What are AI agentic workflows?
Ever since the release of ChatGPT in 2022, we’ve enjoyed the modest gains in productivity and innovation unleashed by generative AI. However, the current GenAI workflows are limited to responding to user inputs, and so fall short of many enterprise-class use cases.
Agentic AI changes this. Capable of operating autonomously, perceiving the environment, and using real-time data to achieve the goals set for it — AI agents are the next step in the evolution of AI.
This new class of intelligent agents complements GenAI algorithms and, when combined, can automate use cases that have been too complex until now. By shifting from reactive to proactive capabilities, AI agentic workflows are helping enterprises solve a range of real business problems that outstrip traditional automation.
In this article, we explore these exciting advancements and how to approach them, specifically:
What are AI agentic workflows?
An AI agentic workflow is a process in which AI executes tasks on its own in pursuit of a goal set for it. In other words, an AI workflow is agentic if it can act autonomously as an intelligent agent, and doesn’t need to be prompted by a human to take actions along the way.
Also known as agentic process automation, these workflows harness the ability of AI agents to make decisions, use tools, analyze data, and adapt to changing circumstances without constant human intervention.
AI agentic workflows: How do they work?
Agentic AI workflows are the latest and greatest iteration of autonomic computing. First developed by IBM in the 1990s, autonomic computing is based on a template for action known as MAPE — Monitor, Analyze, Plan, and Execute. Today, intelligent agents follow this same basic feedback loop.
However, due to advances in machine learning and the inclusion of GenAI models, the analysis and planning components of agentic AI are far more sophisticated. Unlike previous AI workflows that are basic input-out operations, an agentic feedback loop incorporates a structured, iterative approach to problem-solving, tool use, and collaboration. This allows agentic workflow to tackle multi-step objectives that require greater flexibility and precision.
For instance, when ChatGPT 3.5 was wrapped in an iterative agentic workflow instead of a zero-shot setting that generated outputs without revision, its coding accuracy soared from 48.1% to 95.1%. As studied by AI expert Andrew Ng, this demonstrates the transformative potential of agentic AI workflows in enhancing model performance, allowing for more human-like reasoning and independent action in AI workflows.
Agentic design patterns
Agentic AI workflows are autonomous thanks in large part to agentic design patterns. These architectural templates define the agent's behaviors within the workflow, structuring how they collaborate with tools, environments, and other systems. When implemented by a business, they guide development and ultimately allow for more flexible, intelligent, and scalable AI systems.
There are four main types of agentic design patterns:
Tool use: AI agents can access external data and tools to solve problems. This can involve using web searches, APIs, and processing data in real time to bolster its knowledge base with information or functionality needed to reach a goal.
Planning: AI agents can break intricate multi-step tasks down into a series of smaller, manageable subtasks. This ability to iteratively problem-solve helps them automate complex workflows.
Reflection: Agentic AI can learn from past actions and improve through self-evaluation. It uses stored logs and memory models to create a feedback loop, continuously checking actions against outcomes to refine strategies and update its knowledge base.
Multi-agent workflows: Agentic AI systems can involve multiple agents specializing in different roles working in coordination to achieve a shared objective. These agents can be software apps, AI models, bots, or even humans in the loop (HITL).
Examples of AI agentic workflows
Consider the example of an agentic AI workflow for customer service. Unlike a generative AI chatbot that can only respond to queries based on its static knowledge base until making a handoff to a human — an AI agent could resolve a complex issue from start to finish.
The agent does this by perceiving the problem in the context of its environment, reasoning and planning how to solve it, retrieving data as needed, then executing independently to completion.
To troubleshoot a multi-step technical IT issue, for instance, the agent would break down the complex task into a series of subtasks and complete each in sequence:
Query the company's database for customer service history and device logs.
Request real-time diagnostic data on relevant devices using an API call.
Analyze the collected data to identify potential causes and suggest solutions.
Once the objective is met, the agent reflects on the outcome, its actions, and any feedback before compiling data into its knowledge base to self-improve.
This is an example of a single-agent design pattern. Agentic AI systems can also accommodate multi-agent architecture better suited to handling complex workflows.
Multi-agent AI workflow example
Much like human teamwork, AI agentic workflows can be a group effort. In a multi-agent AI workflow, a group of specialized agents collaborate toward a common goal. This design pattern is used when tasks are distributed, asynchronous, or require orchestration between diverse systems or entities.
Take the example of planning a marketing campaign. One agent might process market research, another would draft content, and another edits to brand guidelines—all of which is coordinated by a supervisor agent in charge of strategy.
