Agentic workflows: A complete guide for enterprises.
Agentic workflows, or agentic processes, put AI agents to work for task management and automation, bringing flexibility, responsiveness, and a degree of independence to business processes.
AI agents employ technologies such as large language models (LLMs), machine learning, and natural language processing to understand context, interpret information, and interact with other systems and users. As they operate within workflows, they can adapt to new inputs and changing circumstances in real time, continuously learning from their experiences to improve their performance. This allows organizations to handle tasks as dynamic entities, driving new levels of efficiency and enabling agile response to evolving business needs.
Agentic workflows vs. traditional workflows.
Traditional workflows take their toll—if you thought waiting on hold was tiring, it’s nothing compared to the circus of systems, copy-paste sequences, and authentication hoops customer service teams navigate on the other end of the line, over and over again. Predictable yet too complex to hand off easily to simple automation, traditional workflows consume valuable resources, time, and energy as human workers make decisions reflecting the nuances of in-the-moment business or customer needs to fit process pieces in at the right angle to complete the overall process puzzle.
While automation tools like robotic process automation (RPA) can execute repetitive tasks based on fixed rules and linear processes, driving improved efficiency, they fall short when work requires adaptability and quick decision-making. This is where agentic workflows come into play, enabling enterprises to bring unprecedented agility to task management and automation.
This article aims to provide a foundational understanding of agentic workflows, their benefits, and applications to help shape your next steps toward embracing this innovative technology.
What are agentic workflows?
Agentic workflows represent sequences of well-defined jobs to be done that are executed dynamically by AI agents and orchestrated as part of a larger end-to-end business process automation. In this way, agentic workflows are the application of AI agents to process automation. They organize tasks, assign work to specialized agents, and facilitate collaboration between agents and humans to improve output.
Sometimes referred to as agentic processes or AI-agent-enabled processes, agentic workflows harness the ability of AI agents to reason, problem solve, choose a course of action, and execute these actions. Unlike existing applications of AI within automated workflows that are used to analyze data and inform decision-making, AI agents are designed to take action based on their analyses, making decisions and adapting processes to changing circumstances in real time.
In short, the step-change of agentic workflows compared to existing approaches to automation is that AI agents have the agency to treat tasks as the dynamic entities that they are. This agency refers to the capacity of AI systems to make independent decisions, adapt to changing circumstances, and execute tasks without constant human intervention. These systems—AI agents—serve as the intelligent drivers of agentic workflows; they can analyze data, make decisions, and execute tasks without constant human oversight.
The role of AI agents
At the core of agentic workflows are AI agents, which act as the intelligent drivers of automated systems. AI agents empower a high degree of workflow flexibility. They are able to formulate plans, assess progress, and adjust course as they execute tasks toward a complex goal.
AI agents combine the cognitive capabilities of generative AI with action and automation across systems and data. What this means, in practical terms, is that AI agents can use the cognitive capabilities of large language models (LLMs) to make decisions, learn from data and context, interact with humans through natural language, and ultimately take action to achieve defined goals. The robustness of the underlying models determines the agent's capacity to perform complex tasks accurately and efficiently.
For example, back to our customer support scenario, an AI agent can handle inquiries by understanding the context of a customer’s request, accessing relevant data, and providing appropriate responses. AI agents can shorten call handle times by supporting human agents with automated execution of related workflows, such as ordering a replacement part or even handling common inquiries independently from start to finish.
Evolution to complex agentic workflows
Until the emergence of LLMs, responsive AI systems were notoriously difficult to implement. Relying on complex rule-based programming or intensive in-depth training of machine-learning models, first-generation agentic workflows were few and far between. With LLMs, AI agents have pre-built engines to power their ability to respond to inputs and prompts they haven’t explicitly been trained on, eliminating the need for labor-intensive rule-based programming.
