Today, we are at the cusp of another significant advancement in AI, Agentic AI. 

Agentic AI is autonomous, self-directed, and goal-oriented. Unlike traditional AI, which waits passively for user prompts, agentic AI can execute multi-step tasks, adapt in real-time, and respond with minimal human intervention.

Gartner predicts that 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.

Yet, an essential question arises: How do these autonomous systems decide which actions to take? Where does the “intelligence” come from that guides them toward (or away from) certain decisions? 

The answer lies in agentic reasoning—the driving force behind Agentic AI’s decisions, plans, and actions.

In this article, we’ll deep dive into the concept of agentic reasoning, discuss its need, how it works, its applications across industries and how it’s poised to shape the future of autonomous AI. 

Agentic reasoning emulates human problem-solving skills and acts as the “brain” of an AI application. It helps the AI understand user intentions, gather relevant context, strategically plan actions, execute tasks sequentially, and learn continuously. 

One thing no one formally taught us, yet we practice every day, is decision-making. We make decisions in one of two ways: automatically, with little conscious thought, or deliberately, after careful consideration.   

In psychology, this is called the dual process theory. It describes that human decision-making is either automatic and spontaneous (System 1) or conscious and controlled (System 2).

Daniel Kahneman, psychologist, Nobel-prize winner, and author of ”Thinking, Fast and Slow,” discussed the differences between the two styles. System 1 is the default mode of thinking because it conserves cognitive energy. In contrast, System 2 is a more deliberate and analytical process that yields the most accurate solutions for complex problems.

Agentic reasoning takes inspiration from this System 2 style, aiming to replicate human-like deliberation in AI. Agentic reasoning is the cognitive structure that enables Agentic AI to interpret goals, plan approaches, and adapt to new data or challenges.

When Generative AI and LLMs first launched, many companies adopted them into their workflows to improve efficiency and productivity. But, as users became comfortable with these tools, they began demanding more. 

They wanted AI to handle complex tasks rather than just provide information. In response, enterprises sought solutions like Retrieval-Augmented Generation (RAG) for domain-specific knowledge, but RAG systems often struggle to prioritize and interpret the data they retrieve. Without robust reasoning capabilities, these solutions can’t always determine which information is most relevant.

This is where the need for AI systems to be better at reasoning emerged. We’ve discussed them in detail: 

• Changing user expectations

When users started with Generative AI, they worked with a basic sense of what they could accomplish with it. Users would ask ChatGPT questions like “What are the top 3 Michelin-star restaurants in New York?”. With time and experimentation with basic queries, their queries gradually became more advanced. They now expect AI to complete tasks for them, and their queries may look like, “Can you book me a table for two at one of the top 3 Michelin-star restaurants in NYC?”

In the enterprise setting, employees now expect AI to complete complex tasks using their enterprise data. Their needs moved from information retrieval and insights discovery to workflow execution, where AI reasons and makes decisions. 

• Demand for context-rich information

Enterprises increasingly require detailed, domain-specific knowledge incorporating organizational policies, compliance regulations, and real-time operational data. Simple question-answer capabilities no longer suffice.

For example, an HR team might need an AI to reference internal policies, local labor laws, and employee eligibility data all at once—rather than just returning a general definition. AI may only provide surface-level facts without a robust reasoning layer, failing to factor in the exact context needed for accurate, actionable insights.

• Shortcomings of static approaches 

While Retrieval Augmented Generation (RAG) can fetch relevant documents, it lacks the logic to prioritize or interpret that information for multi-step tasks. Similarly, basic LLM reasoning is typically confined by limited context windows and lacks an advanced feedback mechanism to manage complex enterprise workflows.

We know that agentic reasoning helps in human-like thinking, but how does it work to achieve human-like cognition while solving complex problems? Let’s find out:

Goal identification

The first step in agentic reasoning is to define the goal. The AI identifies its goal through a user request or a system-generated trigger. This goal then becomes the anchor for every subsequent decision.

If the AI receives a direct prompt from the user, like “My VPN is not connecting,” it interprets the input to set the objective, which is to restore the user's VPN access. This interpretation involves parsing the user’s statement, understanding the nature of the request, and mapping it to a goal the system can act upon.

When the AI detects issues on its own by monitoring logs, alerts, or performance metrics and notices deviation from predefined thresholds. For example, if server response times exceed normal ranges, the system sets its goal as “Stabilize server performance.”

