What Are AI Agentic Workflows?
Use Cases, Benefits & Guide for 2025

As AI solutions gain ground, businesses want to automate as much as possible without losing control. But finding the right balance can be tricky: static AI workflows are often too rigid, and fully autonomous AI agents are overkill for most organizations. That’s why leading companies are implementing agentic AI workflows that can execute complex tasks with adjustable boundaries. 

The 2025 UiPath Agentic AI Report shares interesting discoveries from over 250 US companies with revenue exceeding $1 billion. It turns out that 93% of US-based IT executives are extremely interested in agentic workflows. Over 37% are already using agentic AI workflow solutions, and about one-third plan to invest in the next six months.

Let’s look at what agentic workflows are in AI and how they are different from other AI tools. We’ll explain how agentic AI works, ways organizations use it in their operations, and the likely path the technology will follow in the future. We’ll also break down how to implement these workflows in your organization.

Agentic AI workflows are goal-oriented and dynamic task-execution tools running within a controlled framework with minimal human intervention. We know this sounds complex, so let’s talk it through.

Agentic AI is akin to a semi-autonomous algorithm that can perform complex tasks based on predefined goals. Unlike static workflows, agentic AI can chain together complex processes and correct itself mid-operation based on live feedback.

However, agentic AI remains goal-bound and deterministic to a degree, so the outputs are logically constrained and auditable.

Agentic AI is sometimes confused with ‘AI agents,’ but these technologies differ.

Agentic AI workflows are mostly deterministic sequences. The AI executes predefined tasks within clearly designed bounds. It is designed for cases where outcomes should be adaptable but controlled (for instance, invoice processing with human review)

AI agents are autonomous systems that plan, schedule, execute, and refine tasks independently based on goals. They can prioritize tasks and make decisions without direct human oversight (and with a lot of improvisation). For example, an AI agent can help develop a software app with minimal instructions.

Most businesses don’t need fully autonomous AI agents and may only need agentic workflows.

Agentic AI performs tasks and adjusts execution paths in real time based on feedback and contextual changes. Again, the output stays within a predictable (set) range, meaning it doesn’t improvise as randomly as autonomous agents.

Despite these limitations, agentic workflows still analyze conditions continuously and select what they believe is the optimal course of action. To allow this, agentic AI employs these technologies: 

• Machine learning (ML) models allow the AI to recognize patterns in larger datasets and predict likely outcomes. Another distinct characteristic is the ability of ML algorithms to train on data, making workflows more accurate over time.
• Natural language processing (NLP) interprets human language, extracting intent and insight from user input.
• Large language models (LLMs) are ML-powered models trained on vast datasets that can recognize and generate information: synthesizing, resolving ambiguities, generating plans, adjusting task sequences, and communicating with users.
• Robotic process automation (RPA) handles routine and transactional actions based on predefined conditions.
• Computer vision and sensors provide live visual data and readings, including temperature, vibration data, and patient signals.
• Cloud computing provides processing power for data analysis and task execution, real-time data storage, and remote accessibility to agentic AI.

These AI tools wouldn’t truly be sophisticated enough to be considered agentic without these technologies.

Agentic AI comprises components that enable adaptability, efficiency, and scalability.

• Memory and context management. Stores historical interactions, system states, and outcomes that the workflows reference in future actions. For instance, an agentic workflow could recall that a customer previously contacted support about the same issue.
• Automation frameworks. Define how tasks are structured, sequenced, validated, and marked complete. They orchestrate the flow of operations and, if applicable, other AI tools and agentic models, ensuring each component works in concert toward the goal.
• Context awareness. Interprets data from operational environments to adapt and personalize completion tactics.
• Decision-making algorithms. Weighs risks, prioritizes actions, predicts outcomes, and chooses the optimal path. While decisions are bound to limits, algorithms should react intelligently to situations within those boundaries.
• Reflection. Assesses whether the goals were met and what anomalies were detected during execution. Agentic AI can devise methods to act more quickly, provide more accurate or relevant results, or identify better error-handling options.