Instead, anyone can prompt a generative AI model, such as via ChatGPT, for a zero-shot response—where the model completes the entire task from start to finish the moment you hit enter, without pausing to plan, review, or reference other information. While models perform remarkably well in this scenario, attaining around 50% output accuracy, layering in multi-shot prompting and retrieval augmented generation (RAG), among other techniques, can significantly improve performance.
However, the leap toward agentic workflows comes from chaining prompts and adding function calling (tool access) to enable planning, reflection, iteration, and action—creating AI agents. Effective iteration is often supported by collaboration/feedback loops, where models work together to improve performance by being the checks and balances to each other’s output using techniques such as flow engineering.
Benefits of agentic workflows.
But agentic workflows are not just a technical advancement; they represent the groundwork for a paradigm shift in enterprise operations. Agentic workflows extend the gains offered by existing automation tools by delivering more adaptable, scalable task and process support. What does that look like in terms of business benefits?
Increased efficiency
Taking the efficiency gains of automation to a new level, agentic workflows can take on complex yet repetitive tasks, such as report generation, which would typically take a non-trivial amount of time and resources to complete. For example, in finance operations, agentic workflows can process invoices and manage approvals in a fraction of the time by executing continuously and at a higher speed while reducing bottlenecks, leading to faster turnaround times.
Enhanced decision-making
Agentic workflows empower organizations to make data-driven decisions with greater confidence. AI agents are capable of analyzing vast amounts of data in real time to identify patterns, generate insights, and deliver recommendations to stakeholders.
For routine decisions, agents can act autonomously, such as choosing the appropriate risk mitigation action for a cybersecurity threat and executing the associated action, such as isolating servers. This capability enables businesses to respond more quickly to market or operational changes as well as enables more informed decisions based on the most current and relevant information.
Improved accuracy
Agentic workflows reduce the likelihood of errors by employing the combined abilities of AI and automation to execute tasks with consistency and high accuracy. They ensure that discrepancies are identified and addressed right away, either by an AI agent within the workflow itself or by triggering human-in-the-loop processes to surface exceptions for human review and input. The result is improved accuracy, which enhances data integrity and builds trust in the information being used for decision-making.
Increased agility
Agentic workflows are responsive to real-time changes in their environment, able to adjust actions and pivot to align with new parameters or priorities. This brings a new level of flexibility to automated processes, supporting operational agility and resilience.
Scalability
Agentic workflows are inherently scalable, capable of handling large volumes of work without compromising quality or efficiency. AI agents can manage increased workloads by distributing tasks intelligently and optimizing resource allocation. For example, in an e-commerce context, employing AI agents to manage customer inquiries, process orders, and track inventory levels ensures continuous service levels even when demand spikes occur, such as before major holidays like Christmas.
Cost savings
Following in the footsteps of Intelligent Automation, agentic workflows can deliver significant cost savings for organizations by enabling an expanded horizon of process automation and better allocation of resources while reducing error and improving operational efficiency. For customer operations, McKinsey estimated more than $400B in productivity uplift alone.
Practical use cases for agentic workflows.
While the projected value of generative AI across industries and functions is immense, translating the potential into on-the-ground impact requires understanding relevant real-world applications of agentic workflows. As more organizations adopt agentic workflows, the potential for innovation and growth continues to expand.
Human resources management
Agentic workflows for HR departments can accelerate and improve talent management processes, including recruitment, onboarding, and performance management. AI agents within agentic workflows can automate screening resumes to identify qualified candidates and schedule interviews, not only accelerating the filling of the talent pipeline but also reducing possible biases that may be associated with traditional candidate screening.
In onboarding, AI agents can act as interactive guides for new employees, assisting with navigating training modules, completing necessary forms and enrollments, and answering common questions.
Project management
Traditional project management typically relies on manual updates and communications, which can exacerbate any misalignments and contribute to delays. Applying agentic workflows can automate project management tasks like monitoring project progress in real time, providing updates when milestones are reached or alerts and reminders when potential bottlenecks arise, and re-assigning tasks to balance workloads and use resources effectively.