Contextual data gathering

Once the goal is set—in this case, restoring a user’s VPN access—AI queries the necessary data sources via direct API calls. It may first check whether the software on the user's laptop is updated, then reference corporate policies to verify VPN permissions. 

The AI gains a complete view of the issue by synthesizing these details and can determine the most effective next steps.

Reasoning and approach planning

After collecting the necessary context, the AI transitions into a decision-making phase that mirrors human problem-solving and decides how to solve the problem for the user. During this planning stage, large language models help the AI system simulate different approaches and select the one that is most likely to succeed.

For example, potential strategies for VPN access might include:

• Asking the user to try a self-service VPN reset tool.

• Checking server logs for common error codes.

• Escalating to a human support agent if repeated login attempts fail.

Action and orchestration

With the plan charted out, such as having the user attempt a self-service VPN reset tool or inspecting server logs, the AI shifts its role from planning to execution. The system may initiate automated scripts that unlock users' accounts, restart VPN services, or adjust network settings. If an action doesn’t resolve the problem, the AI continues to try the next approach without waiting for instructions from the user. 

By chaining these steps together, the system completes multi-step tasks in a logical order, continually verifying whether each intervention has fixed the issue. When all automated strategies are exhausted or a critical block is detected, the problem escalates to a human support agent.

The ability of Agentic AI to take autonomous actions powered by agentic reasoning has helped businesses realize the potential for applying these capabilities to generative AI and boosting productivity across the entire organization.

Below, we explore how agentic reasoning is adopted across various industries to transform workflows and deliver tangible business benefits.

Enterprise search and knowledge management 

Enterprises store a massive corpus of information in the form of technical documents, FAQs, product manuals, policy documents, and more. Traditional search tools might display a list of documents, leaving employees to sift manually.

With agentic reasoning, the AI can interpret a user's complex, nuanced query and retrieve accurate information. 

For example, if a user asks, “Find the most recent policy changes regarding remote work and prepare a summary,” the AI will then retrieve policy documents from HR and legal departments, extract relevant sections, highlight updates, and format them in a concise summary. 

IT support

IT service desk agents are bogged down by repetitive IT support requests like resetting passwords, installing software updates, and troubleshooting. 

Enterprises can employ agentic workflows with agentic reasoning to automate these repetitive tasks. The system understands employee queries in natural language, applies logic, and executes solutions to resolve queries.

Another time-consuming and complex task for IT teams is root cause analysis. The AI system leverages agentic reasoning to check system logs, correlate similar incidents in history, and analyze previous fixes. 

At first, the AI system applies known methods to solve the issue one after the other. Still, if an issue is too complicated, it automatically escalates them to the right support agent with full context. This helps reduce downtime and provides faster resolution of queries. 

Customer support

Today, your customers want fast and personalized resolutions. Traditional chatbots can easily answer straightforward queries, but complex and nuanced queries demand a context-aware approach. 

Agentic reasoning helps automate end-to-end ticket triaging and resolution. For example, suppose a customer reports a delivery delay. In that case, the AI interprets the concern, gathers account details and order history, and checks logistics data to provide a status update on the parcel. 

It also detects customer sentiment to handle queries with empathy. If the customer is upset with the situation, the AI applies a reasoning framework to determine the best approach, such as offering discounts or other compensatory benefits. This significantly improves customer satisfaction. 

HR support

You can incorporate agentic reasoning in your enterprise AI Copilot to automate complex tasks like onboarding, offboarding, and payroll management. HR teams can also automate responses to complex, nuanced queries regarding PTO requests, health benefits, tax, etc.

For example, when employees ask nuanced HR questions such as, “I want to apply for parental leave,” the AI applies a chain of thought to check eligibility, retrieve HR policies, calculate personalized leave benefits, and provide the steps to apply for leave. This way, the AI delivers context-aware HR support without any intervention from HR staff.

Finance & Banking

Banking and finance industries can drastically reduce turnaround time, minimize human errors, and improve operational efficiency by leveraging agentic reasoning in their workflows. 

For example, when handling payment disputes, banks can employ agentic reasoning to automate the process. The AI can gather transaction logs, user statements, and relevant policies to produce a recommended action plan, like issuing a refund, requesting more documentation, or escalating to a specialist.

Healthcare

The healthcare environment involves complex patient data and high-stakes medical decisions. Agentic reasoning can help doctors make decisions specific to individual patients, enabling faster diagnoses. 

While final decisions rest with healthcare professionals, an agentic AI can chain thoughts to gather patient data, cross-reference medical databases, and propose potential diagnoses or care pathways.

