(Editor’s Note: What follows is Part 1 of “AI Agents Unleashed: A Comprehensive Guide for Industry Trailblazers.” Part 2 follows tomorrow.)
Dr. Gopala Krishna Behara & Prasad Palli
An AI agent is a software application that engages with its surroundings, collects information, and utilizes that data to accomplish predefined objectives.
AI Agents are,
- Software programs that perform tasks autonomously or semi- autonomously
- Runs independently to design, execute, and optimize workflows
- Guardrails can be built into AI agents to help ensure they execute tasks
correctly - Powerful tool for driving better decision-making and operational efficiency
- Personalization
- Perform tasks with high precision and consistency
- Manage and optimize complex systems
- Monitor and analyze security threats in real-time, providing proactive measures to prevent breaches and ensure data protection
- Increase productivity by reasoning, planning, and self-checking, releasing users from certain tasks
According to Mark Zuckerberg of Meta, “We are going to live in a World where there are going to be hundreds of millions of different AI Agents, eventually probably more AI agents than there are people in the world”.
According to Satya Nadella, “SaaS apps are nothing more than a CURD database with some business logic, but once the business logic moves to AI agents, SaaS is over”. Microsoft introduced AI Agents and updates to Copilot 365 apps as the war to make AI more useful intensifies.
According to Matt Wood of PwC, “AI agents are becoming a significant trend for enterprises, with the potential for agents to cross from one system to another”. He emphasized the need for robust orchestration frameworks to manage these agents.
Dharmesh Shah of HubSpot, envisions a future where “AI agents become integral to business operations”. He believes that these agents will collaborate to achieve complex tasks, often without human supervision. The agents become “digital teammates“, according to Shah.
This Comprehensive Guide covers the industry adoption of AI Agents, characteristics of AI Agents, AI Agent adoption steps and challenges, AI Agent reference architecture and benefits of AI Agents. It also covers real world Use case using AI Agent technology.
Industry Adoption of AI Agents
According to Gartner, “Any organization in any industry, especially those with very large amounts of data, can use AI for business value.” The report says, AI agents will streamline operations, reduce manual tasks, and improve decision-making processes.
- By 2028, there was a significant increase in AI agent adoption across various industries-Gartner.
- By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously – Gartner
- By 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop. This is not happening at all today – Gartner
- Recent CEO surveys show almost 80% of CEOs believe AI is likely to significantly enhance business efficiencies in their organization – Forbes
- According to the Deloitte survey, 42% of organizations already cite tangible benefits from AI agents
- AI agents will lead to a flattening of organizational structures, with up to 20% of organizations eliminating middle management positions by 2026- Gartner
- AI adoption increased from 50% to 72% and highlight the potential of AI agents to improve business operations – McKinsey
- According to Forrester report, “The State of AI Agents, 2024,” AI agents are advancing from decision-making to action. It emphasizes that businesses will lead AI agent adoption, but tech and risks will temper timelines
- By 2028, agentic AI will be responsible for making 15% of everyday work decisions – Forbes
Business Case for AI Agents Usage
Many CXO’s see the IT budget as an area of overspending and are continuously looking for ways to reduce costs and effort.
The ability of Technological advancement to do “More and More with Less and Less until eventually you can do everything with nothing” – R. Buckminster Fuller, Architect, Systems Theorist, Writer, Designer, Inventor, Philosopher and Futurist.
Driving business outcomes with AI Agent requires strategy and collaboration from enterprise teams. The following strategy level questions help to understand the enterprise readiness for the AI Agent adoption.