This level of automation not only enhances productivity but also allows project managers to focus on strategic decision-making rather than administrative tasks. For example, in a software development environment, an AI agent can track the progress of development tasks and assign resources as needed to meet deadlines.
Customer support
The deployment of AI agents in customer support is one of the most visible applications of agentic workflows. Agentic automation is already at work for routine inquiry handling, assisting and accelerating service agents, and providing personalized recommendations. Natural language processing (NLP) enables AI agents to understand and respond to customers and service staff in real time.
AI agents can handle many common use cases in customer service, from answering frequently asked questions to assisting with order tracking, to surfacing expert-level knowledge to new service staff. When complex issues arise that require human intervention, AI agents can escalate these cases to human agents, ensuring customers receive the right level of support. This hybrid approach not only improves response times but also allows human agents to focus on resolving sensitive and uncommon problems, boosting overall service quality.
Finance
Because finance operations involve large data sets and rapid, accurate decision-making, agentic workflows are an obvious value-driving opportunity—AI agents can process large datasets to identify trends and inconsistencies and assess potential risks by analyzing historical data and current business conditions to inform financial decisions. This proactive approach enhances the organization’s ability to make informed decisions, respond to market fluctuations, and minimize potential losses.
Another example of agentic workflows in finance is supplier discount negotiation. AI agents can review unstructured agreements and trend data across finance operations, compare terms, and recommend actions, as well as negotiate next steps. Yet another application of agentic workflows in finance is for SOX controls—AI agents can automatically check company transactions against SOX controls to identify violations, generate detailed reports for the audit team, and notify stakeholders.
Tax optimization is another immediate opportunity to apply agentic workflows. AI agents can understand tax laws, predict tax liabilities, and plan strategies to minimize tax obligations. The benefits can often be seen immediately, in the case of Petrobras, returning $120 million in tax savings in just three weeks.
Key components of agentic workflows.
Agentic workflows are built on a foundation of Intelligent Automation, which enables enterprises to create secure, governed AI-driven automated processes. The key components of agentic workflows—RPA, NLP, AI agents, workflow orchestration, and integrations—work in synergy to create dynamic, automated processes.
Robotic process automation (RPA)
RPA tools enable organizations to create software robots that mimic human actions, such as data entry, transaction processing, and report generation. RPA is a key component of agentic workflows, allowing AI agents to accurately and reliably execute rule-based tasks across applications. Within an agentic workflow, RPA might be used, for example, to automatically input invoice data gleaned by an AI agent from unstructured invoice document data into an accounting system.
Natural language processing (NLP)
NLP enables AI agents to understand, interpret, and respond to human language in a way that is both meaningful and contextually relevant. This capability makes interacting with AI agents a seamless user experience, eliminating the need for specialized training or programming knowledge. NLP is essential for agentic workflow tasks in customer support, content generation, and sentiment analysis. NLP is part of the underlying agentic workflow toolkit that enables creation of conversational agents, or chatbots, that engage with users in real time.
AI agents
AI agents are the intelligent entities at the heart of agentic workflows that perform complex tasks, make decisions, and learn from their interactions. AI agents are built and run on a semantic engine, usually a large language model (LLM). The semantic model/LLM foundation provides the ability to reason, plan, and reflect as well as provides a conversational interaction layer so that the agent can seek clarification or approval of work.
To get work done, AI agents incorporate function calling, or tool use, to enable them to connect to systems, run automations, search for information—and more. This is why the automation layer that agents are built on is so critical—it provides a secure and robust set of actions for AI agents to complete their goals.
Workflow orchestration
Workflow orchestration is the process of coordinating and managing the various components and tasks within an agentic workflow. This involves defining the sequence of actions, managing dependencies, and ensuring that tasks are executed efficiently and at the right time.
Workflow orchestration tools provide a visual interface for designing and monitoring workflows, making it easier for organizations to manage complex processes that integrate multiple technologies and systems. For a marketing workflow, that might look like connecting leads coming in from social media campaigns to customer relationship management (CRM) systems and triggering automated lead assignment workflows to ensure sales opportunities are acted on right away.