Understanding these technical aspects and components can help us grasp agentic AI workflow patterns. 

Behind the scenes, agentic AI operates through recognizable processes that keep it controllable and efficient.

• Task reception: The workflow receives the input (request or event trigger) that signals a task that needs to be executed. These can come from an external API, system notification, or a user command.
• Interpretation and planning: The workflow identifies the objective behind the task by parsing the task’s description, instructions, and metadata. An agentic AI then decomposes the task into smaller, manageable subtasks.
• Context gathering: The system gathers real-time contextual information before deciding how to proceed with the subtasks. This data can come from IoT networks, CRM systems, and other business software.
• Tool selection: Agentic AI chooses which internal or external tools (APIs, RPA bots, software modules, and cloud services) it needs to execute each subtask.
• Decision-making and execution: It executes subtasks, making decisions at each point based on current conditions, feedback, and progress toward the goal. The system prioritizes them based on their impact and urgency.
• Error handling and recovery: During execution, the workflow continuously monitors task outcomes and detects anomalies. Unexpected errors can lead to fallback plans, and exceeding risk thresholds (like suspicious activity detected during a password reset) can trigger escalation to human teams.
• Result compilation: Finally, the AI compiles a report, generates an audit trail, and reviews the performance. The results are analyzed to improve over time.

Correctly implemented agentic workflows can improve several organizational aspects of a business. Let’s take a closer look at how this pans out.

Agentic workflows in AI are seen as an opportunity to generate more value by 36% of respondents to the 2025 Global AI Survey. Some of the benefits the technology delivers to companies include:

• Less time wasted on redundant activities. Agentic AI workflow automation with goal-bound predictability allows your employees to spend less time on tracking, handoff, and other tasks barely related to their job. For example, 66% of customer service representatives dedicate time to activities not related to customers (per Salesforce). Agentic AI can streamline these processes, helping reduce administrative headcount.
• Scalable operational efficiency. Once deployed, agentic workflows can execute thousands of concurrent processes. Indeed, 55% of the 2025 UiPath Report respondents cite automation as their favorite benefit of agentic AI. If deployed on cloud computing platforms with a modular design, companies can scale without massively expanding the headcount and infrastructure.
• Improved business oversight. Agentic workflows can approach tasks in many ways, but they always have boundaries, allowing managers to retain control over decisions and accountability. In the report above, 58% of companies noted that agentic AI improved the oversight of business workflows.
• Enhanced user experience and loyalty. Workflows can promptly address a wide range of customer issues. The key way agentic workflows differ from traditional AI workflows is the ability to retain more context and chain together decisions. According to the SS&C Blue Prism 2025 Global Enterprise AI Survey, 38% of companies believe that adopting advanced AI technologies, such as agentic AI, will improve the customer experience.
• Better employee experience and retention. Freeing up employees from robotic tasks makes them more productive and less liable to burn out. With mundane work automated by agentic AI, your team’s morale will only improve. About 64% of employees believe that these technologies will provide them with new career opportunities and a better work-life balance.
• Proactive risk and exception management. Agentic workflows include conditional logic that enables exceptions, anomalies, and edge-case routing. A predictive maintenance workflow can pause a machine before failure, and cybersecurity workflows can temporarily deactivate a user account based on suspicious patterns.

For these advantages to materialize and translate into returns, businesses must understand what they want to accomplish by integrating agentic AI.

We’ve compiled examples of how businesses from various industries can implement agentic AI across departments. 

HR management and onboarding

AI-enhanced workflows automate resume parsing, interview scheduling, and onboarding checklists. Agentic AI can screen CVs based on set criteria and plan interviews with shortlisted candidates. During onboarding, the workflow can guide the employee through the company’s documentation and initial setup tasks or handle routine inquiries.

Example

Achieve integrated agentic AI into Slack to allow employees to self-service IT support, automating many processes and 95% of password resets.