Business
- How does AI Agent align with enterprise overall business goals and strategic objectives
- Is enterprise having governance framework in place to manage the deployment and ethical use of AI Agent
- Is there a top management-level charter for AI Agent tied to one or more of the drivers
- Do enterprise has a published Business Strategy for AI Agent
- How does the AI Agent help in enhancing existing processes and enterprise strategy
- Is there an internal business case built? If so, at what level
- What strategies enterprises have in place to identify, assess, and mitigate risks associated with AI Agent
- How enterprise is managing the organizational changes required for the adoption of AI Agent
- What key performance indicators (KPIs) will be used to measure the success and impact of AI Agent initiatives
Technology
- Is current IT infrastructure capable of supporting the computational demands of AI Agent
- Does the workforce possess the skills to use AI Agent, and what are the implications for talent acquisition and upskilling
- Are data management practices robust enough to handle the data requirements and ensure data quality for AI Agent
- How enterprise addressing security and compliance concerns related to the deployment of AI Agent
AI Agents are Not Magic. They perform the same steps a human worker would do. But Much Faster.
Basic AI Agent Types
There are several types of AI Agents, and each is designed for specific tasks and environments.
Simple Reflex Agents: These agents make decisions based on the current perception. They follow condition-action rules and are suitable for observable environments. No learning or memory.
Example: Automated Hand Sanitizer Dispensers, these devices dispense sanitizer when they detect motion, ensuring hygiene without human intervention.
Model-based Reflex Agents: These agents handle partially observable environments by maintaining an internal state based on perception history.
Example: Smart IV Pumps, these pumps adjust the flow rate of intravenous fluids based on real-time patient data, such as heart rate and blood pressure, ensuring safe and precise delivery.
Figure 1 Types of AI Agent
Goal-based Agents: These agents have goals and choose actions to achieve those goals. They consider the current state and the desired goal to make decisions. They are centered not only on existing conditions but also on future conditions and the relationship between conditions and operations.
Example: Personalized Treatment Planning Systems, these systems analyze patient data, including genetics and medical history, to create customized treatment plans for managing chronic diseases.
Utility-based Agents: They choose actions that maximize the expected utility.
Example: Resource Allocation Systems, these systems optimize the use of hospital resources, such as beds and medical equipment, to maximize patient care efficiency and minimize costs.
Learning Agents: These agents learn from their experiences and improve their performance over time. They start with basic knowledge and adapt through learning. Example: Predictive Analytics for Patient Monitoring, these agents learn from historical patient data to predict potential health issues, allowing for early intervention and better patient outcomes.
Hierarchical Agents: These agents decompose tasks into subtasks and solve them hierarchically. They can handle complex tasks by breaking them down into simpler, manageable parts.
Example: Hospital Workflow Management Systems, these systems break down complex tasks into subtasks, such as patient admission, treatment scheduling, and discharge planning, to streamline hospital operations.
Key Characteristics of AI Agents
AI Agent systems possess several key characteristics that distinguish them from traditional AI systems:
- Autonomy: AI Agent systems operate independently. They can make decisions and executing actions without human intervention
- Adaptability: Learning from experiences, Agents can adapt to new environments and scenarios
- Goal-Driven: Agents are designed to achieve specific objectives or goals, which are capable of strategic decision-making
- Context Awareness: Agent systems can understand and respond to the context in which they operate, allowing for more nuanced and effective interactions.
- Proactivity: Agent systems can anticipate future needs and take proactive measures to address them, rather than simply reacting to external inputs.
- Technical Capabilities: AI Agent systems leverage a variety of tools to enhance their functionality and handle complex scenarios. Some of them are,
- Internet Access: Allows agents to retrieve real-time information, perform web searches, and gather data from online sources.
- Code Interpreters: Enable agents to execute and interpret code, facilitating complex computations, data analysis, and automation tasks.
- API Calls: Allow agents to interact with external services and systems, enabling seamless integration with various platforms, databases, and applications.
- Communication: Effective communication is a hallmark of AI Agent, enabling them to interact seamlessly with humans and other AI systems.
- Scalability: Agents are designed to scale efficiently, handling increased workloads and complexity as needed.
- Robustness: AI Agent systems are resilient and can maintain performance even in the face of challenges or disruptions.
