Agentic Workflows Explained- The Way Forward in AI-Powered Workflow Automation
Key takeaways
- Agentic Workflows are sophisticated iterative and multi-step processes to interact and instruct Large Language Models to complete tasks with more accuracy.
- Agentic workflows in AI involves deploying several AI agents to carry out specific roles and tasks.
- Traditional automation methods such as RPA follow pre-defined rules and design patterns.
- AI agentic workflows approach complex problems in an iterative manner to adapt dynamically to business processes.
- Businesses implementing agentic process automation (APA) have been able to improve their productivity by up to 66%.
- AI agents are systems that combine large language models for reasoning and decision making with tools for real world interaction.
- You can create practical and functional AI-powered workflows with Seyarc.ai in Cflow for core business processes.
Table of Contents
What are Agentic Workflows?
Agentic workflows may be defined as AI-driven processes where autonomous AI agents make decisions, take actions, and coordinate tasks with minimal human intervention. An agentic workflow or agentic workflows in AI use AI agents to work for task management and automation. AI agentic workflows employ technologies like large language models, machine learning, and natural language processing to understand the context.
Read on to learn more about what agentic workflows are, how agentic workflows in AI work, various agentic workflow patterns and types, limitations of agentic workflows, and use cases and examples of agentic workflows.
Understanding the Basics of Agentic Workflows
What is an agentic workflow? Agentic workflows or processes employ large language models technology and natural language processing to interpret the information and decipher the context for interacting with other systems and users. As AI agents operate within workflows, they can adapt to new inputs and dynamic circumstances in real-time, while continuously learning from their experiences to improve their performance.
Agentic workflows in AI allow organizations to handle tasks effectively by treating them as dynamic entities, driving new levels of efficiency and enabling agile responses to evolving business needs. While automations can handle simple, repetitive tasks, agentic workflows take automation to a higher level by interpreting and streamlining complex and large-scale processes.
Once an AI agent workflow is set up, your team no longer has to manually fill the gaps that traditional automation may leave behind. These workflows have a significant impact on business operations, to the extent that 50% of businesses using generative AI will implement AI co-pilots by 2027. To answer the question: What is agentic workflow in AI, let us look at the findings of Deloitte’s TMT predictions 2025 report. According to this report, agentic AI has the potential to complete tasks autonomously, thereby improving the productivity and efficiency of knowledge workers.
For a better understanding of agentic workflows, let us imagine a series of tasks being performed seamlessly and automatically. These workflows solely rely on AI systems that are capable of making decisions and learning along the way. Agentic AI is the core around which agentic workflows are built and operate. Unlike traditional automation systems that require constant supervision, these workflows use AI systems for making decisions and learning along the way.
A workflow becomes agentic when one or more agents guide and shape the progression of tasks. When agents are added to an existing non-agentic workflow, a hybrid approach is created that combines the reliability and predictability of structured workflows with the intelligence and adaptability of LLMs. Agentic workflows can –
Make a plan – The planning module in agentic workflows breaks down complex tasks into sub-tasks through task decomposition to determine the best execution route.
Execute actions with tools – Agentic workflows incorporate a set of predefined tools that are paired with permissions to accomplish tasks and execute the generated plan.
Reflect and iterate – Agents can assess results at every step and adjust the plan as needed.
How do Agentic Workflows Work?
“Small and wide data approaches in data analytics together facilitate more robust analytics and AI, which reduces the dependency on big data. A combination of these 2 approaches empowers organizations with a richer and fuller situational awareness. “ – Jim Hare, Research Vice President at Gartner.
Gartner predicts that this approach will be adopted by 70% of organizations for providing more context for analytics and making AI less data hungry.
An agentic workflow may be considered as a series of connected steps that are dynamically executed by an agent, or a series of agents, for achieving a specific task or goal. AI agents are granted permissions by their users, which give them a limited degree of autonomy to perform tasks, gather data, and make decisions to be executed in the real world. Agentic workflows also leverage the core components of AI agents, including reasoning capability, environment interaction, and persistent memory, for complete transformation from traditional workflows.
AI agents collect and process data by interacting with their environment to perform specific tasks. These tasks are usually assigned by humans and AI agents operate autonomously to create their own independent action plans to accomplish the tasks. To understand how AI agentic workflows work, it is important to recognize the key components of Agentic Workflows.