Integrations
Integrations and APIs are central to creating agentic workflows to connect different systems, applications, and technologies to enable seamless communication, data exchange, and task execution. Agentic workflows require connecting with applications and platforms to get work done and ensure a cohesive process, such as connecting AI agents with RPA tools integrated into CRM systems to automate information updates to provide sales and support teams with the most current customer information.
Best practices for building agentic workflows.
Building effective agentic workflows is similar to developing any business process automation in that following best practices goes a long way toward ensuring their efficiency and effectiveness. Advanced automation platforms that offer AI agent creation with best practices already embedded provide a simplified, accelerated path to building agentic workflows.
Define clear, specific goals
The goal you want to achieve is the common purpose that AI agents involved in the workflow will align to. Clearly and explicitly defining this goal not only helps measure the effectiveness of the workflow; it also guides the design and implementation of the agents themselves.
For example, if the goal is to improve customer satisfaction through faster response times, the workflow should be designed to prioritize tasks that enhance customer interactions. This alignment ensures that every choice and subsequent action taken by the AI agents contributes to the overall objective.
Tap into the strengths of AI agents
Robust agentic workflows harness multiple specialized AI agents, each with different strengths and skill sets.
Not unlike human employees, each agent will have core capabilities to collaborate, communicate, and coordinate with one another and with human users while embodying task-specific skills and underlying (model-driven) strengths that are both different from each other and diverse in their range. Recognizing the opportunity to harness specialized AI agents is key to building effective agentic workflows that involve multiple complex tasks.
In a healthcare setting, an analytic AI agent could analyze patient data while a calendaring agent manages appointment scheduling. Combined, they contribute to an overall patient care workflow.
Keep humans in the loop
Agentic workflows require striking a balance between agentic capabilities and oversight. Build agentic workflows to keep humans involved and validate AI decisions so actions and outputs align with business goals.
Lean into multi-agent collaboration
Designing workflows that support collaboration among multiple AI agents is vital for tackling complex, multi-step processes. By enabling agents to share information and coordinate their actions, organizations can create more resilient, efficient agentic automations.
In a supply chain management scenario, a stock tracking agent could monitor inventory levels while another manages supplier communications. Connecting them not only through orchestration but also enabling them to collaborate directly can create a more agile workflow that can respond on its own to changes in demand or disruptions to the supply chain.
Employ rigorous data governance
Data transparency underpins the trust and safety of agentic workflows. Track data interactions and apply metadata to create accountability. Metadata enables building audit trails starting from the origin of data through every activity, access, and transformation in the context of where and when it was used. Support data privacy and compliance with clear policies and standards for data management.
Examples of agentic workflows.
Supply Chain: Backorder Processing
Efficient backorder processing contributes to operational efficiency and customer satisfaction. Existing backorder processing workflows automate disparate tasks such as order updates and inventory checks, with the aim of responding quickly to provide alternate options, which helps to retain customers and protect revenue.
Typical process
For a new backorder case, an automated system opens the case and sends an email notification to the customer service team. An employee reviews the email and accesses the case in the CRM. They verify the details and consult the ERP system to identify potential replacement products. The employee then initiates an automation that checks warehouse inventory for available alternatives. Once a suitable replacement is found, the employee finalizes the replacement order, triggering the fulfillment process workflow, which involves additional employees in shipping and logistics.
Agentic workflow
When a backorder case is opened, it is immediately picked up by a review agent to update associated systems such as the CRM and validate information and backorder details, communicating directly with the customer as needed to discuss preferences for replacement products.
The review agent hands off the case to a replacement agent, who identifies potential alternatives, checks inventory levels, and finalizes the replacement order.
The order is then picked up by a fulfillment agent, who coordinates with logistics and shipping teams to execute the fulfillment process
Finance: Invoice processing
Finance teams aim to streamline invoice processing workflows to accelerate payment cycles, strengthen vendor relationships, and improve cash flow management. To date, automating invoice processing tasks, such as data entry, verification, and approval, has helped organizations minimize errors and discrepancies, leading to better compliance and financial accuracy.
