A practical guide to agentic workflows in 2025

Let’s be honest, the hype around AI agents is getting a little out of hand. We’ve all seen the flashy demos of AI assistants that promise to do everything, but when it comes time to actually use them, you find out you need a team of developers and months of setup just to handle a simple task.

The root of the problem is that most automation is too rigid for the messy, human work that support and IT teams do every day. And on the flip side, the idea of a fully autonomous agent is just too risky and unpredictable for most critical business operations. So where’s the middle ground?

This is where agentic workflows come in. They hit that sweet spot, giving you smart, multi-step automation without forcing you to hand over the keys and hope for the best. This guide will walk you through what agentic workflows are, how they work, and how you can actually use them to make your business run more smoothly.

What are agentic workflows?

Put simply, agentic workflows are automated processes where an AI agent can reason, plan, and use different tools to complete complex tasks on its own.

Think of it like hiring a really sharp assistant. You don’t give them a rigid, step-by-step script that will fall apart if something unexpected happens. Instead, you give them a goal and a toolbox. The agent can look at a problem, figure out the steps to solve it, and grab the right tool for each part of the job. It can adjust its approach on the fly, just like a person would.

This is a big leap from the kind of automation most of us are used to. Here’s a quick breakdown of how they compare:

Feature	Traditional Automation (e.g., RPA)	Agentic Workflows	Fully Autonomous Agents
Decision-Making	Rule-based, follows a script	AI-driven, dynamic planning	AI-driven, open-ended
Adaptability	Low (breaks on unexpected input)	High (adapts to new information)	Very High (can change goals)
Setup Complexity	Low to Medium	Low to High (platform-dependent)	Very High (requires deep expertise)
Reliability	High (for defined tasks)	High (with proper guardrails)	Variable (can be unpredictable)
Best For	Repetitive, predictable tasks	Complex, multi-step business processes	Open-ended research, complex coding

Now, building these workflows from scratch can be a massive project. Luckily, you don’t have to. Platforms like eesel AI are built to make agentic workflows accessible, so teams can automate their work without needing a squad of AI engineers.

The core components of effective agentic workflows

So, what’s actually going on inside one of these workflows? It’s not magic, just a few key parts working together in a clever way.

How an agent ‘thinks’: Reasoning and planning

At the core of any agent is a Large Language Model (LLM), which basically acts as its brain. The first thing it does is break a big goal down into smaller, manageable steps. For example, if a customer asks for a refund, the agent doesn’t just guess. It maps out a plan: first, check the order date, then look up the company’s return policy, and finally, either approve or deny the request based on those rules.

Good agents also have the ability to self-correct. They can look at the result of an action, see if it worked, and try a different approach if it failed.

The tricky part is that if you’re building this from scratch with open-source tools, it can be a real pain to figure out why an agent did something unexpected. With eesel AI, you get a simple prompt editor that gives you full control. You can define the AI’s persona, tell it when to escalate to a human, and set the exact actions it’s allowed to take, making sure its logic always matches your business rules.

A screenshot of the eesel AI platform showing how users can define rules and guardrails for their agentic workflows.

Giving the AI hands: Tool use

An AI model by itself is just a brain in a jar. It can think, but it can’t do anything. Tools are what connect it to the real world. A tool can be anything from searching a knowledge base or calling an API to looking up a customer’s order details in your e-commerce platform.

This is often the hardest part of the setup. Most companies have information scattered all over the place, in Google Docs, Confluence, old support tickets, and a dozen other systems. Getting an AI to reliably access all of that is a huge technical challenge.

This is a problem we thought a lot about when building eesel AI. It connects to all your company knowledge instantly. With over 100 one-click integrations, it can pull information from your helpdesk, internal wikis, and even e-commerce platforms like Shopify. This gives your agent the context it needs to be genuinely useful right away. You can also create custom actions to connect to your own internal APIs, giving it pretty much limitless capabilities.

A view of the numerous one-click integrations available in eesel AI, connecting agentic workflows to various company knowledge sources.

Learning from experience: Memory and context

For an agent to feel smart, it needs a memory. It really comes down to two types:

• Short-term memory: This is all about remembering the current conversation. If a customer says, "My order is #123," and then asks, "Where is it?" the agent needs to remember that order number to give the right answer.

• Long-term memory: This is about learning from past conversations to improve over time. An agent should be able to recognize that a certain problem was solved a specific way last time and try that solution again.

The biggest hurdle here is that training an AI on your company’s unique voice and context is usually a massive, ongoing project. You can’t just feed it a generic manual and expect it to sound like your team.

eesel AI gets around this by training on your historical support tickets from day one. It learns your team’s tone, understands your common issues, and knows what a good resolution looks like for your business, not some generic template.

