How to Use AI Agents for Enterprise Automation

AI agents — truly autonomous “digital workers” capable of making decisions and acting independently — represent the next step in enterprise transformation.

But every masterpiece has its cheap copy.

Unfortunately, the hype around agentic AI has resulted in vendors slapping the “agentic” label on existing products — like AI assistants, RPA tools, and chatbots — that aren’t truly agentic. According to Gartner analysts, only about 130 of the thousands of agentic AI vendors are the real deal. This phenomenon, dubbed “AI agent washing,” just makes it harder for businesses to understand what agentic AI is and implement it effectively.

For agentic AI to deliver real business value, enterprises must be able to separate the hype from the reality. That means looking past flashy promises of instant automation and focusing on responsible integration, secure data access, and clear governance to create a strong foundation in which AI agents can reach their full potential.

So what are (true) AI agents?

AI agents aren’t just fancier chatbots or smarter virtual assistants. To be truly agentic, AI agents must function autonomously to achieve their goals. While that goal is set by humans, an AI agent is able to act independently — pulling data from different systems, making decisions, and interacting with applications — without prompting.

Agentic AI vs. Generative AI

The main difference between agentic AI and generative AI (tools like ChatGPT) is autonomy. AI agents can act on their own, while generative AI requires prompting from humans to function.

Types of Autonomous AI Agents

AI agents can vary in complexity, but at their core, they all follow the same basic principle: observe, decide, act. What differentiates them is how they make those decisions:

• Simple reflex agents: Respond directly to stimuli. Ideal for straightforward, rules-based tasks like data validation or alert monitoring.
• Model-based reflex agents: Use an internal model of the environment to make better decisions, such as predicting inventory shortages before they occur.
• Goal-based agents: Make decisions by evaluating how actions move them closer to specific goals, such as meeting a delivery deadline or reducing ticket backlog.
• Utility-based agents: Assess outcomes based on performance metrics (speed, cost, accuracy) to choose the most efficient path forward.
• Learning agents: Continuously improve performance through feedback, refining how they handle repetitive processes or anomalies.

Multi-Agent Systems

AI agents aren’t designed to manage every business process on their own, but they are designed to collaborate with other agents and systems to enable agentic automation at scale.

Benefits of Using AI Agents for Automation (And How to Measure Them)

At a high-level, the benefits of using AI agents to automate processes are obvious: businesses can save time and money by reducing manual work. But businesses can’t deploy AI agents based on vague promises of success. For agentic AI initiatives to be successful, businesses must be able to demonstrate clear and measurable ROI.

Cost & Time Savings

One of the easiest benefits to measure is how much time an AI agent is saving your employees. If you know the average time it takes an employee to complete a task — like approving a purchase order or processing an invoice — you can calculate how many hours (and how much money, based on hourly employee costs) are being saved by having an AI agent complete that task.

Improved Customer Satisfaction

Quantitative metrics like time and cost savings are important for understanding ROI, but qualitative metrics like customer satisfaction can be equally valuable. AI agents can contribute to better customer experiences by reducing response times and providing more accurate recommendations — and these improvements can be measured through customer surveys and retention rates.

Better Employee Experiences

AI agents aren’t replacements for human employees. Done right, agentic systems can actually improve employee satisfaction by reducing the number of repetitive, time-consuming tasks they have to complete. As with customer satisfaction, surveys are a valuable tool to measure how employees feel about AI agents — but businesses can also look to agent adoption rates and task completion rates to better understand how AI is contributing to operational success.

Challenges to AI Agent Adoption

As AI becomes more advanced, the associated security risks become more complex.
Enterprise environments — which use over 1,000 applications on average — are particularly vulnerable, and simply cannot afford to take a laissez-faire “implement first, ask questions later” approach to AI. With so many applications in play, there’s also the issue of disconnected, siloed data making it difficult for new AI tools to integrate effectively and access the information they need.

Security vulnerabilities

Without proper oversight and governance, autonomous AI agents can unintentionally expose private information, execute harmful actions or create new attack surfaces within the enterprise.

Data silos

An AI agent’s ability to solve problems, make decisions and act autonomously is what makes it, well, agentic. If the AI runs into inconsistent or incomplete data, it will do its best to fill in the gaps and infer the best course of action — but the farther the AI gets from an authoritative source of truth, the more likely it is to make the wrong assumptions and make mistakes.

The Solution: A Layered Approach to AI

Deploying AI agents in an enterprise environment is challenging, and poorly planned initiatives driven by hype are doomed to fail. In fact, Gartner is already predicting that 40% of agentic AI projects will be canceled by the end of 2027.

Enterprise data that is spread across hundreds of applications and systems demands a layered approach to AI.

• The first, core layer is Jitterbit Harmony, our AI-infused, low-code integration platform that connects disparate systems to create a single source of truth and ensure seamless data flow between applications.
• The second layer consists of autonomous AI agents that leverage the integrated data and workflows facilitated by Harmony to operate effectively and securely.
• The third layer is infused AI — AI capabilities embedded within existing applications.

This layered architecture anchors your AI agents to accurate data and improves governance while still allowing flexibility to add extra AI tools on top.

Use Cases in Enterprise Automation

Practical examples of workflows where agentic AI delivers measurable results include:

• Human resources: Automate onboarding with agents that create accounts, provision systems, and send personalized welcome emails — ; all triggered from a single HR workflow.
• Supply chain management: AI agents predict inventory needs, reorder materials, and synchronize data between ERP and logistics systems to reduce stockouts and delivery delays.
• Finance and accounting: From invoice reconciliation to expense validation, agents can manage repetitive financial tasks while maintaining real-time reporting accuracy.
• Customer service: Agents automatically classify support tickets, suggest resolutions, and escalate high-priority cases to the right team, improving response times and satisfaction.

How to Get Started with AI Agent Automation

Build a Strong, Integrated Foundation

AI agents can’t be slapped on top of disconnected systems and messy data and be expected to deliver value. Creating a strong foundation for AI deployment means connecting applications, cleaning and standardizing data, and establishing clear governance across the enterprise.

The Jitterbit Harmony platform makes this easy by providing a low-code, AI-infused integration layer that links disparate systems, creating a single source of truth that AI agents can pull from.

Pre-Built AI Agents

For businesses looking to accelerate adoption, pre-built AI agents are a great place to start. In the Jitterbit Marketplace, you’ll find secure, enterprise-ready agents for:

• Sales: Automate lead tracking, follow-ups, and pipeline updates.
• Knowledge Management: Automate employee access to knowledge bases, documentation sites, business systems, and other data repositories.
• HR: Automate employee onboarding processes, from hardware procurement to role-based training.

Custom AI Agents

For more specialized use cases, custom AI agents offer the flexibility to go further. Jitterbit’s Agentic AI Professional Services pair your business with a team of AI and ML experts that will design, test and deploy agents designed around your unique data, goals, and operational challenges.

Deploy AI Agents at Scale with Jitterbit

From fully autonomous AI agents to embedded AI assistants, Jitterbit’s Harmony platform offers a comprehensive, AI-infused, low-code solution for enterprise automation.

Learn more about Jitterbit’s secure, layered approach to AI automation, or jump into a demo of our HR Agent to see our AI capabilities in action.