In today's fast-paced world, delivering seamless customer service is essential for business success. However, achieving this goal can present challenges. For example, as the volume and complexity of customer queries grow, business leaders must find ways to maintain or improve customer satisfaction scores when existing levels of service are no longer sufficient. To do this, they can either hire more agents or automate some of the processes. Agent workflows and Artificial Intelligence Operating System speed up resolution times and help customers get the answers they need faster. This blog will help you get started with agent workflows by defining what they are, illustrating their importance, and outlining how to design, implement, and scale them for real-world impact.
AI Acquisition's AI Operating System makes it easy to design, implement, and scale agent workflows. With our solution, you can automate your processes without the complexity.
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AI agents. Agentic AI. Agentic architectures. Agentic workflows. Agents are everywhere. But what are they really? And can they do anything? New technology brings with it a muddled mixture of confusing terminology, wild expectations, and self-proclaimed online experts.
In this article, we cut through the noise and hype surrounding AI agents to explain and illustrate a critical tenet of agentic AI: agentic workflows. Agents, completely on their own, can’t do much. They need to be given roles, goals, and structure to achieve their goals. This is where workflows come in
AI agents are systems that combine large language models, or LLMs, for reasoning and decision-making with tools for real-world interaction, enabling them to complete complex tasks with limited human involvement.
Agents are assigned specific roles and given varying degrees of autonomy to accomplish their end goals. They are also equipped with memory, allowing them to learn from past experiences and enhance their performance over time.
Although AI agents are designed for semi-autonomous decision-making, they rely on a larger framework of components to function properly.
This framework consists of LLMs that enable the agent to reason effectively, tools that help the agent complete its tasks, and memory that allows the agent to learn from past experiences and improve responses over time.
Part of what makes AI agents so effective is their capacity for iterative reasoning, essentially allowing the agent to actively “think” throughout the entire problem-solving process. The reasoning capabilities of an AI agent stem from its underlying LLM and serve two primary functions: planning and reflecting.
In the planning phase, the agent performs task decomposition, the process of breaking down a more complex problem into smaller, actionable steps. This technique allows for agents to approach tasks systematically and enables them to use different tools for different tasks.
It also allows for query decomposition, in which complex queries are broken down into simpler queries, which improves the accuracy and reliability of responses from the LLM.
Agents also reason through reflecting on the outcomes of their actions. This allows them to evaluate and iteratively adjust their plan of action based on results and data pulled from external sources.
LLMs possess static, parametric knowledge, meaning their understanding is confined to the information encoded during training. To expand their capabilities beyond their original dataset, agents can leverage external tools like web search engines, APIs, databases, and computational frameworks.
This means that the agent has access to real-time external data to guide its decision-making and accomplish tasks that require it to interact with other applications.
Tools are often paired with permissions, such as the ability to query APIs, send messages, or access specific documents or database schemas. The table below outlines several standard tools for AI agents along with the tasks they perform.
Retrieve and summarize real-time information.
Retrieve and summarize external data.
Iteratively run code generated by agents.
Retrieve real-time information and perform tasks with external services and applications.
When the LLM selects a tool to help achieve a task, it engages in a behavior called function calling, extending its capabilities beyond simple text generation and allowing it to interact with the real world.
The choice of which tool to use can be predetermined by the end user or be left to the agent. Letting the agent dynamically select tools can help solve more complex tasks, but can add unnecessary complexity for simpler workflows, when predefined tools would be more efficient.
Learning from past experiences and remembering the context in which actions take place are part of what set agentic workflows apart from purely LLM-driven workflows. Memory is a key component that enables the capture and storage of context and feedback across multiple user interactions and sessions.
Agents have two main types of memory: short-term memory and long-term memory.
Short-term memory stores more immediate information like conversation history, which helps the agent determine which steps to take next to complete its overall goal.
Long-term memory stores information and knowledge accumulated over time, throughout multiple sessions, allowing for personalization of the agent and improved performance over time.
In general, a workflow is a series of connected steps designed to achieve a specific task or goal. The simplest types of workflows are deterministic, meaning they follow a predefined sequence of steps and are unable to adapt to new information or changing conditions.