By enabling systems to form intentions, plan multi-step actions, self-correct, and continuously learn, agentic reasoning helps reduce operational costs, improve user experiences, enhance autonomy and scalability. 

Reduced operational costs

Agentic reasoning drastically lowers dependency on human agents by giving AI the cognitive ability to reason like humans when executing complex, multi-step tasks. Whether it’s IT ticket resolution or responding to repetitive HR queries, AI system handles resolution with minimal manual intervention. 

Improved user experience 

With agentic reasoning, Agentic AI systems deliver more personalized and prompt interactions. Users don’t have to repeat the same information or wait for multiple approvals. The AI seamlessly plans each step for resolution, aligning with the user’s and organization’s best interests.

Enhanced autonomy and scalability

Agentic reasoning enhances the self-directed behavior of generative AI. This means that when you integrate reasoning frameworks and build a cognitive architecture with LLM models for your enterprise, you can confidently expand AI-driven operations across multiple departments like IT, HR, and Finance.

Today, leaders are looking to embrace more agency in their enterprise functions. 82% of companies in a Capgemini survey want to implement AI agents in the next 1-3 years, and investors are exploring more companies producing autonomous AI technology.

Agentic reasoning is the driving force that enables Agentic AI to self-direct decisions, orchestrate multi-step tasks, and adapt to changing contexts with human-like cognition.

Looking forward, we can expect further enhancements in agentic reasoning. As LLMs like ChatGPT, Claude, and DeepSeek-R1 become more refined and domain-specific, they’ll serve as stronger foundations for agentic workflows.

As agentic AI takes on more autonomous decisions, many AI governance platforms will emerge to ensure transparency, fairness, and accountability.

While agentic reasoning is powerful, practically implementing it in a complex enterprise environment can be challenging. That’s where Workativ steps in. The platform offers advanced AI capabilities for enterprise-grade knowledge management and workflow automation. 

With Workativ’s Knowledge AI, enterprises can unify their data sources by connecting their internal knowledge base, CRM systems, HR portals, ITSM tools, and other third-party applications. As a result, your employees will get access to relevant data in seconds.

Workativ simplifies the creation of agentic workflows. The platform offers an intuitive drag-and-drop interface, connects with popular enterprise tools through API and connectors, utilizes reasoning layers, and enables enterprises to fully automate multi-step processes like onboarding, leave management, and IT ticketing.

What’s more?

You can decide which tasks the AI system can handle fully autonomously—like password resets and software provisioning—and which tasks require a manager’s approval or a quick review, like leave approvals and accessing confidential reports. This helps enterprises strike a balance between human-AI collaboration. 

Workativ helped automate IT tasks such as password resets, printer installations, support ticket creations, and distribution list management for GoTo, a software-as-a-service (SaaS) provider of cloud-based remote work tools for collaboration and  IT management products.

This collaboration with Workativ helped GoTo improve metrics like MTTR and FCR, reduce downtime, automate repetitive tasks, provide omnichannel support, and improve overall employee satisfaction. 

Want to see how Workativ leverages agentic AI solutions to help employees and leaders focus on innovative and strategic work? Book a demo now.

What is the meaning of agentic AI?

Agentic AI refers to AI systems that act autonomously to achieve complex goals and workflows with limited direct human intervention. These systems understand context and instructions in natural language, set appropriate goals, reason through subtasks, and adapt decisions and actions based on changing conditions. 

What is agentic reasoning?

Agentic reasoning is the cognitive structure that enables Agentic AI to interpret goals, plan approaches, and adapt to new data or challenges.

How does agentic reasoning differ from traditional AI decision-making?

Agentic reasoning adds a chain-of-thought process, enabling systems to plan, adapt, and manage unforeseen issues in real-time. 

Traditional AI systems follow predefined rules or single-step computations without self-correction. By contrast, agentic systems interpret context, formulate multi-step actions, and refine decisions, resembling human problem-solving approaches.

Can agentic reasoning be integrated with existing AI systems?

Yes, agentic reasoning can be integrated with existing AI systems. Enterprises can add a reasoning layer to augment existing architectures like RAG or LLM-based solutions. By incorporating goal-setting, contextual checks, and feedback loops, agentic reasoning complements established models without replacing them.

What are the key components that power agentic reasoning in AI systems?

Key components that power agentic reasoning in AI systems include NLP, knowledge graphs, inference engine, ML models, and API integrations.