Agentic AI workflows: Trends, examples and best practices

How do agentic AI workflows work?

Agentic AI workflows fall under the broader umbrella of agentic process automation, a subset of business process automation (BPA). Automating manual workflows can offer significant benefits; traditional workflows often use resources inefficiently, fail to adapt to new conditions and go years without meaningful updates.

Agentic workflows rely on machine learning techniques, particularly large language models and natural language processing. In addition, robotic process automation can support routine agentic workflow operations using rules or guidelines.

Raw data to drive an agentic AI workflow can come from many different sources, such as IoT fleets, computer vision devices and medical monitoring devices. Finally, the storage and compute resources needed to process agentic AI workflows and implement their decision-making can be challenging for local data centers, so cloud computing is often the preferred platform for agentic AI systems.

These technologies enable agents to gather and interpret information, respond to user requests, understand business context and engage with human workers and other IT systems. Agentic workflows can also adapt in real time to changing conditions, optimizing performance continuously.

Agentic AI has garnered serious attention from IT executives. A 2025 UiPath agentic AI report noted that 93% of U.S. IT executives were extremely or very interested in agentic AI, while 32% said they plan to make agentic AI investments in the next six months or less.

Phases of agentic AI workflows

Agentic AI workflows typically unfold in four broad phases:

1. Task analysis. The AI agent analyzes existing workflows and identifies the tasks required for execution. It usually interoperates with existing IT or BPA systems to visualize and map these tasks.
2. Task creation. Based on its analysis, the agent builds automation elements to carry out workflow tasks. It can then delegate these tasks to subagents -- for example, a pricing agent in an e-commerce system or a customer service agent on a help desk platform.
3. Task execution. All involved agents work together to execute the workflow. If issues arise, errors are flagged for review and remediation. If the workflow is completed successfully, agents can log execution details in a management dashboard for human review.
4. Assessment and adaptation. The AI agent gathers workflow results, performance metrics and UX feedback to evaluate effectiveness. It can then identify opportunities to improve efficiency, reduce costs and optimize the workflow.

Core components of agentic AI workflows

Agentic AI systems are composed of software elements designed to manage workflows and drive business outcomes. A typical agentic AI workflow includes five core components:

1. Context awareness. Every agent within an agentic AI workflow maintains the context of the task being performed. Context can be established through user prompts, data types and user or business customization choices.
2. Memory. The agentic AI system remembers the interactions, system states and agent outcomes along the workflow. These memories influence future agentic workflows. For example, an agentic workflow can remember that a user made a similar request or experienced a similar problem in past interactions and can adjust its responses or task execution accordingly.
3. Automation and orchestration. Automation focuses on executing individual tasks, while orchestration focuses on organizing those tasks into a cohesive workflow. These capabilities enable the agentic AI system to define task structures, establish proper sequences, validate the relationship to the intended goal and track the execution of each task throughout the workflow -- including interacting with resources and services, such as enterprise databases.
4. Decision-making. Agentic workflows can result in definitive decisions for the enterprise. Decision-making algorithms can set priorities, evaluate risks, predict outcomes and select the most appropriate workflow path to achieve the intended outcome. Decision-making algorithms typically include rules or guardrails that prevent excess risk and can call for human intervention when additional clarity is required.
5. Learning. The agentic AI system can evaluate a workflow's outcome compared to its intended goal, consider any deviations between the two and examine any human input. This enables agentic workflows to learn from past actions and experiences, systematically developing faster, more accurate and more relevant results in the future.

Benefits of agentic AI workflows

Agentic AI workflows build on many strengths of traditional automation and orchestration techniques. According to the UiPath report, 37% of respondents said they already use agentic AI for business use cases.

Agentic AI workflow benefits include the following:

• Efficiency. Agentic workflows can automate more complex and nuanced tasks than standard BPA systems, reducing bottlenecks, accelerating approvals and handling a wider range of variations.
• Security. Agentic workflows can incorporate security measures and considerations -- such as risk assessments or compliance obligations -- into workflows and decision-making. This can enhance security and mitigate foreseeable business and operations risks.
• Productivity. The automation and autonomy offered by agentic workflows can handle many routine, time-consuming and error-prone tasks that humans currently handle or require significant human intervention. This frees skilled staff to focus on more strategic or valuable business tasks.
• Accuracy. By collecting and analyzing more data -- often in real time -- agentic workflows can support more informed decisions, detect anomalies and surface insights that conventional tools might miss.
• Continuous improvement. Agentic AI workflows can learn from past actions and outcomes. This enables them to refine and optimize workflows for faster, more efficient, more accurate and more relevant outcomes in the future.
• Agility. Agentic workflows can adapt dynamically to shifting business conditions, realigning to new goals with little, if any, human interaction.
• Scalability. Agentic systems can distribute tasks across subagents to handle enormous volumes of work without sacrificing performance.
• Cost savings. Agentic workflows can reduce operational costs compared to their manual counterparts by executing large-scale tasks more quickly and efficiently.

