Agentic Workflows
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What Are Agentic Workflows and Why Are They Important?
The rise of Artificial Intelligence has changed how businesses think and act. With continuous advancements in automation technologies, agentic AI have emerged as a transformative milestone. Automation has been around for years with numerous industries relying on them optimize processes while reducing reliance on human oversight. But often these technologies lacked contextual awareness, leading to outputs that may not align with specific tasks or enterprise environments. Agentic Workflows resolves this by incorporating decision-making and contextual understanding, enabling more relevant and actionable responses, serving as a foundation for agentic workflows.
These workflows are innovative automation systems that empower businesses to achieve more by seamlessly integrating intelligence into everyday operations. They interact with large language models (LLMs) and other AI tools to handle complex tasks with precision. Unlike traditional methods, these workflows can adapt, learn, and deliver outputs that are significantly more accurate and efficient.
In this blog, we will decode the concept of agentic workflows, explore their core pillars, and learn how businesses can reap its full potential.
What Are Agentic Workflows?
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Agentic Workflows are automation systems that use AI agents to make informed decisions and execute tasks autonomously. These systems don’t rely on rule-based operations and act as virtual co-workers, adapting to dynamic environments, interacting, and freeing up humans to focus on tasks that bring the most value to the business.
Agentic workflows are also referred to as agentic processes or AI-agent-enabled processes, with the ability to reason, solve, choose the course of action, and execute. Unlike traditional AI applications that are utilized to process data and guide decision-making, these agentic workflows are designed to take action in real-time based on their analyses, making decisions and adapting processes to changing circumstances.
This type of workflow is common in systems where agentic AI needs to interact dynamically with its surroundings such as in robotics, game AI, smart manufacturing, and autonomous driving. Other use cases include personalized virtual assistants, adaptive cybersecurity systems, intelligent traffic management, automated financial trading, AI-driven healthcare diagnostics, and predictive maintenance in industrial operations.
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The Difference between Agentic Architectures and Workflows
New technologies often come with a lot of new terms. Two such terms- “agentic architectures” and “agentic workflows”. These might sound similar but mean different things.
An agentic workflow is the set of steps an agent follows to reach a goal. These steps can include using large language models (LLMs) to plan, break it into smaller tasks, use tools like web search to complete those tasks, and reflect on the results to improve the plan.
An agentic architecture is the overall system or setup that helps an agent complete a task. These systems may vary, but they usually include an agent that can make decisions, tools the agent can use, and memory systems to store information for short or long periods.
Agentic Workflows vs. Traditional Workflows
| Basis of Differentiation | Agentic Workflows | Traditional Workflows |
|---|---|---|
| Intelligence | Incorporates AI agents with contextual understanding and decision-making capabilities. | Operates on predefined, rule-based logic with no contextual adaptability. |
| Adaptability | Adapts dynamically to changes in data, tasks, or environments. | Rigid and static; requires manual intervention for changes or updates. |
| Automation Level | Combines intelligent automation tools like RPA and NLP to handle complex, unstructured tasks. | Limited to repetitive, structured, and rule-based tasks. |
| Decision-Making | AI agents analyze data and make decisions autonomously, reducing reliance on humans. | Relies on humans for decision-making and problem-solving. |
| Integration | Seamlessly integrates with multiple systems, APIs, and tools to ensure cohesive workflows. | Limited integration capabilities; often works in silos without smooth cross-system communication. |
| User Interaction | Leverages NLP and conversational AI for seamless, natural user interactions. | Requires predefined input formats and lacks conversational capabilities. |
| Scalability | Easily scalable to handle large volumes of data and complex processes. | Scaling requires significant reconfiguration and manual effort. |
| Learning Capability | AI agents can learn from historical data and improve over time. | No learning capability; outcomes remain static unless explicitly reprogrammed. |
| Error Handling | Identifies and resolves errors autonomously with minimal human intervention. | Errors often require manual intervention to diagnose and resolve. |
| Examples of Use Cases | Customer service chatbots, predictive analytics, automated financial reporting, and decision support. | Invoice processing, basic data entry, and static approval workflows. |
Key Components of Agentic Workflows
Agentic workflows are built on a foundation of intelligent automation, enabling secure and governed AI-driven processes. Here are the key components:
- Robotic Process Automation (RPA): RPA tools create software robots that mimic human actions such as data entry, transaction processing, and report generation. In agentic workflows, Robotic Process Automation (RPA) enables AI agents to accurately execute rule-based tasks across applications. For example, RPA can automatically input invoice data extracted by an AI agent into an accounting system.
- Natural Language Processing (NLP): NLP allows AI agents to understand and respond to human language meaningfully and contextually. This eliminates the need for specialized training, making interactions with AI agents seamless. NLP is crucial for tasks like customer support, content generation, and sentiment analysis, enabling the creation of chatbots and conversational agents that engage with users in real time.
