Getting Started with Agentic Workflows in AI Applications

Jodie Quillmore

·February 12, 2025

·16 min read

Getting Started with Agentic Workflows in AI Applications — Image Source: pexels

Agentic workflows are smart systems that work on their own. They make choices, adjust to changes, and improve tasks. These systems save time by doing boring jobs automatically. This means people don’t have to do as much work. They also help businesses grow by handling tough tasks easily. For example, companies finish projects faster and save money with automation. Agentic AI also helps with decisions by studying data and giving useful tips. This lets you focus on big goals. With over $1 billion in savings and 40% better efficiency, agentic workflows can change how you use AI solutions.

Understanding Agentic Workflows

Definition and Core Principles

Agentic workflows are a new way to automate tasks. These systems work on their own to reach specific goals. They use AI agents that decide, adapt, and improve without needing much help. Unlike old automation, which follows strict rules, these workflows are smart and flexible.

Agentic workflows are based on four main ideas:

Autonomy: AI agents do tasks alone, so less human help is needed.
Adaptability: They change plans using real-time data and situations.
Advanced Decision-Making: They study options and make smart choices.
Intelligent Automation: AI and automation together make work faster and better.

For example, CSL Seqirus used an AI platform to check 130,000 documents in minutes. This saved days of work, cutting research time to hours. Deloitte also saw an 82% drop in research time for hard projects. This shows how agentic workflows boost productivity.

These ideas not only save time but also help systems get better. AI agents learn from data and improve their decisions over time. Using these workflows can help you work smarter and innovate more.

How Agentic AI Transforms Traditional Workflows

Agentic AI changes workflows by adding intelligence and flexibility. Old workflows follow fixed rules and need people to make changes. Agentic AI lets systems act on their own, solving problems as they happen.

Think about how agentic AI helps learning. Instead of just offering courses, it creates custom learning plans. It changes content based on what users do, helping them learn better. For businesses, this means workers can learn faster and meet company goals.

In healthcare, agentic workflows improve patient care by acting on real-time data. They adjust treatments quickly to meet patient needs. In supply chains, they fix backorder problems without needing people to step in.

The benefits go beyond saving time. Agentic workflows also make work more accurate and effective. They focus on specific tasks and ensure high quality. Their design lets you reuse agents for different jobs, saving effort. For example:

Greater accuracy: AI agents are experts at specific tasks.
Better performance: They use computing power wisely for each task.
Time savings: Automating reports lets analysts focus on big decisions.

By adding agentic AI to your work, you can turn old workflows into smart systems. Whether it's stopping fraud in finance or personalizing ads, the possibilities are huge.

Key Parts of Agentic Workflows

Autonomy: Making Choices Alone

Autonomy is what makes agentic workflows work well. It lets AI agents do tasks without needing people all the time. These agents look at data, decide what to do, and act by themselves. This means less work for people and faster results. For example, an AI agent can check stock levels and order more supplies when needed. This saves time and stops items from running out.

Numbers show how autonomy helps workflows:

Measure	What It Tracks
Time saved in processes	How much faster tasks are finished
Fewer mistakes	Counts how errors go down
Better use of resources	Checks how well resources are used
Money saved with automation	Shows how much money is saved
Improved quality and rules	Tracks better quality and following rules
Happier customers or workers	Measures satisfaction levels
New tech abilities	Lists new things technology can do

Using autonomy makes workflows faster and lets you focus on big goals.

Adaptability: Changing with the Situation

Adaptability helps agentic workflows adjust to new situations. AI agents learn from what they do and change to fit new needs. This makes them good at handling surprises. For example, if a competitor drops prices, an AI system can notice and lower your prices too.

Benefits of adaptability include:

Reacting quickly to market changes.
Saving time by reducing manual work.
Making smarter choices in tricky situations.
Working together better for hard tasks.

Adaptability turns workflows into flexible systems that handle change well. This keeps your business ready for anything.

