Artificial Intelligence (AI) has become an integral part of daily life, seamlessly integrating into a variety of devices such as smartphones, laptops, tablets, smartwatches, TVs, and even household appliances like refrigerators. But not all AIs are equal; some are more capable than others. Agentic AI is one of the more recent advancements in the AI space and is expected to see widespread adoption in the coming years. Gartner predicts that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. But what is agentic AI, and how does it work? This blog answers both questions, explains how agentic AI differs from generative AI and from AI agents, and shows where it is already used in business operations.
What is agentic AI?
Agentic AI is artificial intelligence that can pursue a goal on its own: it plans the steps, uses tools and data, takes actions in software systems, checks the results and adjusts, while a person sets the objective and the limits rather than directing each step. The agentic AI meaning in one line: software that acts, not just answers. Agentic AI is the next step in the evolution of AI as a productivity and efficiency tool. Essentially, agentic AI software has agency. It can leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), machine learning, predictive algorithms, and more to create AI systems that do more than generate content. These agentic AI frameworks can function as autonomous, iterative, and adaptable digital workforce modules that can execute complex, multi-step, and effort-intensive tasks without needing constant human oversight and input.
Agentic AI can go beyond simply following instructions and start proactively learning, adapting, and solving problems. This makes it more than just a tool; it becomes a digital workforce capable of managing workflows end-to-end.
Agentic AI vs generative AI vs AI agents: what is the difference?
| Feature | Agentic AI | Generative AI |
| Primary Function | Makes decisions and takes autonomous actions to achieve specific goals. | Generates content (e.g., text, images, music) based on input data and patterns. |
| Learning and Decision-Making | Uses reinforcement learning to improve decision-making over time through real-time feedback. | Uses supervised and unsupervised learning to mimic creativity by producing new outputs from training data. |
| Output | Produces actions or decisions (e.g., driving a car, sending a report) | Produces creative outputs (e.g., stories, images, videos). |
| Autonomy | Autonomous within set limits: acts on its own and escalates to a person at defined checkpoints. | Requires input (e.g., prompts, instructions) from humans to generate content. |
| Examples | An agent that receives an invoice, matches it, resolves the exception and schedules payment; an IT agent that diagnoses and fixes a ticket. | ChatGPT (text generation), DALL-E (image creation), AI music generators. |
Agentic AI and AI agents are related but not the same thing. An AI agent is a single program built for one job, such as answering a query or classifying a document. Agentic AI is the wider approach in which one or more agents, an orchestrator, tools and memory work together toward a goal over many steps. Every agentic AI system uses AI agents; not every AI agent is agentic.
How does agentic AI work?
Agentic AI works by integrating multiple components that allow it to process information, evaluate outcomes, and take autonomous actions. These systems rely on complex algorithms, such as machine learning and reinforcement learning, to continuously improve their decision-making capabilities.
For example, decision trees or neural networks help agentic AI determine the best course of action based on real-time data. Unlike conventional AI models, which require explicit instructions for each task, agentic AI learns to make decisions based on past actions and feedback from its environment.
Agentic AI is particularly powerful in environments where decisions need to be made quickly or when human involvement is impractical, such as high-volume back-office processing or round-the-clock service operations.
Most agentic systems combine five components: a large language model for reasoning, tools and application programming interfaces (APIs) to act in other systems, memory to carry context across steps, an orchestrator that sequences agents and tasks, and guardrails that define what the agent may do without a person.
There are four primary steps that make up an agentic AI software or program:
- Deconstructing the task: AI agents process the task and start gathering resources from all available inputs to help execute the task. This might involve extracting data, analysing media, or creating a workflow. The AI sets processes in motion to begin the task.
- Reasoning: Next, the AI uses the cognitive capabilities of an LLM (rather than its generative applications) and creates the cerebral core of the task. The decision-making aspect of agentic AI comes to the fore here, with tools like RAGs, external software, and additional AI agents or processes used to understand the task and plan steps.
- Execution: Once the agentic AI has the processes in place, it runs the workflow and executes the task based on the processes that it has created in the “Reasoning” stage. Compatibility with failsafe procedures and safety checkpoints are also validated at this stage.
- Iteration and augmentation: The final step involves refining and validating the results. This can be achieved through user input, a separate AI agent included for this very purpose, and ingestion of the results to create a validation data loop.
What are the benefits of agentic AI?
The benefits of agentic AI are immense, especially in industries that rely on automation and decision-making at scale. Some key advantages include:
- Increased Efficiency: Agentic AI can operate 24/7 without fatigue, enabling businesses to automate repetitive tasks and processes that previously required human input. This efficiency leads to reduced operational costs and faster decision-making.
- Improved Decision-Making: By processing large amounts of data and evaluating countless potential outcomes, agentic AI can make decisions that would be difficult for humans to achieve in real-time. This is particularly useful in fields like financial trading, where fast, data-driven decisions are crucial.
- Scalability: As systems powered by agentic AI become more prevalent, organizations can scale operations without the need for additional personnel. This ability to grow without corresponding increases in workforce size is a significant advantage.