- AI Agents: AI agents are intelligent entities that perform complex tasks, make decisions, and learn from their interactions. Built on semantic engines or LLMs, AI agents can reason, plan, and interact conversationally. They use function calling to connect to systems, run automations, search for information, and complete goals, supported by a robust automation layer.
- Workflow Orchestration: Workflow orchestration coordinates tasks and components within agentic workflows. It defines action sequences, manages dependencies, and ensures tasks are executed efficiently. Orchestration tools provide visual interfaces for designing and monitoring workflows, integrating technologies and systems to manage complex processes seamlessly.
- Integrations: APIs and Integrations: APIs and integrations are like the hands and legs of agentic workflows. They help agents act by connecting to different apps, tools, and systems. With this, agents can send emails, update records, pull data from a database, or start another workflow without any human in the loop. In simple terms, APIs help agents get things done smoothly, efficiently, and in real time
- LLMs: LLMs are the brain behind agentic processes. They help AI agents evaluate the current scenario and make smart decisions in real time without needing instructions at every step. These LLMs also enable agents to read and understand things like emails, documents, or messages, and respond in a way that makes sense. They provide flexibility and intelligence by understanding everyday language, breaking big tasks into smaller ones, and adjusting plans as needed.
Practical Use Cases for Agentic Workflows
Agentic workflows offer immense value across various business functions. Here are some key practical applications or agentic workflows examples:
1. Human Resource
RPA for the human resources department operated on a rule-based model to automate repetitive processes like recruitment, onboarding, and performance management. Traditional bots were trained to follow specific rules to extract data, fill out forms, and send trigger emails. However, this approach lacked intent, context, and adaptability, limiting users from making need-based changes.
With the introduction of agentic AI in HR tasks, these responsibilities can now be handed over to intelligent bots, allowing HR professionals to focus on strategic initiatives such as talent development and enhancing the employee experience.
| HR Function | Before Agentic Automation | With Agentic AI |
|---|---|---|
| Candidate Screening | Manual resume screening, time-consuming, potential bias | AI agents auto-screen resumes, match qualifications, and flag top candidates |
| Interview Scheduling | Back-and-forth emails with candidates and interviewers | AI coordinates calendars and schedules interviews instantly |
| Onboarding | Paperwork-heavy, multiple systems, manual checklist tracking | Automated onboarding workflows with smart task routing and document handling |
| Employee Queries | HR teams overloaded with repetitive queries | AI agents answer FAQs 24/7 via chat or email |
| Payroll & Compliance | Manual data entry, prone to errors, compliance risk | Intelligent agents ensure timely payroll and real-time compliance checks |
| Learning & Development | Generic training plans, low engagement | Personalized learning paths based on role, performance, and goals |
| Performance Management | Infrequent reviews, subjective evaluation | Continuous performance insights via AI-driven analytics |
| Exit Process | Manual clearance and feedback collection | Automated offboarding workflows, sentiment analysis on exit interviews |
| Time to Hire | Several weeks due to manual screening and coordination | Reduced to days with AI-led sourcing and scheduling |
| HR Decision-Making | Based on outdated or incomplete data | Real-time dashboards with predictive insights for proactive decisions |
2. Project Management
Traditional project management involved manual updates and communications, which lead to delays and misalignments. Agentic AI in project management automates tasks like tracking project progress, providing real-time updates on milestones, sending alerts for potential bottlenecks, and reassigning tasks to balance workloads. For example, in software development, an AI agent can monitor task progress and allocate resources to meet deadlines efficiently.
| Process Area | Before Agentic Automation | With Agentic AI |
|---|---|---|
| Task Assignment | Manual allocation, often imbalanced | AI assigns based on workload, role, and availability |
| Timeline Management | Static Gantt charts and manual updates | Real-time tracking with auto-adjusted timelines |
| Risk Management | Issues detected late in project cycles | Predictive alerts and scenario planning by AI |
| Communication | Multiple tools, scattered threads | Unified updates and auto-logging of interactions |
| Status Reporting | Time-consuming slide and doc creation | Auto-generated dashboards and weekly reports |
| Budget Control | Manual expense tracking, often outdated | Real-time budget burn tracking and alerts |
| Collaboration | Missed dependencies across teams | Agents highlight interdependencies and suggest solutions |
| Resource Allocation | Inflexible or gut-feel-based | AI recommends reassignments and reallocations as priorities shift |
| Issue Escalation | Reactive and sometimes late | Smart escalation routing with urgency and impact scoring |
3. Customer Service
Before the introduction of agentic AI in customer service, teams relied on manually solving complex queries and automating routine inquiries. AI agents in customer support enhance service quality by providing personalized recommendations and assisting service agents. NLP-powered agents handle FAQs, order tracking, and escalate complex issues to human agents when necessary. This hybrid approach improves response times and allows human agents to focus on resolving sensitive or unique problems.