Optimization: Making Work Better

Optimization focuses on improving how workflows run. AI agents study current processes, find better ways, and test if they work. This helps workflows run as smoothly as possible.

For example, agentic AI can handle tasks like creating tickets and solving problems. This makes response times faster and keeps customers happy. Many businesses using these workflows solve issues on the first try, which 73% of customers expect.

Optimization also helps with:

Finishing tasks faster with smart systems.
Saving money by using fewer resources.
Doing repetitive tasks more accurately.

By improving processes, agentic workflows help you get better results with less effort.

Benefits of Agentic Workflows in AI Applications

Better Efficiency and Productivity

Agentic workflows help you finish tasks faster by automating boring jobs. This lets your team focus on important goals instead of small tasks. For example:

Automating jobs saves money and increases work speed.
AI agents study big data quickly, reducing mistakes and improving choices.
Learning all the time keeps workflows useful and up-to-date.
Automation connects systems, making everything work smoothly together.

Real-world examples show these benefits clearly. Amazon uses agentic AI to manage supply chains, speeding up data checks and making smart decisions. SAP’s system handles invoices automatically, lowering mistakes and improving vendor trust. CrowdStrike uses agentic workflows for security, finding and fixing threats faster. These examples prove agentic workflows make work better in many industries.

Growing with Complex Systems

Agentic workflows are great at growing with bigger systems. They adjust to more work by testing ideas and finding better ways to work. Studies show these systems perform well, often scoring over 0.9 in tests. This means they can handle more work without losing quality.

For example, in healthcare or factories, agentic AI manages huge amounts of data while staying accurate. As your business gets bigger, these workflows keep things running smoothly. They help you face new challenges with ease.

Smarter Decisions with Data

Agentic workflows help you make better choices using data. AI agents study information, find patterns, and give helpful tips. This way, decisions are more accurate and have fewer mistakes. Feedback and performance measures show how well the system works.

Measure	What It Tracks
Accuracy	Checks if the AI agent’s results are correct.
Mistakes	Counts how often the AI agent makes errors.
User Opinions	Collects what users think about the AI agent’s work.
Key Results	Tracks things like faster work, saved money, more income, and happy customers.

Using agentic workflows gives you an advantage. These systems make work easier and give useful insights for smart decisions. They help your business succeed for a long time.

Implementing Agentic Workflows

Designing Supervisor and Specialist Roles

To set up agentic workflows, you need clear roles for AI agents. Think of it like a team where each agent has a specific job. A supervisor agent acts as the leader, organizing tasks and checking quality. Specialist agents focus on specific jobs, like studying data or making reports.

For example, in a marketing project, one specialist agent can study the market, another can write content, and a third can edit it to match the brand. The supervisor agent makes sure all tasks follow your goals. This setup improves speed and reduces mistakes by giving tasks to the right agents.

Steps to design these roles:

Assign tasks: Give each agent a clear job to do.
Enable communication: Let the supervisor agent talk to specialists easily.
Check quality: Use the supervisor agent to review work before moving forward.

By organizing agents into supervisor and specialist roles, your system works like a well-run team. This keeps workflows smooth and dependable.

Task Decomposition for Better Workflow Management

Breaking tasks into smaller parts helps agentic workflows run better. This method, called task decomposition, lets you give jobs to the best agents. It also makes hard workflows easier to handle and improves results.

Imagine you’re planning a marketing strategy. Instead of giving the whole job to one agent, split it into steps. One agent can make an outline, another can study trends, and a third can polish the final plan. The supervisor agent checks each step before moving on.

Benefits of task decomposition:

Clearer tasks: Smaller jobs are easier to understand and finish.
Smarter assignments: Give tasks to agents who are best at them.
Quicker results: Doing tasks at the same time saves time.

Figure	Description
2	Shows simple step-by-step tasks versus faster parallel tasks.
5	Explains how connected tasks are done quickly in parallel.

