What are Agentic Workflows? Architecture, Use Cases, and How To Build Them

Agentic this and agentic that. AI agents have been the epicenter of today’s tech buzz, but another entrant has arrived to the show: agentic workflows. With so many emerging terms and frameworks, it’s easy to get lost in a sea of jargon. In this blog, let’s break down what an agentic workflow is, how it works under the hood, use cases, challenges, benefits, and most importantly, how you can get started with building your own agentic workflows.

What is an agentic workflow?

An agentic workflow is an AI-driven process where the sequence of tasks are dynamically executed with minimal human intervention to achieve a particular goal. Agentic workflows are a form of agentic AI systems, which consist of AI models with memory, planning, reasoning, and tool-use capabilities.

Unlike traditional automated workflows, agentic workflows are dynamic, enabling it to account for multiple unexpected variables and achieve more complex tasks that often exceed the boundaries of what an algorithmic script can do. Rather than following a predetermined path, an agentic workflow involves constant evaluation of what steps to take next, based on real-time information and conditions. To achieve this level of flexibility, agentic workflows usually involve a decision-making loop until the resolution is accomplished.

At a high level, an agentic workflow can be distilled into a process that contains an iterative Thought–Action–Observation loop. In this loop, an AI model assesses the situation, devises or updates a plan, takes action (often via an external tool or API), observes the result, and so on.

Similar to AI agents, agentic workflows are semi-autonomous or autonomous. However, agentic workflows provide a more structured process focused on coordinating AI systems, agents, humans, and distributed services. An agent is akin to a smart worker, while an agentic workflow is like an assembly line.

Agentic workflow vs AI workflow vs automated workflow

Workflows are nothing new. Executed by an orchestration layer, a workflow is a coordinated sequence of tasks completed to achieve a specific goal. However, there is much confusion over how an agentic workflow differs from an AI workflow or an automated workflow. The key difference lies in the degree of AI autonomy involved in achieving the intended goal.

For example, a traditional automated workflow involves multiple pre-determined algorithmic scripts, like a customer service bot that runs through a rigid set of steps or questions and cannot help with issues that have not been coded for beforehand.

On the other hand, an agentic workflow is a subset of AI workflows. Non-agentic AI workflows involve AI models completing a pre-determined workflow task, such as an AI-powered expense approval flow or a RAG-based AI chatbot. These non-agentic flows involve the use of AI, but the AI models do not make autonomous decisions.

In contrast, agentic AI workflows include undetermined tasks that are decided upon by AI models after reasoning and evaluation. This could look like an expense approval flow, where AI is used to determine if the uploaded document is an expense before deciding whether to approve the expense for processing or route it to human review. Here, AI is used for key decision points, making the workflow much more dynamic and powerful.

In general, here are the key differences between an automated workflow, AI-powered non-agentic workflow, and an agentic workflow.

Area	Automated workflows	AI-powered workflows	Agentic workflows
Workflow logic	All tasks are pre-determined beforehand.	All tasks are pre-determined beforehand.	At a high level, the general workflow logic is pre-determined, but the specific tasks that will take place are decided dynamically at runtime by AI models.
Task execution	All tasks are completed by logic, algorithms, or human input.	AI models are used to complete some tasks. These tasks tend to be too complex even for sophisticated algorithms, like classifying documents or summarizing text.	AI models are used to complete some or most tasks. These tasks tend to require even more cognitive skills (planning, reflection, reasoning), like deciding and executing a financial buy order.
AI involvement	No AI models involved.	AI models are used to complete complex tasks pre-determined by humans.	AI models are used for key decision-making, as well as for completing cognitively demanding tasks determined at runtime.
Responsivity	Not responsive to contextual changes and unexpected elements, as tasks are pre-determined and executed within the boundaries of the algorithmic logic.	Not responsive to contextual changes and unexpected elements, but able to handle a wider scope of tasks even if not specifically trained for it.	Responsive to contextual changes and unexpected elements, due to agentic decision-making and dynamic execution flow.