For example, an automated expense approval workflow could look like this: “if expense is tagged as ‘Food and Meals’ and is less than $30, automatically approve.”
Some workflows, however, leverage LLMs or other machine learning models or techniques. These are often referred to as AI workflows and can be agentic or non-agentic. In a non-agentic workflow, an LLM is prompted with an instruction and generates an output.
For example, a text summarization workflow would take a more extended passage of text as its input, prompt an LLM to summarize it, and return the summary. Just because a workflow uses an LLM doesn’t mean that it’s agentic.
An agentic workflow is a series of connected steps dynamically executed by an agent, or a series of agents, to achieve a specific task or goal. Agents are granted permissions by their users, which give them a limited degree of autonomy to gather data, perform tasks, and make decisions to be executed in the real world.
Agentic workflows also leverage the core components of AI agents, including their capacity for reasoning, ability to use tools to interact with their environment, and persistent memory to transform traditional workflow processes into completely:
An AI workflow becomes agentic when one or more agents guide and shape the progression of tasks. Adding agents to an existing non-agentic workflow creates a hybrid approach that combines the reliability and predictability of structured workflows with the intelligence and adaptability of LLMs.
Agentic workflows are defined by their ability to:
As you can see, we need to differentiate between three types of workflows:
The difference between a traditional, rule-based workflow and an AI workflow is the use of predefined steps vs. the use of AI models to accomplish a task.
The difference between non-agentic and agentic AI workflows is the use of static AI models vs. dynamic AI agents. This makes the agentic workflow more adaptive and dynamic than a non-agentic workflow.
With any emerging technology comes a flood of new terminology. While some may use the terms “agentic architectures” and “agentic workflows” interchangeably, they have an important distinction.
An agentic workflow is the series of steps taken by an agent to achieve a specific goal. These steps may include using LLMs to create a plan, break down tasks into subtasks, using tools like internet search to accomplish tasks, and using LLMs to reflect on the outcomes of tasks and adjust their overall plan.
An agentic architecture, on the other hand, is the technical framework and overall system design used to achieve a given task. Agentic architectures are diverse and creative but always contain at least one agent with decision-making and reasoning capabilities, tools the agent can use to accomplish its goals, and systems for short-term and long-term memory.
Recall that an agentic workflow is the structured series of steps taken to complete a specific task, also known as a final target. So when we talk about agentic workflows, we talk about particular patterns of behavior that enable agents to achieve their final target.
The core components of AI agents, as we mentioned earlier, play a key role in agentic workflow patterns. The capacity for agents to reason facilitates both the planning and reflection patterns, while their ability to use tools to interact with their environment underlies the tool-use pattern.
The planning design pattern allows agents to autonomously break down more complex tasks into a series of smaller and simpler tasks, a process known as task decomposition. Task decomposition leads to better results because it reduces the cognitive load on the LLM, improves reasoning, and minimizes hallucinations and other inaccuracies.
Planning is especially effective when the method to achieve a final target is unclear and adaptability in the problem-solving process is paramount. For instance, an AI agent instructed to fix a software bug would likely use the planning pattern to break down the task into subtasks like reading the bug report, identifying the relevant code sections, generating a list of potential causes, and finally selecting a specific debugging strategy.
If the first attempt to fix the bug doesn’t work, the agent can read the error messages after execution and adapt its strategy.
While planning can help agents better tackle more complex tasks, it can also lead to less predictable results than more deterministic workflows. As a result, it’s best only to use the planning pattern with tasks that require intense problem-solving and multi-hop reasoning.
A significant constraint of generative LLMs is their reliance on pre-existing training data, meaning they cannot retrieve real-time information or verify facts beyond what they have previously learned. As a result, they may generate non-factual responses or “guess” when they don’t know the answer.
Retrieval augmented generation helps mitigate this limitation by providing the LLM with relevant, real-time external data, enabling more accurate and contextually grounded responses.
Tool use goes beyond naive retrieval augmented generation by allowing the LLM to dynamically interact with the real world, as opposed to simply retrieving data from it. In agentic workflows, the tool use pattern expands the capabilities of agents by allowing them to:
Standard tools include:
These tools are used to perform specific tasks like searching the web, retrieving data from an external database, or reading or sending emails that help the agent achieve their target.