Breaking tasks into parts makes workflows flexible and fast. It also helps your system adjust to changes easily.

Selecting Tools and Platforms for Agentic AI

Picking the right tools is important for building agentic workflows. The platform you choose should support multi-agent systems, have central control, and allow role-based permissions. These features make workflows scalable and safe.

Platforms like Momen make it easier to create agentic AI systems. They connect large language models (LLMs) with your current tools. Features like Retrieval-Augmented Generation (RAG) and Human-in-the-Loop improve accuracy and reliability.

Things to consider when choosing tools:

Scalability: Make sure the platform can handle more tasks as you grow.
Customization: Pick tools that let you adjust workflows to fit your needs.
Tracking: Choose platforms with tools to measure performance.
Compliance: Ensure the platform follows rules for handling data safely.

Benefit Description	Measurable Impact
Less time spent on routine tasks	15-25% faster completion of routine jobs.
Faster data analysis	20-35% quicker insights from data.
Better content creation	30-50% improvement in making content.

Choosing the right tools helps you build strong workflows that show clear results. These tools make processes smoother and help you reach your goals faster.

Testing, Improving, and Adding Safeguards

Testing and improving agentic workflows make sure they work well. Safeguards, called guardrails, keep workflows on track and avoid mistakes. Together, these steps build a strong system that handles problems and gives steady results.

Why Testing is Important

Testing checks how well your workflows are working. It finds problems, slow spots, and areas to make better. By testing, you can see if workflows meet your needs.

Main testing methods include:

Practice Runs: Test workflows in safe settings to watch how they act.
Heavy Load Tests: See how workflows handle lots of work at once.
Performance Goals: Compare results to set standards to check efficiency.

For example, testing might show if an AI agent struggles with hard tasks or uses too many resources. Fixing these early avoids big problems later.

Making Workflows Better

Improving workflows means fixing issues found during testing. It focuses on making processes smoother, more accurate, and faster.

Steps to improve workflows:

Review Results: Look at test feedback to find weak spots.
Tweak Settings: Change agent settings to make better choices.
Keep Testing: Test and improve again until workflows work their best.

For instance, if an AI agent misunderstands data during testing, you can adjust its instructions or code. This helps it give better answers.

Adding Guardrails for Safety

Guardrails stop workflows from making mistakes or wasting resources. They set rules and limits to keep agents focused.

Good guardrails include:

Spending Limits: Set maximum resource use to control costs.
Quality Rules: Make sure results meet your standards.
Error Plans: Create steps to fix unexpected problems.

A study showed 79% of users think agentic systems can be unpredictable. To solve this, researchers made new ways to track and improve workflows. These methods make guardrails stronger and testing better.

Tools for Testing and Guardrails

Platforms like Momen make testing and adding guardrails easier. They include features like:

Activity Logs: Watch workflow actions and find problems.
Human Oversight: Let people check important decisions.
Custom Rules: Set specific guidelines for agents to follow.

Feature	Benefit	Example Use Case
Activity Logs	Track workflow actions	Check unusual results
Human Oversight	Add checks for key tasks	Review sensitive reports
Custom Rules	Follow important standards	Protect private data

By testing, improving, and adding guardrails, you create workflows that are strong, flexible, and reliable. These steps help you use agentic AI fully while lowering risks.

Real-World Uses of Agentic Workflows

Healthcare: Faster Diagnoses and Better Patient Care

Agentic workflows are changing healthcare by making diagnoses quicker. AI tools study complex data to help doctors make better choices. For instance, AI can find patterns in patient records to suggest treatments. These workflows also speed up medicine delivery by improving supply chains.

Research shows automated systems save time and reduce effort. Doctors still check results for accuracy, but AI handles repetitive tasks. This mix of human and AI work ensures healthcare is both fast and precise.

Online Shopping: Custom Suggestions and Smart Pricing

In online shopping, agentic workflows improve customer experiences. AI studies what users like and suggests products they may enjoy. Smart pricing systems change prices based on trends and stock levels, keeping businesses competitive.