Typical process
On receiving an invoice to the AP inbox, an automation tool captures it and enters the data into the invoice management systems, such as SAP, triggering a notification to the accounts payable team for verification. AP reviews the invoice against the agreed terms by toggling between invoicing and contract management systems. For any discrepancies, AP communicates with both the vendor and the internal business owner associated with the purchase order to resolve issues.
Once all adjustments are made, the analyst initiates a payment request which sends an automated message to approvers. After approval, the payment requisition is often entered manually into the accounting system to start the payment processing workflow. When the payment is issued, a separate automation updates related systems to reflect that the invoice was paid.
Agentic workflow
At the outset, an intake agent reviews incoming invoices, verifying the information and ensuring accuracy before creating a payment request. If discrepancies are detected, the agent proactively communicates with the vendor and business owner to resolve issues and confirm details.
Once verified, the payment request is passed to an approval agent, who evaluates it against historical data and the business context of the invoice. The approval agent recommends approval, seeking confirmation from the business owner.
Upon receiving the approval, the approval agent creates a requisition in the accounting system, which is assigned to a payment agent who executes the payment processing workflow, issuing funds and updating business systems to confirm that the invoice was paid, as well as notifying stakeholders and the vendor.
IT: Network Threat Detection
IT teams work to improve network threat detection to safeguard sensitive data, strengthen overall security posture, and maintain business continuity. By implementing monitoring systems, organizations typically automate the collection and analysis of network traffic and threat intelligence. This supports a proactive approach aiming to mitigate risks before they escalate, and reduce the time and resources required for incident response.
Typical process
Networking monitoring systems continuously gather and normalize data from various sources, including network traffic logs and threat intelligence feeds. This data is analyzed by threat monitoring systems and IT security analysts using statistical analysis and machine learning algorithms to identify any anomalies that may indicate security threats.
When an anomaly is detected, an alert is generated to notify the incident response team. Incident response specialists investigate the alerts to assess the validity and severity of potential threats by examining logs, correlating data, and consulting threat intelligence.
When a threat is confirmed, they initiate incident response processes to contain the threat, mitigate its impact, and remove any malicious elements from the environment. Throughout, IT security analysts document findings and any actions taken for compliance and process improvement.
Agentic workflow
Ongoing, a monitoring agent oversees the data collected from network monitoring systems and threat intelligence feeds.
When a potential threat is identified, the agent notifies the security team, and a threat response agent is assigned to the confirmed threat. The agent executes containment procedures and documents every action for accountability and compliance.
An optimization agent collaborates with the threat response agent to evaluate the effectiveness of the containment measures and adjust the security posture accordingly to ensure optimal protection.
Financial Services: Loan Application Processing
In the competitive banking industry, automating the loan processing workflow minimizes errors and compliance risks, ensuring that applications are evaluated consistently and accurately. Overall, effective loan application processing enhances the bank's ability to manage risk, optimize resources, and support strategic growth, ultimately driving profitability and customer satisfaction.
By automating key tasks such as data entry, credit assessments, and underwriting evaluations, organizations can significantly reduce the time it takes to approve loans, facilitating faster access to funds for borrowers. This efficiency not only strengthens customer relationships but also increases the bank's competitiveness in the market.
Typical process
A potential borrower submits a loan application online, via mobile, or in person at a branch. On receipt, the application data is entered into a central loan management system with the help of document automation for initial review by a loan processing analyst. The analyst ensures that all necessary documentation is provided, including identification and income statements.
After confirming completeness, the bank conducts a credit assessment to evaluate the applicant's creditworthiness, which involves verifying income through financial documents and potentially contacting the applicant’s employer. A risk assessment is performed to inform the decision-making process, supported by employing artificial intelligence tools to analyze factors such as debt-to-income ratio and employment history.