Use cases for agentic workflows

Agentic workflows offer extensive opportunities to inject more flexibility into traditional automated workflows. This translates to greater efficiency and performance for more complex tasks, reducing the degree of human involvement required.

Here are some common scenarios where agentic workflows can drive value for enterprises:

• Intelligent customer support

  Troubleshoot issues, resolve queries, and file customer requests in multi-turn conversations. With agentic workflows, the customer bot can look up the customer’s order status via an API, determine and gather all relevant information for the customer’s case, dynamically escalate the issue to a human agent, or submit the customer issue post-call.

• Dynamic document handling

  Verify, classify, and approve documents for all sorts of business operation needs, like expense management, leave approval, claims processing, or invoice filing. Using agentic workflows, the document handler can dynamically reject irrelevant documents, extract relevant information regardless of the document layout, route to the appropriate flow based on the document content, and engage in the necessary processing actions determined by the AI model.

• Cybersecurity monitoring

  Monitor activity patterns in an organization’s network and learn the organization’s unique activity rhythm over time to detect and escalate unusual behavior. Agentic workflows enable real-time dynamic detection of threats across multiple systems and automatic defensive response strategies, like blocking an IP address or escalating an alert.

• Finance advisory co-pilot

  Provide real-time insights into customers’ financial habits and take proactive steps to help them hit financial targets, like investing unused budget or picking which financial products to buy. With agentic workflows, complex decisions can be evaluated, with human involvement as guardrails, and acted upon after careful analysis.

• IT or DevOps automation

  Monitor system health and alerts and autonomously trigger remediation steps like restarting a service, reverting a deployment, or pulling relevant logs when paging a human. Agentic workflows enable dynamic reflection based on real-world circumstances, empowering systems with the ability to take the next relevant action even if the situation has never been encountered before.

These examples are just the tip of the iceberg. Agentic workflows are set to power intelligent automation across industries. Deloitte analysts predict that by 2027, up to 50% of companies that use generative AI will have launched agentic AI pilots or proofs of concept.

Benefits and challenges of agentic workflows

The main benefits of using agentic workflows are self-evident:

• Performance for complex or decision-heavy processes

  Compared to deterministic or non-agentic AI workflows, agentic workflows are better equipped to handle complex processes due to their ability to plan and decompose processes into actionable tasks.

• Autonomy and adaptivity to changing situations

  Unlike static scripts or rule-based systems, agentic workflows can handle edge cases, exceptions, or evolving circumstances more gracefully. It can evaluate and re-evaluate based on new information, reflect on their own actions, and adapt accordingly.

• Scalable and cost-efficient

  If built correctly using the right platform and framework, agentic workflows can accurately automate time-consuming or labor-intensive processes at scale, saving both time and money.

But there are undeniable challenges when it comes to implementing agentic workflows properly at scale:

• Huge technical overhead

  As shown in the implementation architecture for agentic workflows below, many resources are required to build an enterprise-ready agentic workflow. For a straightforward process, an agentic workflow may add unnecessary technical overhead to your infrastructure. But if deemed valuable to build, using the right tools and frameworks will go a long way in making it frictionless to build agentic workflows.

• Risk of unreliability or unethical behavior due to agentic autonomy

  Since agentic systems are non-deterministic, they pose a serious risk of making a wrong or unethical decision in high-stakes workflows. When building and deploying an agentic workflow, it is imperative to include proper guardrails, human-in-the-loop checkpoints, and thorough testing to prevent unwanted consequences.

Implementation architecture for agentic workflows

As mentioned, agentic systems are equipped with memory, planning, reasoning, and tool-use capabilities. So, what are the key elements required to build an agentic workflow? Let’s explore the seven core components:

1. Agent core (execution engine):

   An orchestration layer that coordinates and connects all the other components involved in the agentic workflow. This is the main execution unit that dynamically routes workflow tasks, calls functions, and fetches additional information, keeping the Thought-Action-Observation loop running. This can be a self-built orchestrator or an existing platform like Orkes Conductor.