| Process Area | Before Agentic Automation | With Agentic AI |
|---|---|---|
| Ticket Management | Manual triage, delays in assignment | AI instantly categorizes and assigns based on topic, sentiment, and urgency |
| First-Level Response | Long wait times, agent dependency | GenAI agents resolve FAQs and low-complexity tickets instantly |
| Customer Interaction | Limited to business hours | 24/7 availability through bots on chat, email, and voice |
| Sentiment Tracking | Limited analysis and slow reaction | AI performs real-time sentiment analysis for proactive handling |
| Multilingual Support | Dependent on language-trained agents | Language-agnostic virtual agents handle global support |
| Escalation Routing | Inconsistent and manual escalation | Context-aware routing with prioritization cues |
| Feedback Collection | Survey-based, low participation | Real-time feedback capture with AI-based NPS analysis |
| Knowledge Base Access | Static and outdated articles | AI suggests updated, contextual knowledge to agents and customers |
| Agent Onboarding | Takes weeks to train new agents | AI guides new agents in real-time during live conversations |
4. Finance
Earlier finance teams used manual analysis and traditional automation tools to process data and manage operations. Tasks like identifying trends or assessing risks require finance professionals to gather and analyze historical data using spreadsheets or rule-based systems with limited flexibility. With Agentic AI in Finance, finance leaders can now leverage intelligent agents to process large datasets, identify trends, and assess risks. These agents analyse historical data and market conditions to make informed financial decisions, such as optimizing supplier discounts or ensuring SOX compliance. Additionally, AI agents can automate tax optimization processes, reducing liabilities and increasing savings.
| Process Area | Before Agentic Automation | With Agentic Workflows |
|---|---|---|
| Accounts Payable | Manual invoice entry, slow approvals | Bots extract, validate, and auto-approve based on logic rules |
| Expense Management | Manual claim reviews, fraud risk | AI flags irregularities and policy violations |
| Budget Forecasting | Quarterly, manual forecasting using outdated data | Real-time forecasting with dynamic adjustments based on real data |
| Month-End Close | Reconciliation errors and manual processes | Automated data consolidation and reconciliation across systems |
| Financial Reporting | Time-consuming slide and document creation | Real-time dashboards and scheduled report delivery |
| Audit Readiness | Manual collation of supporting documents | Bots maintain organized audit logs and evidence trails |
| Compliance Monitoring | Static checks, high effort to maintain standards | AI monitors transactions and alerts on non-compliance |
| Treasury Operations | Cash flow visibility is delayed | Agents provide real-time updates on account balances and liquidity risks |
| Vendor Payments | Risk of missed or late payments | Intelligent reminders and payment scheduling bots |
5. Healthcare
The healthcare sector is developing with remarkable medical advancements but there’s another flip side of overwhelming workloads, system inefficiencies, and emotional strain for both patients and care teams. Healthcare processes like clinical data analysis, managing patient records, and scheduling manual reviews by practitioners often lead to delays in diagnosis or treatment planning.
With agentic workflows in the picture, healthcare departments have become more adaptive, efficient, and patient-centric. AI agents can effectively coordinate across systems to schedule appointments, send smart reminders, automatically adjust bookings based on patient needs and provider availability, and analyse patient histories and lab results, assisting doctors in real time.
| Process Area | Before Agentic Automation | With Agentic Workflows |
|---|---|---|
| Patient Registration | Manual data entry and paper-based forms | AI pre-fills data using previous records and ID verification |
| Appointment Scheduling | Staff-based coordination and calls | AI schedules, confirms, and reschedules automatically |
| Claims Submission | Slow processing and high rejection rates | Bots auto-fill forms, validate entries, and submit claims |
| EHR Access | Multiple systems, delayed data retrieval | Unified view through AI-mediated secure data fetching |
| Lab/Test Tracking | Manual updates and patient follow-ups | AI agents monitor lab orders and notify patients and doctors |
| Admin Workload | Overburdened staff with repetitive tasks | Repetitive tasks are offloaded to intelligent automation |
| Compliance Handling | Reactive compliance with risk of error | Real-time checks and audit-ready logs generated automatically |
| Emergency Coordination | Slow internal communication during emergencies | Agents send smart alerts and coordinate response teams instantly |
| Insurance Verification | Manual and delay-prone | Real-time insurance eligibility checks and pre-authorizations via bots |
6. Supply Chain Management
The 2022 Economic Report of the President says that the globalization of production has made supply chains more likely to face disruptions. With traditional forms of automation, supply chain operations relied on manual oversight, and the demand forecasting process was based on historical trends using static models that struggled to adapt to sudden market shifts. The AI in the supply chain market is expected to expand at a compound annual growth rate (CAGR) of 42.7% between 2024 and 2033, ultimately reaching a value of USD 157.6 billion by 2033. Agentic AI brings intelligence and adaptability to supply chain operations, making them resilient in the face of disruptions, shortages, and demand fluctuations.