Perception and Input Handling Module
Agentic AI must be able to ingest and interpret information from disparate sources. Inputs can come from different forms, including user queries, system logs, structured data from APIs, or sensor readings. The agent uses natural language processing for parsing and understanding text-based inputs or data extraction techniques for structured sources.
The complexity of the perception module depends solely on the agent’s purpose. Upon receiving raw data, the perception module cleans, processes, and structures the information into a usable format. For effective conversion of information, AI tools for speech-to-text conversion, object detection, sentiment analysis, anomaly detection, and entity recognition are often used. In some cases, prompt engineering might be required for successfully guiding the behavior of agents within certain workflows.
Planning and task decomposition module
Planning agents set out to map sequences of actions before execution. This module is important for AI applications such as autonomous robots, logistics optimization, and AI-driven scheduling systems. Once the planning module deciphers the input, the task decomposition starts. Complex problems are broken down into smaller, manageable tasks. Task decomposition is done by sequencing actions and determining the dependencies between tasks.
In systems that employ multiple agents, planning and decomposition become even more complex as they involve coordination and negotiation for resources. The absence of a robust planning module in an agentic workflow results in agents struggling with long-term tasks and failing to optimize processes.
Memory Module
The memory module enables the AI agent to retain and recall information, which ensures learning from past interactions and maintaining context over time. This module is typically divided into short-term and long-term memory, where the former stores session-based context and the latter stores structured knowledge bases, vector embeddings, and historical data. Short-term memory modules allow AI assistants to recall messages in a conversation and maintain coherence.
Long-term memory modules house well-structured knowledge bases and historical data that the agent can refer to when making decisions. Successful memory recall requires persistence and organization for providing personalization in applications, such as customer bots, virtual assistants, and recommendation engines. An agentic workflow without a memory module operates aimlessly, forcing users to repeat information, which adversely affects the user experience.
Reasoning and decision-making module
Chatbots – AI-agents in the simplest format, work based on pre-defined rules for a narrow set of decisions. Modern AI agents are more advanced and capable of evaluating different solution paths, refining their approach, and assessing performance. The reasoning module determines how an agent reacts to its environment by weighing various factors, evaluating probabilities, and applying logical rules or behaviours. Reasoning can either be rule-based, heuristic-driven, or probabilistic.
The two most important reasoning paradigms are ReAct (Reasoning and Action) and ReWOO (Reasoning without observation). Simple, rule-based AI systems are based on predefined logic, while more advanced systems are based on reinforcement learning or neural networks.
Action and Tool Calling Module
The action module is concerned with implementing the agent’s decisions in the real world. This module allows it to interact with users, physical environments, and even digital systems. The appropriate response is determined by the reasoning and planning modules, while the response is executed by the action module.
Agentic workflows may require access to external tools, datasets, APIs, and automation systems for executing actions.
Tool calling is a mechanism adopted by Agentic AI systems for invoking external tools, APIs, or functions to extend its capabilities beyond inherent reasoning and knowledge. Calling external tools allows AI agents to perform actions, retrieve real-time data, perform external computations, and interact with external systems dynamically.
Communication module
The communication module enables agents to interact with humans, other agents, or external software systems. Clear communication channels enable seamless integration and collaboration capabilities. This module incorporates natural language generation and protocol-based messaging. The complexity of communication varies with the type of agent; simple agents follow predefined scripts, while advanced agents use generative AI models that are trained on vast amounts of data for generating dynamic, contextual responses.
Learning and Adaptation Module
An important feature of intelligent agents is their ability to learn from past interactions and improve over time. Learning algorithms enable AI agents to recognize patterns in data and refine predictions, and adjust the decision-making process based on feedback. Learning is achieved through various learning paradigms, such as supervised learning, unsupervised learning, and reinforcement learning.
What are Agentic Workflow Patterns?
The Learning and Adaptation module in AI agentic workflows works based on algorithms that recognize specific patterns in the data. So, when we talk about agentic workflows in AI, we refer to specific patterns of behavior that enable agents to achieve their final goal. These core AI agent components play a crucial role in agentic workflow patterns. A clear understanding of the workflow patterns helps teams use the Agentic Workflows the right way.
