Organizations are rapidly learning about the amazing capabilities of agentic AI for business automation. The tech at the heart of AI agentic workflows, agentic AI is far more autonomous than traditional AI models like generative AI. With Agentic AI, businesses can automate increasingly complex tasks so they can shift their attention to strategic work.
Considering implementing an AI agentic workflow? We’ve got the information you need to create effective and efficient processes.
We’ll cover:
- What Are AI Agentic Workflows?
- Three Pillars of AI Agentic Workflowssome text
- AI Agents
- Advanced Engineering Techniques
- Generative AI Networks (GAINs)
- How to Implement AI Agentic Workflows
- Benefits of AI Agentic Workflows
- Challenges of AI Agentic Workflows
- AI Agentic Workflows FAQs
- Leverage AI Agentic Workflows in Your Technology
What Are AI Agentic Workflows?
Agentic AI is a type of artificial intelligence with the ability to learn from experience and develop multi-step action plans to meet complex goals. Its ability to break tasks down into simpler steps—plus its learning capabilities—makes agentic AI able to work relatively autonomously, with little guidance from human input.
An AI agentic workflow is a system of large language models (LLMs) used to handle repetitive tasks. But what makes AI agentic workflows different from other AI workflows? Agentic AI’s power to work autonomously makes it capable of handling more complex tasks than other types of AI. Their ability to learn also enables increasing accuracy in handling assigned tasks.
Once implemented into a company’s workflow, AI agents break tasks down into simpler steps, determine the optimal order of steps, alter plans when encountering difficulty, and review processes to find areas for improvement.
AI agentic workflows have a variety of potential use cases across a wide range of industries. In e-commerce, AI agents can automate the collection and analysis of customer data, manage inventory, and provide customers with personalized product recommendations. AI agents can transform healthcare by enabling more accurate diagnoses and identifying potential health risks. HR teams can also benefit from agentic AI, as AI agents are increasingly capable of screening resumes, conducting preliminary interviews, and providing insights to streamline hiring. And the list doesn’t end there!
What Technology Do AI Agentic Workflows Need?
There are a few types of technology that help AI agentic workflows function, including:
- Artificial intelligence: AI helps agents perceive their environment, resolve problems, and communicate with users through natural language processing (NLP).
- Machine learning: AI agents use machine learning to conduct data analysis, pattern recognition, and anomaly detection.
- Big data: AI agents require vast data to function effectively, so they rely on the infrastructure created by big data, which collects, stores, processes, and analyzes large amounts of data.
- Robotic process automation (RPA): RPA simplifies the process of building, deploying, and managing robots meant to mimic human actions, which helps manage AI agentic workflows.
- Cloud computing: AI agentic workflows rely on cloud computing for secure collaboration, use of vast amounts of data, and scalability.
- Workflow orchestration: Workflow orchestration streamlines every step of task execution, including error handling, sequencing, scheduling, and monitoring.
Three Pillars of AI Agentic Workflows
There are three major pillars of effective AI agentic workflows. We’ll explore how each pillar contributes to these processes.
1. AI Agents
The autonomy and versatility of AI agents form the core of AI agentic workflows. AI agents can learn from experiences and implement complex processes with little human guidance. This means they can engage with more tools and resources and accomplish more than traditional AI models, making them more widely applicable across industries and tasks.
Steps to make AI agents more effective for your AI agentic workflow include:
- Providing relevant tools so your AI agent can learn and expand its capabilities
- Integrating tools to widen the range of inputs and tasks your AI agent can handle, including code execution environments, search engines, or image manipulation software
2. Advanced Engineering Techniques
Using advanced prompt engineering techniques and frameworks allows AI agents to attempt tasks (i.e., create drafts) and use those attempts to conduct self-improvement reflection processes. Potential prompt engineering techniques include Planning, Chain of Thought, Meta Prompting, ReAct, and Self-Reflection.
3. Generative AI Networks (GAINs)
Generative AI Networks (GAINs) greatly enhance AI agentic workflows through their use of the multi-agent collaboration principle. GAINs assign each AI agent a different role, such as critic, coder, designer, or CEO. The AI agents then collaborate to solve problems and accomplish goals.
This process allows AI agents to specialize their skills and perspectives and utilize this specialization to enhance the AI agentic workflow. Solutions gained through multi-agent collaboration are more comprehensive and often more innovative than those generated by single-AI agent systems.
How to Implement AI Agentic Workflows
A strong implementation process is essential to ensure your AI agentic workflows function exactly as you need them. If you plan on implementing AI agentic workflows, there are a few steps you can take to make the process run smoothly and effectively.
- Determine which processes would benefit from AI agentic workflows. Processes that benefit from agentic AI include those that require complex decisions or that are repetitive, data-heavy, or error-prone.
- Assess whether your organization is prepared to implement AI agentic workflows. Check the quality of your data and determine the computational resources your chosen technologies need.