2. Reasoning module:

   The AI model(s) in the agent core responsible for planning, evaluating, and reasoning what to do next. It may also be involved in decomposing tasks into smaller units or selecting what tool to use.

3. Memory module:

   Consists of long-term memory for retaining information across multiple workflow instances or sessions, and short-term memory for retaining information within the current instance. The memory module provides critical context to the agent core for planning and reasoning. This can be implemented as a vector store or as in-session environment or workflow variables.

4. Toolset:

   All the integrations, tasks, services, and functions that the agent core can use for the workflow. This could look like an a web query API, a notification webhook, a database insertion, and so on.

5. Prompt template store:

   A separate configurable storage for the prompt templates used for various parts of the agentic workflow, like the reasoning module, memory module, or other AI-powered workflow tasks. These instructions can be independently tuned for optimal performance.

6. Observability store:

   The audit trail, metrics, or logs storage for the entire workflow session, tracking the state of the workflow, the decisions made, and other performance metrics.

7. Human-in-the-loop controls:

   The control measures for human review or approval at crucial workflow stages where high-stakes actions are involved. These steps can be statically coded into the agentic workflow or dynamically chosen at runtime.

Patterns in agentic workflows

There are four design patterns for creating agentic workflows: planning, tool-use, reflection, and multi-agent collaboration. Each of the first three patterns roughly correspond to the Thought-Action-Observation loop mentioned previously, and multi-agent collaboration describes the use of multiple agents in a single workflow.

Let’s explore each design pattern in turn.

Planning

The planning pattern refers to an AI model autonomously deciding what sequence of tasks to do to achieve a certain goal. In practice, this looks like task decomposition based on the user query. For example, if an agentic system is tasked to write an essay about the gut microbiome, it may break it down into several tasks: ask for more details on the topic, length, audience, and content to include; conduct web research; synthesize the finding; then generate and refine the essay.

The planning pattern is useful for cases when you are not able to specify the task decomposition flow ahead of time. Typically, this means processes where the specific sequence varies on a case-by-case basis or where problem-solving is involved, like a fraud detection check or a security monitoring flow.

Since the planning pattern tends to produce less predictable results, it isn’t always necessary to include it in your agentic workflows. Most flows can work well even with just tool use or reflection.

Tool use

The tool use pattern refers to the ability to interact with external services or resources to execute some task, including looking up information, completing an action, or editing databases. Some common tools include APIs, web browsing, code interpreters, database retrieval, and other AI models.

Similar to function calling, the tool use pattern goes further by having the agentic system pick which tool to use for a specific task. For example, if the agentic workflow is tasked with finding the top hotels in Barcelona, it could choose to do a web search or query a specific database like Yelp.

Reflection

The reflection pattern refers to iterative prompting to get the AI model to refine its output for a certain task. For example, after an executable code has been generated, the model is prompted to check the code for accuracy, style, and elegance and to provide critical feedback on ways to improve. Using this feedback, the model is prompted again to make changes to the code. This self-reflective loop can be implemented using a single agent or with multiple agents: one for generating the work and the other for providing the feedback.

The reflection pattern is particularly powerful in an agentic workflow, especially when combined with several intermediary steps between the generation step and the feedback step. In the code generation example, these intermediary steps could be test executions with a debugger or unit tests. The results from these steps can be added as context to the feedback agent for greater accuracy in pinpointing improvements.

Multi-agent collaboration

Lastly, the multi-agent collaboration pattern refers to using multiple agents, each with their own role, to complete specialized sub-tasks that add up to accomplish a broader project. This is a broader pattern where the previous three patterns (planning, tool use, and reflection) can be used by each agent in a multi-agent system.

By distributing various tasks to different agent roles, it provides the opportunity to individually equip each agent with its own memory and optimize them for their assigned tasks without side effects. For example, a website builder agentic workflow may consist of a software engineering agent, a UX designer agent, and a content marketer agent. The software engineering agent may be prompted to be reflective about their production code and have access to code interpreter tools, while the content marketer agent may be required to access internal resources for content ideas and analytics tools for strategizing.