Planning pattern
The planning pattern design allows agents to autonomously break down more complex tasks into a series of smaller and simpler tasks by following task decomposition. Task decomposition yields better results because it minimizes the cognitive load on the LLM, minimizes hallucinations, improves reasoning, and reduces other inaccuracies. This pattern is especially useful when there is no clarity on the final target, and adaptability in the problem-solving process is most important.
Although planning can help agents tackle more complex tasks effectively, it can also lead to less predictable results. The best way to use this pattern is when tasks require intense problem-solving and multi-hop reasoning.
Tool Use Pattern
A notable limitation of generative LLMs is their reliance on pre-existing training data, which means that they cannot retrieve real-time information or verify facts beyond their previous learning. Due to this limitation, they may generate non-factual responses or even indulge in guesswork when they don’t know the answer. Retrieval Augmented Generation (RAG) helps overcome this limitation by providing the LLM with relevant, real-time information for more accurate and contextually sound responses.
The tool use pattern goes beyond the capability of the RAG pattern by facilitating dynamic interaction of the LLM with the real world. This is in contrast to other models that simply retrieve data. In agentic workflows, the tool use pattern expands the capabilities of agents by allowing interactions with external resources and applications, real-time data, or other computational resources.
Reflection pattern
Reflection is a powerful agentic design pattern that is relatively simple to implement and can lead to significant gains in improvement for agentic workflows. The reflection pattern incorporates a self-feedback mechanism in which an agent iteratively evaluates the quality of the output or decisions before zeroing in on a response or taking further action. These critiques are then used to refine the agent’s approach, rectify errors, and improve future responses.
The real power of the reflection pattern lies in the agent’s ability to critique its own outputs and dynamically integrate these insights into the workflow, enabling continuous improvement without the need for human feedback. These reflections can be encoded in the agent’s memory, which allows for more efficient problem solving during the current user session.
Types of Agentic Workflows
AI agents collect and process data by interacting with their environment to perform specific tasks. These tasks are usually assigned by humans to achieve specific goals. Some of the types of agents are listed below:
Simple Reflex Agent
This is the simplest type of AI agent that makes decisions based on the current percepts or observations and ignores the information gathered from past sensory inputs. These agents work on the condition-action rule and perform any action based on the current condition. An example for a simple reflex agent is a vacuum cleaner that works only after it detects dirt in the room.
Model-based Reflex Agent
Model-based reflex agents handle the current situation by matching it with other similar conditions. This type of reflex agent operates in partially observable environments that do not furnish complete information. At the core of model-based reflex agents’ operations are models and internal state. Models provide information on how things happen in the world, while the internal state represents the agent’s memory or knowledge of the world based on past experiences.
Goal-based agent
As the name suggests, goal-based agents work with the awareness of the goal they intend to achieve rather than just knowing the current information. Goal-based agents are aware of the end goal and can analyze the possible course of action that they can take to reach the goal. AI-powered assistants like Alexa and Siri are the best examples of goal-based agents.
Utility-based Agent
Utility-based agents perceive a goal and find the best possible route to achieve it. This type of agent is useful when there are many possible ways of performing the task. Utility-based agents can choose the action based on their preference or utility. The best example of a utility-based agent is a self-driving car that works towards reaching a specific destination in a timely manner.
Learning agent
A learning agent exhibits learning capabilities by learning from past experiences. This type of agent starts its functioning with basic knowledge and later adapts as per learning experiences. The core elements in learning agents are the learning element, critic, performance element, and problem generator.
AI Agent Automated Process vs Traditional AI Workflows
While we talk elaborately about AI agents and agentic workflows, the question arises about how these workflows score over traditional AI workflows. Let us look at the comparison between AI Agentic Workflows and Traditional AI workflows
What is Agentic Powered Automation?
Agentic-powered automation (APA) is a major upgrade from traditional automation. Consider a system that doesn’t just follow instructions but makes decisions and adjusts in real-time, without the user having to lift a finger. You have your agent-powered automation system! An agent-powered automation system relies on agents for handling complex, multi-step tasks entirely on their own. Core technologies in APA include AI, NLP, RPA, Generative AI, and cloud computing.
AI agents come with a host of capabilities, including decision making, learning from patterns, and adapting to changing business conditions. Agentic-powered automation goes beyond simple task execution, extending to managing tasks from start to finish and learning as they go. An AI agent used in logistics not only tracks customer orders and responds to simple queries, but can also analyze customer data, predict behavior, and automatically trigger actions like sending a follow-up email.