When the application moves to the underwriting stage, a loan officer evaluates the application in detail to determine approval or conditional approval. If approved, the bank prepares a loan offer detailing terms and conditions for the applicant's review.
Agentic workflow
An intake agent is responsible for reviewing and verifying loan applications, executing credit checks, and collaborating closely with the loan officer to assess risk.
Once the application is confirmed to be complete, an underwriter agent conducts an in-depth assessment, reviewing all supporting data to recommend appropriate loan terms.
The underwriter makes the final decision regarding the application and initiates a creation agent to prepare the loan offer.
Throughout this process, agents maintain communication with the applicant, ensuring they are informed throughout the process to support customer satisfaction and trust.
Healthcare: Prior Authorization
The prior authorization process aims to minimize delays in treatment and provide access to necessary medical services while ensuring consistency with insurance policies and compliance with medical guidelines. To expedite approvals, automation efforts have targeted key tasks such as request submissions, documentation reviews, and communication with providers.
Effective prior authorization processes contribute to better patient outcomes, improved operational efficiency, and a more streamlined healthcare experience—all central to the overall goals of the healthcare system.
Typical process
A healthcare provider submits a request for prior authorization for specific services, procedures, or medications, accompanied by supporting documentation, such as clinical notes and treatment plans.
On receipt, the insurance company conducts an initial review to confirm that all required information was submitted. If any information is missing, the insurer reaches out to the provider for additional documentation.
The clinical team then evaluates the prior authorization request based on established medical guidelines and the patient’s medical history to determine the medical necessity of the request. Based on their findings and insurance policy coverage criteria, the insurance company decides to approve, deny, or request further information, notifying both the healthcare provider and the patient of the outcome.
Agentic workflow
On receipt of a prior authorization request, a processing agent conducts an initial review and checks that all required information was included. If any information is missing, a communication agent engages with the provider to gather the necessary documentation.
A validation agent then evaluates the request against established medical guidelines and the patient’s medical history, recommending a decision to the clinical team that also takes the insurance policy’s coverage criteria into consideration. The clinical team confirms the recommended decision, and the communication agent notifies both the healthcare provider and the patient of the outcome.
Future trends: The evolution of agentic workflows.
Development and adoption of agentic workflows is still in its early stages. The future of these workflows is being shaped both by continuing innovation and advancements in multiple AI fields, in particular large language models and machine learning, as well as by technological advancements in embedding agentic capabilities within automated workflows.
Multi-agent systems
As organizations tackle increasingly complex processes by employing AI agents, the future of agentic workflows will likely see a greater emphasis on multi-agent collaboration. Connecting multiple AI agents to work together seamlessly appears to be the gateway to optimizing performance across processes and functions. AI agents will share information, coordinate actions, and use collective intelligence to solve problems more effectively.
Ethical AI and governance
The increasing application of agentic workflows underscores the need for ethical AI practices and governance. Organizations will need to establish frameworks that ensure AI agents operate ethically, transparently, and in compliance with relevant regulations. To this end, future developments in agentic workflows will likely include built-in mechanisms for accountability and oversight, allowing organizations to monitor AI decision-making processes and mitigate biases.
Automation Anywhere's role in agentic AI workflows.
While the rise of large language models (LLMs) generated an AI gold rush, the cost, complexities, and uncertainties of applying generative AI within enterprise environments have in fact translated into slower AI adoption. However, integrating AI technologies within a pre-existing enterprise security and governance framework, such as provided by Intelligent Automation platforms, is paving a path to accelerated adoption of agentic workflows.
The Automation Anywhere platform offers a complete suite of cloud-native automation and AI tools and technologies to build and implement agentic workflows, including RPA and assistive AI for low-code/no-code development. Our solutions empower enterprises to harness the full potential of AI and automation.
Agentic workflows represent a significant evolution in how work is performed, offering the potential to create more agile, intelligent, and efficient organizations. Request a demo to experience firsthand how you can create agentic automation solutions to power your business processes, improve efficiency, and move your organization forward.