| Process Area | Before Agentic Automation | With Agentic AI |
|---|---|---|
| Inventory Management | Reactive restocking and forecasting errors | AI forecasts demand and automates replenishment |
| Order Processing | Manual input and validation of POs | Bots extract and validate data from orders and invoices |
| Shipment Tracking | Reactive follow-ups and customer complaints | Real-time shipment monitoring with proactive exception alerts |
| Vendor Coordination | Delayed follow-ups, miscommunication | AI agents manage vendor communications and updates |
| Procurement Approvals | Lengthy manual approval cycles | Auto-routed, policy-based approvals across departments |
| Demand Planning | Spreadsheet-driven and siloed | Integrated demand signals across departments using AI |
| SLA Management | SLA breaches often discovered post-facto | Agents track SLA adherence in real-time |
| Customs/Compliance | Paperwork-heavy and error-prone | AI validates required documentation and tracks regulatory compliance |
| Invoice Matching | Manual 2-way/3-way matching | Intelligent bots reconcile discrepancies and notify stakeholders |
Types of AI Agents Workflows
1. Reinforcement Learning Agents
Reinforcement learning (RL) agents operate by learning through trial and error, guided by rewards or penalties for their actions. Over time, they identify actions that maximize rewards, improving performance with each iteration. A notable example is AlphaGo, which mastered the game of Go by playing against itself and refining its strategies with every match.
2. Utility-based agent
These agents make decisions based on what gives them the best outcome. They calculate different choices and pick the one with the most benefits, like choosing the fastest and safest route on Google Maps. More examples include stock trading algorithms in which AI analyzes market conditions and selects trades with the highest expected profit while minimizing risk.
3. Goal-based agents
These agents work toward a specific goal. They take actions that bring them closer to achieving their goal, keeping the possible consequences for each action. For example, GPS navigation system that finds the best way to reach your destination, Medical AI analyzes symptoms and test results to find the best treatment plan for a patient, aiming for a correct and timely diagnosis.
4. Model-based reflex agents
These agents remember past situations in the form of past and current data and use that knowledge to make better decisions. They don’t just react but also consider what they learned before, like a self-driving car that remembers road conditions.
5. Simple reflex agents
These agents react instantly based on set rules without thinking about the past. They work like a traffic light that changes based on sensor input, without considering past traffic patterns. More examples include a smart thermostat that detects room temperature and automatically turns the heater on or off without remembering previous temperatures.
Benefits of AI Agentic Workflows
Agentic workflows extend efficiency gains by delivering extensive, scalable, and autonomous process support. Let’s take a look at what it looks like from an enterprise standpoint:
1. Next level of Accuracy
Agentic workflows 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.
2. Enhanced Efficiency
Automates repetitive and time-consuming tasks, allowing businesses to streamline operations and achieve faster results.
3. Improved Decision-Making
AI agents analyze real-time data, providing actionable insights and enabling more informed and accurate decisions.
4. Cost Reduction
Minimizes operational costs by reducing reliance on manual labor and improving resource allocation.
5. Scalability
Handles increasing workloads seamlessly without compromising performance or accuracy, making it ideal for growing businesses.
6. Adaptability
Dynamically adjusts to changing environments, processes, or data inputs, ensuring workflows remain relevant and effective.
7. Error Reduction
Decreases human errors by automating complex processes and improving data accuracy.
8. Increased Productivity
Frees up human resources to focus on strategic and creative tasks, enhancing overall productivity.
9. Seamless Integration
Connects multiple systems, tools, and platforms for end-to-end process optimization and smooth communication.
10. Enhanced Customer Experience
AI agents deliver personalized and timely interactions, improving customer satisfaction and loyalty.
11. Continuous Improvement
Leverages machine learning to refine processes over time, ensuring workflows become smarter and more efficient.
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Experience the Impact of Agentic Workflows Firsthand
Deploying agentic workflows helps enterprises automate complicated and effort-intensive processes. As automation technologies evolve, these agentic workflows' use cases will increase and address real-world enterprise challenges across various industries. Accelirate is a trusted platinum UiPath partner and a leading expert in agentic automation. Our dedicated team can help you leverage the full capabilities of agentic workflows.