Limitations of Agentic Workflows
While agentic workflows come loaded with benefits and innovative features, there are some inherent challenges that cannot be ignored. Their limitations arise from their “probabilistic” approach to automation. Some of the main challenges or limitations of agentic workflows include –
Unnecessary complexity for simple tasks
AI agents can increase the overheads, especially when used for straightforward workflows like form entry or simple data extraction. Introducing agents to rule-based processes may lead to inefficiencies and even give erroneous outcomes.
Ethical and practical considerations
Not all business decisions should be delegated to Agentic workflows. Using AI agents in areas of high stakes or sensitivity requires careful oversight to make sure that unintended consequences are avoided and responsible deployment is carried out.
Reduces reliability and accountability
The high levels of autonomy of AI agents increase the risks of unreliability. As agents gain more decision-making within the workflow, their probabilistic nature can bring unpredictability and unreliability into the process. It is important to maintain the guardrails for agents and review their permissions regularly to avoid unforeseen situations.
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Use Cases and Examples of AI Agentic Workflows
According to Codewave, the year 2025 is not just another year on the AI timeline. Rather, it is the year when Agentic AI becomes a business reality.
Some of today’s most promising applications of agentic workflows include software development, customer support, regulatory compliance, and cybersecurity. The pace of improvement for agents is accelerating, but similar to the rate of adoption of new technologies, its widespread adoption will take time. A Deloitte report states that some AI applications in specific industries may see actual adoption into existing workflows in 2025.
By enabling generative AI to handle intricate workflows, organizations benefit from improved operational efficiency, scalability, and informed decision-making. AI technology is becoming common in industries seeking to automate and optimize processes while reducing reliance on human oversight. The impacts of evolving AI models span industries like healthcare, finance, human resources, and many more.
Agriculture
AI agents can help farmers increase the yield while reducing waste. Agentic workflows can be used for independently monitoring weather conditions and forecasts, evaluating soil conditions, and planning planting schedules.
Banking and Financial Services
The World Economic Forum considers Agentic AI as the transformative era for finance. The ability of agents to act dynamically in fast-paced, data-intensive settings can be used by the BFSI sector to improve decision making, optimize workflows, and stay compliant.
Customer experience
Sharply rising customer expectations, coupled with high levels of burnout among customer service representatives, are the main drivers for the adoption of AI agents. Their ability to accelerate responses and instant recall of customer data in real-time enables AI agents to deliver deeply contextual and hyper-personalized experiences.
Healthcare
Given their ability to autonomously investigate health data and remove the admin burden in medical institutions, AI agents are gaining prominence in the healthcare industry. Their proactive approach to data analysis makes them ideal solutions for efficient diagnostics, patient vitals monitoring, and effective drug management.
Cflow and Agentic Workflows
Seyarc.ai in Cflow has been designed to create practical and functional AI-powered workflows for core business processes. Our AI agent helps you maintain timeliness and accuracy in all of your service deliveries.
Key Capabilities of Seyarc.ai –
- Instant resolution of all your process-related queries
- Effective tracking of approval requests in real-time
- Conversation-based automation of multi-level workflows
- Centralized access to all process data for deeper visibility
Conclusion
According to the Deloitte TMT Predictions for 2025 report, in 2025 25% of companies that use gen AI will launch agentic AI pilots or proofs of concept, and this figure will grow to 50% in 2027.
This blog has explored what is agentic workflow is in great detail and also delved into the use cases and examples of agentic workflows. Agentic workflows have transformed the way organizations operate by bringing in autonomy, collaboration, and integration into process operations.
Looking ahead, super-agent ecosystems will be used by 30% of Fortune 500 companies by 2028, making agility the new norm for enterprise operations. Cflow is all set to take your enterprise through this transformation. From AI-powered workflows to bespoke intelligent process workflows, we offer a host of features that streamline your business processes. Talk to our experts to set up AI-powered workflows for your business.
FAQs
How does prompt engineering help Agentic Workflows?
Using prompt engineering, you can clearly instruct AI agents about the objectives of the assigned work.
What is the cost of implementing agentic workflows?
The cost of implementing agentic workflows depends on several factors, including the resources required, the cost of the token, and the LLMs involved.
How do agentic workflows fit into a broader enterprise automation strategy?
AI Agentic workflows can serve as a primary means of achieving the business goals of an enterprise’s automation strategy.
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