Reflection is a powerful agentic design pattern that is relatively simple to implement and can lead to significant improvements for agentic workflows. The reflection pattern is a self-feedback mechanism in which an agent iteratively evaluates the quality of its outputs or decisions before finalizing a response or taking further action.
These critiques are then used to refine the agent's approach, correct errors, and improve future responses or decisions.
Reflection is beneficial when the agent is unlikely to succeed in accomplishing its target goal on the first attempt, such as writing code. In this case, an agent may generate a code snippet, run it in a sandbox or execution environment, and iteratively feed errors back into the LLM with instructions to refine the code until it executes successfully.
The power of reflection lies in the agent’s ability to critique its outputs and dynamically integrate those insights into the workflow, enabling continuous improvement without direct human feedback.
These reflections can be encoded in the agent’s memory, allowing for more efficient problem-solving during the current user session and enabling personalization by adapting to user preferences and improving future interactions.
We help professionals and business owners start and scale AI-driven businesses by leveraging existing AI tools and our proprietary AI operating system at ai-clients.com. You don't need to have a technical background, invest any significant capital up-front, or work what feels like another 9-5 job, because AI does a lot of the heavy lifting for you.
Check out a free training to see how I used this exact system to go from a burned-out corporate director to making $500,000 per month in under 2 years. Feel free also to book an AI strategy call with one of our consultants to see how you could use your existing skills & experience to start a successful AI business.
The projected value of generative AI across industries and functions is immense, but 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.
Agentic research assistants, sometimes referred to as deep research by certain AI companies, go beyond traditional retrieval-augmented generation (RAG) by producing detailed reports and nuanced insights on complex topics. Rather than merely retrieving relevant information, these assistants synthesise and analyse data drawn from the web and other external sources in response to user queries.
This advanced capability is typically powered by agentic RAG, which combines retrieval with:
Several key features distinguish agentic research assistants from standard RAG implementations.
The result is a system capable of uncovering deeper insights, recognising evolving trends, and assembling original research outputs, rather than simply surfacing existing content. As of this writing, OpenAI, Perplexity, and Google each offer publicly accessible versions of these agentic research systems.
Agentic coding assistants go beyond simple code generation; they can generate, refactor, debug, and iteratively improve code with minimal human input. Traditional, non-agentic coding tools, such as early versions of GitHub Copilot, are powered by generative LLMs fine-tuned for code completion but limited to producing static outputs without awareness of execution context.
What makes a coding assistant agentic is its ability to interact dynamically with its environment. These systems can execute generated code, interpret errors, and refine their output based on the results, all in a feedback-driven loop.
Some, like Anthropic’s Claude Code, are even granted permissions to interact directly with the codebase, creating commits and pull requests as part of a broader effort to automate software development.
Others, like Cursor’s Agent, strike a balance by suggesting terminal commands or code changes and waiting for explicit user approval, keeping the human in control while leveraging the agent's capabilities.
Crucially, agentic coding assistants can encode learnings in long-term memory, enabling them to improve over time and tailor their behaviour based on past experience, marking a significant evolution in AI-driven software engineering.
Efficient backorder processing is key to both operational performance and customer retention. Traditional workflows automate discrete tasks, such as sending order updates or checking inventory, to offer timely alternatives and reduce customer churn. A typical process begins when an automated system opens a backorder case and notifies the customer service team.
A human agent then reviews the email, accesses the CRM, verifies order details, and checks the ERP system for potential replacements. An inventory automation identifies in-stock alternatives, after which the employee finalises the replacement and triggers the fulfilment workflow, bringing in logistics and shipping teams.
In an agentic workflow, however, the process is more autonomous and coordinated. When a backorder case is opened, a review agent immediately validates order details, updates relevant systems (e.g., CRM), and communicates directly with the customer to confirm preferences.
The case is then handed off to a replacement agent, who finds alternatives, checks inventory, and places the order. A fulfilment agent then oversees logistics and shipping, ensuring the replacement is delivered without unnecessary human intervention.