Limitations of agentic AI workflows

However, despite these benefits, agentic AI workflows also present several challenges:

• Infrastructure. Agentic systems require significant computing infrastructure -- including servers, storage and networking -- to support real-time data processing and decision-making.
• Complexity. Agentic workflows can be complex environments to automate and orchestrate, especially as business needs and conditions change over time. This can create performance problems, bottlenecks and poor outcomes that can be particularly difficult to troubleshoot.
• Data quality. Agentic workflows are only as good as their data. Incomplete, biased or otherwise low-quality data can degrade outcomes. Agentic workflows should also adhere to ethical data use standards, especially in high-stakes contexts such as hiring and healthcare.
• Integration. Agentic workflows often build on existing workflows, requiring integration capabilities that can raise issues around data accessibility, interoperability and scalability.
• Security. Because almost all corporate data sources are subject to oversight, strong safeguards are essential to meet governance and compliance requirements. Security remains a strong concern for agentic AI: The UiPath report noted that 56% of respondents cited IT security concerns in adopting agentic AI.
• Explainability. As agentic workflows become more autonomous, their decisions can be harder to explain. This opacity and risk of unintended consequences complicate troubleshooting, compliance and user trust.
• Expertise. Designing, implementing and maintaining agentic workflows demands highly specialized knowledge in AI, data sciences and software development practices. Organizations might lack the staff expertise to deliver a cost-effective agentic AI system.

Agentic AI workflow use cases

Agentic AI workflows have broad applicability across industries:

• Help and support. Agents can personalize responses to customer questions using contextual understanding, customer history and business logic. They can also predict customer needs and trigger support actions.
• Manufacturing. Agents can streamline production, inventory management and warehousing by responding to sales orders, stock levels and user requests. They can also monitor manufacturing equipment in real time and predict failures to enable preventive maintenance.
• Finance. Agents can identify potential fraudulent transactions in real time and perform detailed risk assessments for loans and insurance by analyzing applicant data, market trends and banking records.
• Healthcare. Agents assist with real-time patient diagnoses based on symptoms, test results, medical imaging and medical histories. They can also generate treatment plans that consider insurance coverage, medication availability and provider schedules.
• Logistics. Agents can recommend inventory management based on demand forecasts and stock levels. They can also optimize delivery routes using data on traffic, weather, carrier location and vehicle condition.
• Government. In smart cities, agents can monitor traffic, manage power distribution and detect signs of emergencies.

Best practices for agentic AI workflows

Agentic AI adds insight and autonomy to decision-making -- but effective implementation requires careful planning. Best practices for agentic workflows include the following:

• Define clear goals. Goals serve as benchmarks for evaluating agents' performance, so setting well-defined objectives is critical.
• Use specialized agents. Like human workers, AI agents have unique focuses, strengths and weaknesses. Assigning the right agent to the right task is essential.
• Foster collaboration. Although specialization matters, agents must also work together, synchronizing their actions and output to ensure transparency and reliability.
• Keep humans involved. Collect meaningful metrics and KPIs using alerts, reports and real-time dashboards, which human managers can review to ensure that AI actions continue to meet business needs.
• Ensure data and AI governance. Agentic workflows rely on large volumes of data. Document and validate data sources, and create agentic AI governance policies to enforce transparency, oversight and security across the enterprise.

How will agentic workflows affect the future of AI?

Experts project that agentic AI will become a significant part of enterprise automation. A Mordor Intelligence report noted that the agentic AI market is valued at $6.96 billion in 2025 and is expected to reach $42.56 billion by 2030.

Given the extensive attention and aggressive growth forecasts for agentic AI, there are several potential effects that agentic workflows could have on the future of AI.

More autonomy

As agentic AI workflows gain more autonomy and adaptability, AI agents could make more sophisticated and important decisions, solve more complex problems, conceive larger multistep tasks and dynamically adapt to changing data and circumstances with less human oversight than current implementations. This can make agentic workflows more resilient and responsive in unpredictable situations.

More productivity

Agentic AI systems can generate and follow workflows continuously, reducing errors and bottlenecks to improve performance and accuracy in essential business tasks. As agentic workflows improve their ability to optimize resource use and develop refined outcomes, agentic AI will lead to more cost savings and greater utility.

More interoperability

Agentic workflows require extensive integration to support interoperability between AI agents and the resources and services needed to make decisions. This interoperability could improve over time, gradually making agentic workflows more frictionless and plug-and-play for organizations to adopt and deploy.

Stephen J. Bigelow, senior technology editor at Informa TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.