- Choose appropriate AI tools. Set clear objectives and research AI tools to find the right one for your needs. For example, if your end users need video generation tools, Tavus is the leading AI video API, offering users easy scalability and personalization to build customer relationships.
- Train your AI agent. Use your organization’s data to train an AI agent for your specific needs.
- Deploy a pilot project before scaling. Use a pilot project to test your AI agentic workflow strategy. Testing allows you to refine your processes before implementing them throughout your organization. Once you see appropriate results from the AI agentic workflow, scale it to work for the entire company.
- Collaborate with your team to ensure effective integration. Work with all departments, especially IT, to optimize how your AI agent works with existing systems and processes.
- Evaluate your AI agent implementation. Regularly review the AI agent’s output and processes to determine whether you need to make adjustments or provide additional input.
For video-based AI agentic workflows, turn to Tavus to provide your users with cutting-edge AI agent technology. Tavus’ powerful Conversational Video Interface (CVI) enables easy creation of AI chatbot agents that can speak, see, and hear. Your end users will be able to implement AI agentic workflows for a variety of use cases, including customer support, sales, marketing, life coaching, corporate training, and tutoring.
Benefits of AI Agentic Workflows
There are quite a few advantages to implementing AI agentic workflows—read on to learn how your organization could benefit from using AI agents.
Optimize Workflows
AI agentic workflows help increase your efficiency with complex tasks, breaking those tasks down into simpler steps. By working on one task at a time and collaborating in a multi-agent system, AI agents improve outcomes and free up your time to work on strategic tasks. If, for example, you’re looking to improve your efficiency around routine tasks, AI agents can help, handling a fair portion of customer inquiries so your team can focus on more complicated customer calls.
Boost Adaptability
AI agents are highly adaptable. They can fulfill multiple roles or switch between roles depending on the requirements and development of a task, enabling more responsive problem-solving. With increased adaptability comes more autonomy, since agentic AI systems can respond to problems and changes in data or situations without the need for human oversight or direction.
Reduce Costs
By automating more business processes, you’ll see increased cost savings after the initial investment in agentic AI software. You’ll need less human labor for business processes, and your human workforce will be able to focus on more important tasks.
Easily Scale
Since AI agents can work 24/7, you don’t need to pause business operations. You’ll see increased efficiency, which enables business expansion. You can scale your business by letting AI agents work for you around the clock, and with more time to work on strategic initiatives, your team can increase productivity and growth.
Challenges of AI Agentic Workflows
AI agentic workflows offer amazing benefits, and the challenges and upfront costs of implementation are often worthwhile to access increased automation and long-term cost savings. Challenges to consider before implementation include:
- Difficulty accessing quality data: Data silos can make it difficult for AI agents to access complete, unbiased data, leading to potentially unreliable outcomes.
- Challenges integrating with legacy systems: Some legacy systems do not support new data structures or APIs, which may reduce data quality, limit scalability, and restrict the computational power needed for agentic AI models.
- A lack of the required technical infrastructure: AI agentic workflows require increased computational power, robust data processing infrastructure, and strong security measures, which may be costly to implement and maintain.
- Ethical concerns: Companies that implement AI agentic workflows should consider ethical concerns related to large language models and ensure compliance with applicable regulations.
- Ongoing monitoring: Despite agentic AI’s relative autonomy, businesses should still evaluate AI agents’ performance and implement adjustments to improve results.
More About AI Agentic Workflows
Check out our answers to frequently asked questions to understand AI agentic workflows.
What are the four stages of an AI workflow?
The four primary stages of an AI workflow are data input, processing, decision-making, and output generation. Organizations determine the appropriate data input based on their definition of the workflow and what it should accomplish. AI agents then process data and requests, decide what steps are needed, and generate output to accomplish the task.
What is the agentic approach to AI?
Agentic AI differs from traditional AI approaches in its increased autonomy and ability to handle more complex tasks. Agentic AI models require little human guidance once given a task or goal, and they’re able to create complex, multi-step plans and adapt to any issues they encounter.
How does an AI agentic workflow work?
An AI agentic workflow utilizes AI agents, enhanced prompt engineering, and Generative AI Networks (GAINs) to automate tasks and conduct regular self-reflection to improve functionality. These workflows use autonomous agentic AI models to accomplish routine yet complex tasks.Leverage AI Agentic Workflows in Your TechnologyAs you explore strategies for growing your business, consider implementing AI agentic workflows into your technology. Agentic AI allows you to automate repetitive, time-consuming tasks, freeing up your team to focus on other important work and allowing your business to scale quickly.
Check out Tavus’ AI video API to learn how you can implement AI video agent technology as part of your tech stack. Offer your users access to cutting-edge AI video technology, including the ability to create their very own digital twins and generate unlimited videos. With high-quality lip syncing and dubbing APIs plus easy personalization tools, Tavus is the video generation AI tool your customers need.
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