- Ethical Considerations: AI Agent systems are built with ethical guidelines to ensure they operate responsibly and transparently.
- Personalization: Agents possess the capability to retain individual preferences, enabling personalized interactions. They also have the capacity to store and utilize knowledge.
- Integration Capabilities: Integrate with existing systems and processes, enhancing overall organization efficiency and effectiveness.
These characteristics make AI Agent systems powerful tools for various applications, from strategic decision-making to personalized customer interactions.
AI Agents Workflow
AI agents are transforming automation by emulating human thought processes, decision-making, and actions. Below is a simplified workflow illustrating their operation.
Figure 2 AI Agents Workflow
Perception: In this step the agents process input data received from different data sources. The data is processed to extract relevant information like video, textual and numeric data.
Domain Knowledge: It consists of historical data, and user-specific contexts. AI Agents connects to this knowledge base to make informed decisions.
Planning: It involves sequence of steps or a hierarchical structure of sub-goals. AI Agents break down goals into actionable steps, allocate resources, and prioritize tasks to optimize efficiency.
Decision: AI agents make decisions even when facing incomplete or unreliable information. It assesses the available choices and determines the best course of action considering its objectives, strategy, and the present circumstances. Utilizing logical inference, pattern recognition, search algorithms and decision trees, agents assess alternatives and forecast results.
Tools Usage: Agents integrate with APIs, databases, and external services to access the tools they need for task execution.
Action Execution: It executes the selected action. Execution engine ensures tasks are performed smoothly, managing errors and validating results for accuracy. Action can be sending messages, decision making.
Output: Agents analyze user feedback to improve future responses.
Monitoring: Agents continuously monitor performance metrics, usage patterns, and system health to ensure reliability and efficiency.
Enterprise readiness for AI Agent involves several key factors to ensure successful deployment and integration. Some important considerations are,
- Governance: Establish clear governance frameworks and compliance protocols to manage the deployment of an AI Agent system. Define the policies for data privacy, security, and ethical use of AI.
- Business Use cases Identification: Identify the business challenges that require attention. Also, understand the business benefits of AI Agent adoption that are critical for the success of enterprise. Select the targeted use cases and perform the Proof of Concepts (POC) that can deliver desired business and operational outcomes. Build value through improved productivity, growth, and new business models.
- Change Management: Implement change management to help team to adapt new AI-driven workflows. Establish clear communication, stakeholder engagement, and support systems to ease the transition.
- Ethical Values: Address ethical considerations related to the use of AI Agent, such as bias, fairness, and transparency. Implement ethical guidelines and review processes to ensure responsible AI deployment.
- Infrastructure: Ensure that the existing IT infrastructure can support the deployment of AI Agent systems. It involves upgrading hardware, software, and network capabilities to handle the increased computational demands.
- Upskilling: Reskill the employees to improve productivity by conducting various training courses and encourage them to perform POCs. Also, based on role and skills of employees, identify the skill gaps and train them effectively to contribute better ways to the enterprise transformation initiatives.
- Risk Management: Develop strategies to identify, assess, and mitigate risks associated with AI Agent. Implement robust monitoring and auditing mechanisms to track AI behavior and ensure accountability.
Acknowledgements
The authors would like to thank Tanay Srivastava, Director, Tricon Solution LLC for giving the required time and support in many ways in bringing up this Comprehensive Guide as part of Technical Services efforts.
About Authors
Dr. Gopala Krishna Behara is an Enterprise Architect at Tricon Solutions LLC. He has around 28 years of IT experience. He can be reached at gopalakrishna.behara@triconitsolutions.com.
Prasad Palli is Principal Enterprise Architect at Albertsons Companies. He has around 25+ years of IT experience. He can be reached at palli_prasad@hotmail.com
Disclaimer
The views expressed in this article/presentation are those of authors and Tricon Solutions LLC & Albertsons does not subscribe to the substance, veracity or truthfulness of the said opinion.
(Part 2 appears tomorrow.)