In action: Some real-world examples

Okay, enough theory. Let’s look at how agentic workflows can solve actual problems you probably deal with every day.

Automating frontline customer support

Imagine a customer pops up in your website’s chat widget and asks, "Where is my order?" An AI agent can instantly understand the question, use a tool to look up the order status in Shopify, and give them a real-time update. If the customer then asks about making a return, the agent can check your return policy in your Confluence knowledge base and walk them through the next steps.

The problem is, many AI support tools are all-or-nothing. They either force you to automate everything at once (which rarely goes well) or make you switch away from your current helpdesk. No one wants to go through that kind of migration.

The eesel AI Agent plugs right into the helpdesk you already use, like Zendesk or Intercom. Better yet, its simulation mode lets you test it on thousands of your actual past tickets. You can see exactly how it will perform and roll it out gradually, maybe starting with just one type of ticket, long before it ever talks to a live customer.

The simulation mode in eesel AI, which allows businesses to test their agentic workflows on past customer tickets before going live.

Streamlining internal IT support

Here’s another one. An employee asks for VPN access in a public Slack channel. Instead of that request turning into yet another IT ticket, the AI agent can understand what they need, find the VPN policy and request form in your internal wiki, and guide the employee through the whole process right there in Slack.

Too often, internal knowledge is stuck in different places. This means employees either have to go on a scavenger hunt for information or just file an IT ticket for every little thing, which buries the IT team in repetitive questions.

eesel AI’s Internal Chat acts as a central brain for your company. It connects to all your internal knowledge sources and gives employees instant, accurate answers in Slack or Microsoft Teams. It helps deflect those simple, routine questions and frees up your IT folks to work on bigger things.

An example of the eesel AI agent resolving an internal IT support request directly within a Slack channel.

Scaling e-commerce operations

Say a potential customer is browsing your site and asks if one product is compatible with another. A generic chatbot would just get confused and tell them to email support.

But an AI chatbot trained on your product catalog and help center FAQs can give a confident, correct answer. It can even help the customer add both items to their cart. This is the difference between a frustrated user and a completed sale. The eesel AI Chatbot is designed for this. It integrates directly with platforms like Shopify to provide catalog-aware responses that help turn curious shoppers into happy customers.

How to choose the right platform for your agentic workflows

Not all AI platforms are built the same. As you start looking at your options, it helps to know what to look for and which common traps to avoid.

Andrew Ng discusses the rise of AI-driven agentic workflows and their potential to transform industries by automating complex tasks.

A few things to watch out for with agentic workflows

Bringing in agentic workflows can have a few potential bumps in the road if you’re not careful:

• Needless complexity: Many tools are built for developers, not for the teams who actually need them. If you have to talk to a sales rep just to see a demo, you’re probably in for a long and complicated setup process.

• Surprise costs: Some platforms charge per resolution or per ticket. This means your bill can suddenly spike during a busy season, essentially punishing you for your own success.

• Reputation risk: Unleashing an untested AI on your customers can do real damage to your brand. A few bad interactions can wipe out months of building goodwill.

• The "black box" problem: Many solutions don’t give you any real control. You can’t tweak the AI’s behavior, limit what it can do, or tell it how to handle sensitive situations.

What to ask before you commit to agentic workflows

To find a platform that actually helps instead of creating more work, keep these questions in mind:

• How long until it’s live? Can you set it up yourself in a few minutes, or are you locked into a multi-month sales and onboarding cycle? A true self-serve platform puts you in the driver’s seat.

• How much control do I have? Can you choose exactly which types of questions to automate and which to send to a human? Fine-grained control is key for a safe rollout.

• Can I test it with confidence? Is there a way to simulate the AI on your actual past data before it goes live? Don’t fall for a generic demo that has nothing to do with your business.

• Is the pricing straightforward? Is it a predictable, flat fee, or will you be hit with per-ticket charges that are impossible to forecast? You should know exactly what you’re paying for.

We built the eesel AI platform to address these exact issues, offering a simple, controllable, and risk-free way to build agentic workflows with clear pricing.

Putting agentic workflows to work

Agentic workflows are the most practical way for most businesses to start using AI automation right now. They avoid the rigidity of old-school systems without jumping into the chaos of completely autonomous agents. It’s a balanced approach that delivers real value where you need it most: in your day-to-day operations.

Success isn’t about finding the fanciest AI. It’s about finding the right platform, one that gives you control, lets you test confidently, and fits into the tools you’re already using. By starting with one focused, well-defined workflow, you can begin automating tedious tasks, making your team more efficient, and providing a better experience for everyone.

Ready to build smarter agentic workflows?

Stop wrestling with complicated frameworks and start automating with confidence. With eesel AI, you can launch a powerful AI agent trained on your unique business knowledge in minutes, not months.

Start your free trial or book a demo to see how you can safely automate your support workflows.