The results are clear:

Metric	Improvement
Customer Engagement	15% more users stay active
Fewer Abandoned Carts	22% drop in cart abandonment
Revenue Growth	12–18% increase in earnings
Cost Efficiency	Up to 35% lower costs

Using AI reduces abandoned carts and boosts sales while saving money. With more shopping moving online, agentic workflows help businesses stay ahead.

Content Creation: Smarter Marketing with AI

Agentic workflows make creating content easier by automating tasks. AI tools handle keyword research, competitor checks, and making visuals. This helps create high-quality content faster. For example, AI can write SEO-friendly briefs and design eye-catching images.

The benefits are measurable:

Metric	Advantage
Better SEO	Finds keywords and tracks rankings to improve visibility.
Faster Content Creation	Automates writing and visuals for large-scale production.
Improved Targeting	Adjusts ads based on user actions to reach the right audience.
Smarter Campaigns	Manages budgets and tests ideas to save time and boost results.

By using AI, you can target audiences better, run smarter campaigns, and focus on creative ideas. Agentic workflows make marketing more efficient and scalable.

Manufacturing: Smarter Maintenance and Better Quality

Agentic workflows are changing how factories work. They use AI agents to watch machines, find problems, and improve processes quickly. This helps factories save time, work better, and keep products top quality.

One big use is predictive maintenance. AI checks machines to spot issues before they break. This stops unexpected delays and cuts repair costs. For example, an AI can track machine vibrations and start repairs when something seems wrong. It can even order parts on its own to avoid waiting.

Here’s how agentic workflows help factories:

Smart Maintenance: AI starts repairs and connects with systems like MES and ERP.
Quick Quality Checks: AI finds defects fast, so bad products don’t reach buyers.
Better Processes: Constant monitoring improves work and adjusts to new needs.

Use Case	What It Does
Predictive Maintenance	Spots problems early, saving time and money.
Process Improvement	Watches work in real time to boost efficiency.
Quality Checks	Catches defects early to reduce waste and improve product standards.

AI also helps with quality control. It watches production lines, finds mistakes, and fixes settings right away. For instance, if a defect shows up, the system can stop work, record the issue, and alert the team. This saves time fixing problems and keeps quality high.

Using agentic workflows makes factories smarter and faster. These systems save resources and help deliver better products to customers.

Agentic workflows change how industries use automation and make decisions. These systems use special AI agents to give expert help and adjust quickly. They work well in tough situations, making tasks easier and sparking new ideas. By mixing AI with human input, they bring big improvements to many fields.

Start small to use these workflows. Test them, improve them, and change them based on results. Set clear goals and track progress. As your needs grow, these workflows will handle more work while staying reliable and flexible.

The future of agentic workflows is in their ability to grow and change. They will keep improving industries by building smarter and faster systems.

FAQ

What are agentic workflows, and how are they different from regular automation?

Agentic workflows are smart AI systems that work on their own. They adjust to changes and make tasks better over time. Unlike regular automation, which follows strict rules, these workflows think and make decisions for tricky tasks.

Can I use agentic workflows without knowing how to code?

Yes! Tools like Momen let you build agentic workflows easily. You can set up AI agents, link them to your tools, and manage tasks with a simple interface. This makes it easy for people who don’t know coding.

How do agentic workflows keep data safe?

Agentic workflows follow strict rules to protect data. Platforms like Momen use role-based access control (RBAC) and safety features to secure information. You can also add your own rules to keep data private and safe.

Which industries gain the most from agentic workflows?

Agentic workflows help industries like healthcare, online shopping, factories, and marketing. They make work faster, reduce mistakes, and help with smart decisions. For example, they fix machines in factories or suggest products in online stores.

How can I check if my agentic workflows are working well?

You can measure success by tracking time saved, fewer mistakes, money saved, and happy users. Tools like Momen have built-in trackers to check performance and find ways to improve.