- Increased Adaptability: Agentic AI systems can learn from their environment and adjust their actions accordingly. This adaptability is especially important in dynamic fields like healthcare or customer service, where needs can change rapidly.
Agentic AI examples and use cases in business operations
Agentic AI is already being implemented across various sectors. Below are some of the most prominent examples of agentic AI in action:
- Accounts payable: An agent receives an invoice, extracts and validates the data, matches it to the purchase order and receipt, resolves routine exceptions with the supplier, and schedules payment, escalating only the cases outside its limits. Read how agentic AI automation works in accounts payable.
- Customer service: An agent reads the customer’s history, diagnoses the issue, applies the refund or reschedules the delivery in the order system, and confirms back to the customer, handing over to a person when sentiment or value crosses a threshold.
- Procurement: Agents classify spend, check contracts and catalogues, raise and route requisitions, and flag off-contract buying before the purchase order is issued.
- HR onboarding: An agent collects documents, verifies them, creates system access, schedules training and answers new-joiner questions across the first weeks.
- IT operations: Agents triage tickets, run diagnostics, apply known fixes and open change requests for anything they cannot resolve.
- Healthcare operations: Agents check eligibility, assemble prior authorisation requests, track claim status and chase missing documentation, with clinical decisions left to clinicians.
For how these use cases change business process management itself, read our blog on agentic AI as the next frontier in business process management.
Challenges of implementing agentic AI
Despite its potential, implementing agentic AI comes with several challenges Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls, and warns of "agent washing", where existing assistants, chatbots and RPA tools are relabelled as agentic AI:
- Technical Complexity: The development of agentic AI systems requires advanced algorithms and large datasets to function effectively. This can pose challenges for organizations that lack the necessary technical expertise or infrastructure.
- Ethical Concerns: There are concerns about the autonomy of AI systems, especially when it comes to decision-making in sensitive areas like healthcare or law enforcement. Ensuring that agentic AI is aligned with ethical principles is critical to avoid unintended consequences.
- Regulatory Issues: As agentic AI becomes more integrated into society, governments will need to establish regulations to ensure its safe and fair use. This may involve creating standards for AI development, testing, and deployment.
- Cost of Implementation: While agentic AI can lead to long-term savings, the upfront cost of developing and deploying these systems can be significant, especially for smaller businesses or startups.
Conclusion
In conclusion, Agentic AI is revolutionizing industries by offering unparalleled efficiency, scalability, and decision-making capabilities. While challenges remain, especially in terms of technical complexity and ethical concerns, the potential benefits of agentic AI far outweigh the drawbacks. As this technology continues to evolve, it promises to shape the future of automation, decision-making, and even human interaction with AI.
How can Infosys BPM help?
At Infosys BPM, we understand the importance of harnessing the full potential of advanced technologies like Agentic AI. Our comprehensive solutions help businesses seamlessly integrate AI into their workflows, enabling them to automate tasks, enhance operational efficiency, and achieve their strategic goals. With our expertise in digital transformation, we guide organisations through the process of adopting and scaling Agentic AI, ensuring they stay ahead of the curve and maximise the benefits of this cutting-edge technology.
Our generative AI practice brings more than 12,000 AI assets and 150 pre-trained models to operations work, with agentic AI solutions already running end-to-end invoice processing, spend classification and enrichment, and data acquisition across sources.
Frequently asked questions about agentic AI
How does agentic AI differ from traditional AI?
Agentic AI refers to systems that autonomously make decisions and take actions based on real-time data, without continuous human input. Unlike traditional AI, which often assists human decision-making or performs predefined tasks, Agentic AI acts independently, mimicking human-like decision processes in dynamic environments.
How does agentic AI make decisions autonomously?
Agentic AI uses reinforcement learning and advanced algorithms to process vast amounts of data, evaluate potential outcomes, and make decisions that align with its objectives. These systems learn from feedback to improve their actions over time, enabling them to operate independently and make real-time decisions in various scenarios.
What is the primary function of the reasoning part of an agentic AI?
The reasoning part turns a goal into a plan. It breaks the task into steps, decides which tools, data sources or other agents each step needs, checks intermediate results against the goal, and changes course when a step fails or returns something unexpected. In most systems this is done by a large language model working in a reason-then-act loop, which is what separates an agent from a script that runs fixed steps in a fixed order.
How do you implement agentic AI in a business process?
Start with one process that has a clear goal, structured inputs and a measurable outcome, such as invoice exception handling or ticket triage. Map the steps and decisions a person takes today, give the agent access to the same systems through tools and APIs, set the limits it must escalate at (value thresholds, confidence scores, policy exceptions), run it alongside the human team until accuracy is proven, and then widen its authority step by step. Logging every action and decision from day one is what makes the later audit and scale-up possible.
Is agentic AI safe to use in regulated industries such as healthcare and finance?
Yes, when the autonomy is bounded. In regulated processes the agent works inside guardrails: it can only call approved tools, every action is logged with the reasoning behind it, decisions above a defined risk level are routed to a person, and the model never sees data it is not entitled to. Healthcare operations teams use this pattern for prior authorisation and claims status, and finance teams for payment release and reconciliation, with the human sign-off kept where the regulation requires it