Financial management and fraud detection

Agentic workflows employ ML, RPA, and LLM to process large amounts of invoices, expense approvals, compliance reports, and other financial documents. This technology also processes transactional data in real time to identify anomalies and potential fraudulent activities with greater speed than traditional methods. Agentic AI also learns from new data, which helps it identify subtle deviations in time.

When successful, 53% of financial organizations report that AI has solved their key problems, and 40% believe it has delivered a strong ROI (according to the Global Enterprise AI Survey 2025).

Example

The agentic AI capabilities of Oracle Financial Services allow companies to automate various financial operations and customer services. It can also investigate and document traces of financial crime.

Cybersecurity and threat response

AI agentic workflows monitor network traffic, user actions, and system logs for anomalies that may indicate security breaches. Upon detecting a threat, the agent can initiate a predefined response protocol, such as isolating affected systems and alerting security personnel.

ML and pattern recognition are used to adapt to evolving threats. The agentic workflows can scan public databases of exploits and learn from each incident in your company, which allows it to identify zero-day exploits that conventional methods might miss.

Example

Big players like Deloitte and CrowdStrike use NVIDIA’s tech stack and agentic AI to speed up software security updates and vulnerability management, as well as reduce alert triage times.

Customer service automation

Customer service tasks typically follow strict policies and have predefined outcomes. This makes agentic AI nearly ideal for processing repeatable tasks, such as feature explanations, billing queries, routing tickets, account resets, order tracking, and refund issuing.

Workflows can cross predefined thresholds or reach conditions that can trigger specific events. For example, repeated complaints, billing disputes, or a sudden change in customer sentiment will make the AI escalate the ticket to human employees. 

Example

Vodafone has currently integrated agentic AI to provide context-aware actions across channels, as well as to predict and minimize service disruptions.

IT service management automation

Agentic AI can process routine service requests, such as password resets and account unlocks. Additionally, agentic AI can help ensure consistent software provisioning for IT operations teams.

When an employee requests access to specific resources, the agentic AI verifies whether the request complies with company policies (such as being made from an approved device or location) and checks for the necessary permissions. Should discrepancies arise, the AI can either resolve them or escalate to the appropriate personnel.

Example

ServiceMax uses a combination of agent-based AI assistants for IT technicians. For example, one agent converts the human question into prompts, the other one fetches and processes data in documents, and the third one answers the question.

E-commerce and retail management

In e-commerce and retail, agentic AI personalizes recommendations, marketing messages, and promotions to customers based on their behavioral data. The behavioral data that impacts the personalization can be anything from browsing history and purchase patterns to demographics and social media interactions outside the platform.

Agentic workflows can also adjust inventory levels, reorder stock, and manage the supply chain to prevent stockouts or overstocks. It can also price the items dynamically based on real-time demand, competitors’ pricing, trending events, and seasonal fluctuations.

Example

Amazon utilizes an agentic workflow in AI to send reminders for abandoned carts and provide image-based recommendations, which account for approximately 35% of the company’s revenue.

Healthcare patient management

Healthcare organizations use agentic AI under the purview of licensed medical professionals. For example, it automates routine patient interactions, allowing for scheduling appointments, managing prescription refills, providing billing information, and answering frequently asked questions. 

Agentic workflows can update electronic medical health records (EHR) based on healthcare visits, treatment results, patient data from wearable devices, and unstructured (or hand-written) clinical notes. Some clinics use agentic AI that analyzes patient health indicators to make diagnoses or detect early stages of chronic conditions. 

Over 34% of healthcare providers consider that AI-powered agentic workflows will improve patient experiences, and 42% expect them to enhance the quality of patient care.

Example

Google’s agentic AI is used by several companies for disease diagnosis and treatment planning. It also boasts an 85.4% sensitivity rate for skin cancer cases.

Manufacturing and supply chain optimization

Organizations can configure agentic workflows to monitor and analyze machinery through IoT devices and analyze data to identify issues. Companies use them to simplify scheduling maintenance tasks and predict malfunctions, allowing for repairs to be made during off-hours before they disrupt production.