Finance teams aim to streamline invoice processing workflows to accelerate payment cycles, strengthen vendor relationships, and improve cash flow management.
Automating individual tasks, such as data entry, verification, and approvals, has helped:
When an invoice is received, the process typically unfolds as follows:
This workflow, while partially automated, still involves multiple handoffs and manual inputs, increasing the risk of delays and inconsistencies.
In contrast, an agentic system introduces autonomy, coordination, and contextual intelligence across each stage:
By embedding intelligence and autonomy into each step, agentic workflows minimize friction, reduce turnaround times, and free finance teams to focus on more strategic tasks.
IT teams aim to strengthen network threat detection to protect sensitive data, reinforce security posture, and ensure business continuity. By deploying monitoring systems, organisations automate the collection and analysis of network traffic and threat intelligence, enabling a proactive defence strategy that reduces incident response time and resource strain.
Most organisations follow a semi-automated, analyst-driven process:
While automated tools assist in detection, the process still heavily depends on human analysts to confirm and respond to threats.
In an agentic approach, intelligent agents autonomously manage detection, escalation, and optimisation:
This workflow significantly reduces response times and human dependency, allowing IT teams to shift from reactive firefighting to proactive security management.
In the competitive banking sector, automating the loan application process helps minimise errors, reduce compliance risks, and ensure consistent, accurate evaluation of loan requests. Well-designed automation not only shortens turnaround times but also improves risk management, operational efficiency, and customer satisfaction, directly impacting profitability and market competitiveness.
By streamlining critical tasks like data entry, credit assessment, and underwriting, financial institutions can speed up loan approvals and deliver faster access to funds for borrowers, enhancing the overall customer experience.
In a fully agentic model, intelligent agents take over many manual review and processing steps, improving speed and consistency:
The prior authorization (PA) process is designed to minimize treatment delays while ensuring access to medically necessary services in alignment with insurance coverage and clinical guidelines. When implemented effectively, it supports better patient outcomes, improves operational efficiency, and contributes to a more responsive healthcare system.
To accelerate approvals and reduce administrative burden, automation efforts increasingly target core components of this workflow, such as:
In an agentic system, intelligent agents take over repetitive tasks, enabling faster decisions and more consistent adherence to policy and clinical rules:
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Agentic workflows can introduce new complexities to your operations. Despite their benefits and innovative features, AI agents also come with several challenges and limitations. Because of their probabilistic nature, AI agents inherently add complexity to workflows. Just because agents can be used to automate processes doesn’t mean that they should be used.
Here are a few of the most notable challenges and limitations of agentic workflows:
Agentic workflows can add overhead when used for straightforward workflows like form entry or basic data extraction. In cases where deterministic, rules-based automation is sufficient, introducing agents may lead to:
As agents gain more decision-making power within a workflow, their probabilistic nature can introduce unpredictability, making outputs less reliable and harder to control. Implementing and actively maintaining guardrails for agents and continually reviewing their granted permissions is critical.
Not all decisions should be delegated to AI systems. Using agents in high-stakes or sensitive areas requires careful oversight to ensure responsible deployment and prevent unintended consequences.
Given these limitations, we recommend taking time to reflect on whether using an agent is truly necessary in a given workflow.
Some questions to help you determine this may include:
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.
The goal you want to achieve is the common purpose that AI agents involved in the workflow will align with. Clearly and explicitly defining this goal not only helps measure the effectiveness of the workflow, but 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.
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. Agentic process automation in healthcare can enable AI agents to analyze patient data while a calendaring agent manages appointment scheduling. Combined, they contribute to an overall patient care workflow.
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.
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.
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.
AI Acquisition helps professionals and business owners start and scale AI-driven businesses by leveraging existing AI tools and our proprietary ai-clients.com AI operating system. You don't need to have a technical background, invest any large capital up front or work what feels like another 9-5 job, because AI does a lot of the heavy lifting for you.
Check out a free training to see how I used this exact system to go from a burned-out corporate director to making $500,000 per month in under 2 years. Feel free also to book an AI strategy call with one of our consultants to see how you could use your existing skills and experience to start a successful AI business.
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