Due to the nature of multiple interactions between agents, the non-determinism may compound, leading to unpredictable results. The multi-agent collaboration pattern requires careful implementation and thorough testing to work well.

With these design patterns in mind, let’s finally turn to the million-dollar question: how do you create an agentic workflow?

How to create an agentic workflow?

In general, here are the key steps for creating an agentic workflow:

1. Create a plan.

   Decide on the parameters of your project. What kind of agent do you need? What capabilities does it need? What tools or data does it need? What tech stack or architectural patterns are required?

2. Pick an AI agent platform or framework.

   Decide on what execution platform to run your agent from. This could be AI-specific frameworks like LangChain, or orchestration engines like Orkes Conductor.

3. Design the workflow.

   With your plan and platform ready, create the agentic workflow, using the relevant resources required, like the AI models, databases, and APIs.

4. Choose the AI models and create the prompts.

   As part of the workflow design process, carefully pick which AI models are suited for each stage in your workflow and craft your prompts using prompt engineering tactics. Here, you can test the models and prompts to validate or switch out your choices.

5. Integrate your external tools and databases.

   As part of the workflow design process, integrate any external APIs, databases, or services that your agentic workflow needs for its memory module and toolset.

6. Test and iterate.

   Once your workflow is ready, test it end to end, making sure to evaluate it from the first input to the final output.

7. Deploy and launch.

   Once the workflow is production-ready, it is time to deploy it live to your user interface. This can be done on a pilot or proof-of-concept basis, or gradually rolled out on limited access before it is launched to all.

8. Monitor and enhance.

   Throughout the initial launch period, monitor the agentic workflow to track its behavior and debug any production issues. Use logging or monitoring tools like Open Telemetry, Prometheus, or Datadog.

Example implementation using Orkes Conductor

There are many ways to design and implement an agentic workflow. Let’s take a look at a few example agentic workflow patterns using Orkes Conductor, a unified application platform for executing workflows—agentic, business processes, traditional scripts, and more.

Conductor serves as the centralized orchestrator for all the distributed components involved in the workflow, routing the user query to a specific AI model, tool, or database. A simple agentic workflow design typically includes a for-while loop that dynamically iterates through a series of tasks until the terminating condition is reached.

There are numerous workflow options to achieve this dynamic task selection in Conductor. One way is the LLM Chat Complete task + Switch task combination. The LLM Chat Complete task acts as the reasoning module that decides which task to select next, while the Switch task governs the full toolset that the agentic workflow is equipped to carry out. Each branch in the Switch task represents a skill or tool that can be selected. Here is an example of an agentic customer support workflow that leverages this design style:

This basic architectural style can be extended into fuller-featured agentic workflows by adding more tasks. For example, a RAG system can be added to serve as the memory module for the agentic workflow, or two different AI models can be used to implement the multi-agent reflection pattern.

Check out the latest guide on RAG best practices for enterprise-scale implementation.

Another way to extend the dynamism of your workflows is by adding a Dynamic task, which allows the workflow to decide on what task to carry out at runtime, or a Dynamic Fork operator, which determines at runtime the number of forks to create for a parallel process. Here is another example agentic workflow that makes use of this design style:

The example above is part of an agentic security workflow. In this section, the Dynamic Fork is used to dynamically scan infected devices based on how many infected devices were flagged.

With dozens of AI/LLM integrations, input/output schema enforcement, native human-in-the-loop features, and enterprise RBAC controls, you can seamlessly build agentic workflows in Conductor for any enterprise use case. Stay tuned for more agentic examples and tutorials coming your way. In the meantime, try creating your own agentic systems with our Developer Playground sandbox.

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Conductor is an enterprise-grade Unified Application Platform for process automation, API and microservices orchestration, agentic workflows, and more. Check out the full set of features, try it yourself using our Developer Playground sandbox, or get a demo of Orkes Cloud, a fully managed and hosted Conductor service.