On top of this, agentic AI handles supply chain management tasks that require complex coordination. Systems determine the optimal delivery routes and 3PL vendors based on variables such as traffic conditions, weather forecasts, shipment statuses, and historical data, including the transportation company’s pricing, feedback, and delivery times.

Example

Surgere integrated agentic AI into the analytical platform to help companies automate shipping lane assignments, prevent unnecessary shipments, relocate materials, and perform task-based reporting.

Like most digitalization initiatives, agentic AI offers transformative possibilities for your organization. Success requires thorough, realistic expectations, thorough planning, and a practical implementation strategy. We recommend following these steps:

• Identify and prioritize high-impact processes for agentic AI. Look for current workflows that consume excessive time or require significant resources. These could be inventory restocks, interview shortlisting, compliance reporting, or customer support.
• Assess infrastructure and data readiness. Verify whether your current system supports third-party integrations and allows for real-time data flow without compatibility issues.
• Select the optimal AI framework for agentic workflows. Consider how efficiently an AI system interacts with other AI tools—if it can autonomously call APIs and run database queries, if the decision paths are editable, and whether it can learn and improve on past performance.
• Define data governance and policies. Establish standardized fields and metadata, set human approval thresholds (when human intervention is required), configure data filtering rules (to avoid duplicate entries and noise), and establish compliance flows.
• Build an implementation roadmap with key success criteria. Determine what key performance indicators and benefits you expect from the implementation. Keep the criteria measurable to understand if the agentic AI meets stakeholders’ expectations.
• Consider how to deploy agentic AI. You can assign workflows to specific business areas, group them into roles based on similar activities, and tailor each of these roles to use specific tools or handle domains. 
• Start with a pilot project and scale. Launch a minimum viable product or a pilot project to validate your assumptions and monitor the performance. If the pilot proves valuable, monitor and expand based on feedback. 

The AI workflow will require continuous refinement after implementation. Your IT teams should carefully monitor deconstruction logic, tweak prompts, and refine learning datasets. 

As technology is evolving at an increasing pace, you may need to consider incorporating new implementations into your workflows as appropriate.

In 2024, fewer than 1% of business apps used agentic AI. By 2028, this is expected to reach around 30%, according to Mordor Intelligence. The market will grow from $7.28 billion in 2025 to $41.32 billion by 2030.

As these workflows transition to standard operating models, several trends are shaping the direction of the technology.

• Positive sentiment is rising among customers. Around 20% of users in the US and Germany said they would trust agentic AI to handle tax prep or complete official forms, such as tax filing and application completion.
• Security will remain a serious barrier. Agentic workflows require real-time access to sensitive operational data and must adhere to data privacy laws like GDPR, HIPAA, or SOC 2. So, companies will have to invest in advanced cybersecurity and compliance tools.
• The automotive industry is pushing boundaries. These companies use agentic workflows for a multitude of processes: from streamlining interactions about vehicle maintenance to processing IoT data to make decisions on the road. Agentic AI workflow examples include Waymo’s and Tesla’s autonomous driving systems. 
• Edge computing is essential for speed. Distributed computing frameworks bring data sources and AI agent systems closer to analytical platforms, facilitating seamless integration. What this means is the system takes less time to make decisions in critical moments (like in the aforementioned autonomous driving). 

Companies shouldn’t ask whether agentic AI is worth deploying, but how fast they can do it. However, remember that some challenges and barriers remain, and the technology is far from flawless. To avoid expensive mistakes, thorough planning is essential. 

The key is to view agentic systems as ecosystems of capabilities, rather than isolated solutions. After implementation, companies can refine the algorithms, teach the system new data, and add new tools. That, however, requires an experienced technical team.

We at DevCom provide agentic AI development services that can empower your team and shave off significant operating costs. Have specific requirements? We offer tailor-made solutions for industries like healthcare, banking, logistics, retail, and more. Contact us if you want to learn more.