Depending on the workflow needs, agents can be added or removed. This would allow for adaptability in the face of changing circumstances or environments.
In the graphic below, each black dot represents an AI agent. This architecture can be designed in several ways to suit different use cases:
Use cases for AI agentic workflows
While it's still early days for agentic AI, the applications for these new workflows are as limitless as they are exciting. Some use cases include:
Customer service
Companies already use AI agents to automate ever-more complex customer interactions, helping to streamline service operations and reduce costs. Agents retain context from past interactions in their memory (or access it as needed via APIs), providing a more personalized, continuous experience that improves resolution times and customer satisfaction scores.
Business operations
AI agents could reshape business operations by processing vast amounts of data and making real-time decisions. For example, agents could autonomously manage supply chains, optimize inventory levels, and forecast demand based on internal and external data sources. This would be done while minimizing human input on routine tasks like data entry to improve efficiency and reduce costs.
Cybersecurity
AI agents could continuously monitor network traffic and employee behavior to detect anomalies and respond to potential threats in real time without constant human oversight. This proactive yet responsive approach would enable a cybersecurity posture that’s more resilient, scalable, and cost-effective.
Software development
AI agents could one day manage the entire development lifecycle, from autonomously designing system architecture to writing code to detecting bugs. This would significantly reduce coding time to ensure digital products get to market faster without compromising quality.
Healthcare
Intelligent agents can monitor patient vitals through wearable devices in real time to identify risk signals and improve health outcomes. Based on current trends and medical history, they could recommend diet and lifestyle changes, schedule check-ups, or notify healthcare providers in the case of emergency.
High-frequency trading
In the fast-paced world of trading, AI agents can capitalize on trading opportunities that humans are too slow to recognize. For example, agents could analyze market data, make split-second trade decisions, and adjust investment strategies based on real-time market conditions and customer’s financial position.
Marketing
AI agents could orchestrate and optimize marketing campaigns and content using real-time data on customer history, current context, and market signals. For example, an ecommerce agent that assists with shopping could proactively trigger marketing messages in context — such as coupons at checkout to reduce cart abandonment or lead capture workflows based on omnichannel interactions.
Why AI agentic workflows matter
As a major leap in the evolution of AI, agentic workflows are helping enterprises enable use cases that have been out of reach until now. This is expected to drive rapid adoption. In fact, Gartner predicts that agentic AI will make 15% of all day-to-day work decisions by 2028.
Here are the main value that AI agent workflows bring to enterprises:
Efficiency: They streamline operations, reduce manual effort, and enhance productivity.
Scalability: Workflows can handle larger workloads as they don't depend on linear scaling of human resources.
Cost-Effectiveness: Automating repetitive or time-consuming tasks saves on costs while maintaining high levels of accuracy.
Innovation: By freeing up human employees from routine tasks, AI agentic workflows enable teams to focus on strategic, creative, or high-value activities.
Customer experience: Workflows with conversational AI can automate high-quality, lifelike interactions with customers, while personalizing interactions with real-time data to elevate the omnichannel experience.
For example, the real estate company Redfin created an AI agent to help users find information about home listings and market information on its mobile app and website.
In the course of providing 24/7 support, the agent proactively triggers experiences in context, such as lead generation workflows, next-step paperwork for qualified buyers, or human conversations. The agent improved Redfin’s website and app experience greatly, and 93% of users returned to the app within a week of using the agentic AI assistant.
Learn more: Read the Redfin case study
Key components of AI agentic workflows
1. AI agents
These are the intelligent entities at the heart of agentic AI workflows. Typically built on a large language model (LLM), the agent provides the ability to reason, plan action, use tools, and reflect on actions to self-improve over time. They can be uniquely configured for various tasks.
2. Natural language processing (NLP)
Natural language processing allows the agent to interpret, understand, and respond to users in human language. This capability simplifies the process of training and implementing agentic AI workflows, and allows agents to store data as meaningful natural language. It’s essential for any workflow involving conversational AI or AI chatbots.
3. Robotic process automation (RPA)
Robotic process automation enables the agentic workflow to execute repetitive, rule-based tasks across applications, such as data entry or transaction processing. It’s essential because it allows for creating software robots that function as “workhorses” for routine actions, such as updating spreadsheets with data, and leaving the dynamic decision-making and tasks to agents.
