamental shift in how we think about AI systems.

Traditional AI	AI as Teammates
Role: Tool to be used	Role: Active collaboration partner
Interaction: Responds to commands	Interaction: Engages proactively
Function: Task automation	Function: Complex problem-solving
Agency: Limited/directed	Agency: Autonomous with initiative
Integration: System integration	Integration: Social & team integration

Traditional AI has been positioned as tools that enhance human capabilities through automation and augmentation, but the relationship remains primarily that of user and tool.

AI teammates represent a new paradigm where AI systems as they …

• actively participate in defining problems and generating solutions;
• can take initiative in collaborative processes without explicit prompting;
• engage in multiple steps of complex problem solving (defining problems, identifying causes, proposing solutions, planning, etc.);
• learn from interactions and potentially participate in after-action reviews; and
• may have different roles in teams based on capabilities

This shift requires new frameworks for understanding the collaborative relationship between humans and AI systems.

Critical design areas

Seeber et al. (2020) identify three interconnected design areas that must be considered when developing AI agents as teammates:

1. Machine artifact design
2. Collaboration design
3. Institution design

1. Machine artifact design focuses on the AI system itself — its appearance, capabilities, and how it interacts.
2. Collaboration design addresses how humans and AI work together — team composition, task allocation, and workflows.
3. Institution design considers the broader organizational and societal context — including responsibility frameworks, liability issues, and training requirements.

These areas do not exist in isolation but influence each other. Decisions in one area affect the others, requiring a holistic approach to designing effective human-AI collaborative systems.

Role of AI in teams

According to Dennis, Lakhiwal, and Sachdeva (2023), AI agents can support human teams in various aspects.

Three fundamental affordances that AI team members provide:

• Communication support — enables coordination and reminders, review and feedback, delegation
• Information processing support — includes data cataloging, information search and retrieval, information analysis, organization, creation and management of content indexes
• Process structuring and appropriation — involves planning and scheduling, task breakdown structures, task tracking and delivery, quality assurance and checking

Communication support

• Natural language speech capabilities
• Delegation and supervision functions
• Coordination and reminder capabilities
• Review and feedback functions

Information processing support

• Data cataloging
• Information search and retrieval
• Information analysis
• Information organization
• Creation and management of content repositories

Process structuring and appropriation support*

• Planning and scheduling
• Task breakdown structures
• Task tracking and delivery
• Quality assurance and testing

These affordances enable AI team members to contribute to team processes in specific ways that complement human team members. The researchers conceptualized an AI team member that supports team processes through text-based natural language interactions, operates autonomously, and co-delegates work with other team members.

Q&A

Literature

Acharya, Deepak Bhaskar, Karthigeyan Kuppan, and B Divya. 2025. “Agentic AI: Autonomous Intelligence for Complex Goals–a Comprehensive Survey.” IEEE Access.

Anthrophic. 2024. “Building Effective Agents.” Anthropic Research Team; https://www.anthropic.com/engineering/building-effective-agents.

Dennis, Alan R, Akshat Lakhiwal, and Agrim Sachdeva. 2023. “AI Agents as Team Members: Effects on Satisfaction, Conflict, Trustworthiness, and Willingness to Work With.” Journal of Management Information Systems 40 (2): 307–37.

Russel, Stuart, and Peter Norvig. 2022. Artificial Intelligence: A Modern Approach. Harlow: Pearson Education.

Seeber, Isabella, Eva Bittner, Robert O Briggs, Triparna De Vreede, Gert-Jan De Vreede, Aaron Elkins, Ronald Maier, et al. 2020. “Machines as Teammates: A Research Agenda on AI in Team Collaboration.” Information & Management 57 (2): 103174.

Shavit, Yonadav, Sandhini Agarwal, Miles Brundage, Steven Adler, Cullen O’Keefe, Rosie Campbell, Teddy Lee, et al. 2023. “Practices for Governing Agentic AI Systems.” Research Paper, OpenAI.

Wiener, Norbert. 1960. “Some Moral and Technical Consequences of Automation.” Science 131 (3410): 1355–58.

Footnotes

1. The agent function maps any given percept sequence to an action (an abstract mathematical description).

2. The term percept refers to the content an agent’s sensors are perceiving. The percept sequence is the complete history of everything an agent has ever perceived.

3. Rectangles are used to denote the current internal state of the agent’s decision process, rectangles with rounded corners to represent the background information used in the process.