Process automation has been around for decades, but the tools under this technology umbrella have multiplied over the years. Robotic process automation (RPA) was an early tool for handling simple, routine tasks, and it’s still powerful to have in your intelligent automation arsenal. But when technologies like intelligent document processing, business rules, and workflow orchestration entered the scene, they brought new capabilities to the process automation suite.
Now, the next tool for process automation has arrived, and it too brings powerful new capabilities. Agentic process automation refers to artificial intelligence agents working autonomously to perform dynamic tasks and meet goals by acting inside complex workflows.
Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI. And for good reason: agentic process automation is already yielding results at organizations like behavioral healthcare company Acclaim Autism, who applied AI agents to their onboarding workflow and cut patient onboarding time by 83%.
What is agentic process automation?
Agentic automation refers to the use of autonomous AI agents in business processes to understand context, make critical decisions, and execute processes without human involvement.
When we say an AI agent can “execute processes,” we don’t mean a full end-to-end enterprise process. Instead, agents handle subprocesses—individual steps in a larger business process. This includes repetitive tasks and complex workflows alike. For example, an agent might route emails based on keywords at the start of a support workflow, or extract data from unstructured documents in invoice processing.
Agentic AI’s capabilities fall somewhere between bots and humans. These autonomous agents can handle more complex tasks than RPA bots, making informed decisions based on context reasoning. They do still need human oversight in many cases to avoid risk, but they can still make independent decisions that unlock new levels of efficiency. They operate with more adaptability and intelligence than an RPA bot, and work as autonomously as you allow.
Traditional automation vs. agentic automation
Robotic process automation | Agentic automation | |
Autonomy | Performs tasks by emulating simple keyboard and mouse actions, such as logging in to a database and inputting spreadsheet data | Can manage larger business processes and perform a wide range of actions, such as assigning work to a human or calling a system API (and can even employ RPA in the process) |
Adaptability | Can complete only repetitive, rule-based tasks | Can independently interpret tasks with minimal need for defined structure |
Decision-making | Runs on simple logic with predefined rules and repeatable steps | Capable of more complex reasoning, can understand what to do in processes where actions have many potential outcomes |
While RPA will continue to be a valuable part of any process manager’s tool kit, agentic capabilities expand the reach of business process automation. Forrester Research describes the difference between agentic workflows and traditional automation tools: “Today’s process tools rely on brittle customization and configuration. Exceptions and deviations must be explicitly configured in the system. Agentic systems can adapt to the dynamic and unpredictable nature of real-world processes. In short: the way things actually get done.”
Why orchestration matters in agentic process automation
AI agents are powerful. They can make autonomous decisions involved in workflows like interpreting customer inquiries, processing claims, and completing complex administrative tasks.
But there are caveats. The “process automation” part of the equation is important. AI agents must operate within an orchestration layer that allows human intervention when needed. And they must be built on a strong foundation to ensure effectiveness, safety, and scalability.
Consider these five elements of a good agentic AI deployment:
1. Data
AI agents work best when they have as much context as possible from the data. This requires a strong data management foundation. Clean, structured, and accessible data enables agentic systems to make accurate, context-aware decisions.
Unfortunately, “clean, structured, and accessible” doesn’t describe the data situation at most organizations. Accenture research found that weak data foundations are holding companies back from AI, with 61% of the sample reporting that their data assets are not ready for generative AI yet, and 70% saying they find it hard to scale projects that use proprietary data.
But with Appian data fabric, organizations can build and deploy AI agents that have access to any enterprise data, including in external systems. This lets AI agents make smart, contextual decisions without requiring human input. Plus, Appian data fabric lets you set permissions to keep agents and humans from accessing unauthorized data.
2. Safety
Without guardrails, AI agents can go off track. You may not want agents independently making every choice in a process. That's where the rest of your process automation platform comes into play. It lets you add human oversight to steps in a process that could be risky. For example, AI agents can find and process information for an insurance claim, but a human can still review for accuracy and adherence to company policy before making final payouts. This minimizes the chance of AI making biased or incorrect decisions in complex business processes.
Finally, make sure you have a private AI architecture in place. Private AI ensures data never gets shared with third-party systems or leaves your enterprise compliance boundary. You remain in control of your data. And since AI belongs in a process, your process platform should have strong security and compliance controls in place to protect data across the enterprise.
3. Analytics
AI agents need feedback on their performance. Humans need to verify whether the agent makes the correct decisions to ensure optimal performance over time. You can use analytics to test AI agent performance prior to deploying into processes. But continuous feedback loops are essential even after deployment. Analytics allow you to watch over the outcomes of AI agents to ensure they’re accurate and help meet the goals of the business, such as lowering operational costs or improving customer service response times. A good analytics solution—like Appian Process HQ—lets you measure processes and AI agents against goals for efficiency, accuracy, and even ROI.
4. Scalability
Taking a platform approach to process automation, where agentic automation is just one puzzle piece, makes it much easier to scale. Rather than launching in disconnected pilots, agents can be deployed in processes across the enterprise. Additionally, a process platform provides modular architecture, flexible APIs for seamless integration, and cloud-native infrastructure that lets agentic automation grow as business needs change.
5. Ease of use and maintenance
When AI agents are part of your process platform, you can deploy them where they’re needed and use other tools where they fit best. This reduces cost and complexity. And with a low-code approach to agentic automation, systems are intuitive to build and maintain, with version control and easy updates built in.
Best practices for implementing agentic process automation
If you’re raring to go on agentic process automation, start with an investment in infrastructure. To implement AI well in your business, particularly agentic AI, analysts tend to recommend that you:
Build a strong data and technology foundation. Data fabric technology gives AI agents access to data from across your systems.
Establish governance and trust mechanisms. A process platform makes deploying AI agents easier and safer.
Align AI agent deployment with strategy and high-value use cases. Document extraction or classification is a good use case that appears often in enterprise processes.
Integrate humans into AI workflows. Keep humans in the loop to review decisions.
Get all your stakeholders involved from the start. Ensure early collaboration between engineering, data, and business teams to ensure all critical capabilities meet business requirements.
Adopt a phased, responsible rollout. Start with proofs of concept where you can learn and adapt as you go. Watch USF’s Alice Wei explain their AI rollout in this Appian World video.
Implemented correctly, agentic automation makes process orchestration more effective, extending your enterprise’s ability to improve processes, maximize operational efficiency, and drive business outcomes.
Agentic AI will be a mover in reshaping business processes. It's the future of automation. Now the question becomes: what process will you start with?