4. Workflow orchestration
Workflow orchestration is the process of managing and coordinating the various tasks and components within an AI agentic workflow. Defining the sequence of tasks and managing dependencies ensures the efficient allocation of resources and seamless collaboration between agents, humans, and other systems. For instance, in a financial fraud detection workflow, one agent monitors transactions, another blocks suspicious transactions, then triggers a final workflow to update fraud detection models with new insights.
5. Integrations
Agentic AI workflows require integrations and APIs to connect to different systems, software, and technologies to access data, execute tasks, adapt behavior, and collaborate. For example, integrations allow for AI agents to sync with CRM systems or RPA tools to automate multi-step tasks, while function calling via APIs enables the agent to pull in real-time data from outside sources, like stock market data or network diagnostics.
Best practices for implementing AI agentic workflows
If you’re thinking about adopting an AI agentic workflow into current processes, here are some best practices to keep in mind:
1. Define your objectives: Identify the processes you’ll automate with AI agentic workflows. Whether you want to improve customer service, streamline operations, or refine code, having a clear goal will simplify implementation and help you measure success.
2. Assess your readiness: For all its advanced capabilities, agentic AI requires more advanced infrastructure than previous AI, as well as large sets of data for training. Evaluate current systems to determine if they can integrate with agentic AI, your data availability, and budget for hire or outsource roles.
3. Prepare your data: AI agents must be trained on vast amounts of high-quality data to function effectively. This includes internal business data like customer profiles, transaction histories, product documentation, and external data like market research. Ensure you have data collection, data cleaning, and management systems in place so you get accurate, relevant outputs.
4. Plan for human oversight: AI agents are autonomous in their decision-making and action, so it’s crucial to establish control mechanisms that validate their outputs for accuracy, relevance, and compliance. Plan when human intervention will be needed (humans in the loop), and consider creating guardrails around complex problems or sensitive interactions.
5. Establish governance: Create a comprehensive governance framework for agentic systems that define the roles and responsibilities of all stakeholders involved in development and deployment. Lay out ethical guidelines, compliance measures, and monitoring and auditing mechanisms that underpin trust and safety.
6. Monitor and optimize: AI agentic workflows should be monitored and updated regularly to ensure security, compliance, and performance. Tracking agent action logs, feedback loops, and metrics can inform improvements that maximize the value and reliability of workflows. Applying metadata to tracking helps with data transparency, allowing for auditing that’s key to explainability and accountability.
Learn more: How to build an AI agent
AI agentic workflows: The future of automation
With automated decisions and access to real-time data, agentic AI workflows are a step-change in business process automation. Combining GenAI models with goal-oriented agent technology, they represent a leap forward in operational efficiency and customer experience for businesses.
Going forward, AI agent platforms and providers are expected to offer agentic AI templates (or blueprints) for specific workflows, effectively automating away tasks such as customer service, virtual medical screenings, and many more.
To learn more about custom agentic AI workflows for your unique needs, contact the AI experts at Sendbird. Our team of PhDs in artificial intelligence, machine learning, and software engineering can help you understand, develop, and deploy a custom AI agentic workflow for even the most complex use cases.
Frequently asked questions
What’s the difference between generative AI workflows and agentic AI workflows?
The chart below illustrates the main differences between previous GenAI architectures and the combination of agentic AI architecture to create more autonomous workflows:
What are some common challenges of implementing AI agentic workflows?
For all their exciting potential, agentic AI workflows aren’t without their risks and challenges. Here are the key considerations that enterprises should to keep in mind as they consider AI agentic workflows:
Integration complexity: Setting up and managing AI agents within a complex business environment can be intricate, requiring specialized technical expertise and careful planning.
Data privacy and compliance: To protect sensitive customer data, it’s critical to implement robust security measures like encryption, access controls, and monitoring systems. Ensure workflows comply with relevant industry regulations to maintain compliance.
Change management: Implementing AI agents can significantly alter existing workflows, requiring proper training and support for employees to adapt to the new system.
Investment and maintenance: Developing and deploying AI agentic workflows often involve significant upfront costs for technology, infrastructure, and talent. The ongoing cost to maintain, update, and monitor AI agent workflows must also be factored in.
Human oversight and control: While AI agents are designed to be autonomous in their decision-making, they still require human oversight (humans in the loop) and intervention mechanisms (guardrails) to manage risks and maintain accountability.
Infrastructure optimization: Agentic AI workflows require balancing infrastructure, performance, and cost. For example, with time-sensitive use cases like high-frequency trading, AI agents are best hosted near to end users to minimize latency that undercuts performance. You can consider processing data locally to help reduce costs while ensuring efficient data processing.
