AI agents can manage a wider range of tasks than any automation tool yet developed, thanks to their decision intelligence and context reasoning capabilities. Agentic workflows, or processes where at least some of the work is automated by AI agents, make some IT leaders enthusiastic and give others pause.
There are valid reasons for both feelings. And the stakes are even higher when you begin orchestrating multiple AI agents. However, if you know where to employ caution, you can quickly reap the benefits of agentic workflow automation.
First, let’s answer the big question: what are agentic workflows?
The automation market is loud about AI agents, agentic workflows, and agentic orchestration. And as an emergent technology, definitions for concepts associated with AI agents are still evolving. For our purposes, agentic workflows simply refer to a process where at least a portion of the work is automated by AI agents, but can also include automation by other technologies like rules and bots. Agentic orchestration is the coordination of that work across multiple agents, systems, bots, and people in end-to-end processes.
1. Integration with data.
AI agents bring decision intelligence, reasoning, and autonomy to the automation landscape, all things a robotic process automation (RPA) bot can’t do. However, for your AI agents to move beyond mere task handling, they also need data. Agents are confined to their immediate environment, so if they’re limited to a single SaaS product, a local host, or a narrow set of integrations, that’s all they’ll know.
To provide their full value, AI agents have to be deployed within platforms that provide access to data from multiple systems. Then, they’ll have the context they need to reason effectively—pulling the right data, invoking the right functions, and executing complex tasks autonomously from end to end.
When you use Appian data fabric, a technology that allows processes and the automation tools used in them to securely access enterprise data from any system, you can build and deploy agentic workflows that provide AI agents with access to the data they need.
2. Integration into business processes.
Because of their need for context, your AI agents will only be as useful as the structure and data that surrounds them. This is the crux of agentic automation. Without the context and framework of a larger, end-to-end business process, AI agents can only do so much.
To create real business value, focus on embedding AI agents into processes where they can solve problems. A process also protects you from rogue agentic actions, because your agents will operate within the rules of your established process, including human approval steps and providing activity logs to track every action.
3. Data privacy and security.
While data is a necessity for successful agentic workflow automation, it also requires considering how to keep that data safe from misuse or incorrect access. With Appian, you can unify your data with a data fabric to create rich data sets. Once this data is unified, you can apply security controls at the row or column level, including controls for which data your agents can access.
Data security controls should be one of your top concerns surrounding agentic workflow automation. Your data must be secure, measurable, and in compliance with your internal policies, your government regulations, legal requirements, or your own industry standard policies. Consider these questions when reviewing agent workflow security:
- Data safety. Is sensitive data encrypted in transit and at rest?
- Data control. Do you have full control over what data your AI agents can access and use?
- Geographic and compliance boundaries. Does your AI agent provider meet your data residency requirements?
- Model safety. Does the provider guarantee that your prompts and data won’t be used to train foundation models? Are you working with a pre-vetted model designed to avoid bias, or are you working with a model that learns as it goes? If the latter, are you able to control it?
- Human in the loop. Are there escalation paths and human oversight when AI agents are used?
- Responsible AI policy. Does your AI provider give you access to logs, tracing, and source citations for your agentic outputs? Do they have clear policies for ethical and fair use?
4. Governance and human oversight.
One of the biggest risks in building agent workflows is a lack of governance. The work agents do must be visible and transparent. Otherwise, if anything unpredictable happens in an agentic workflow, you risk not knowing how to trace or fix the issue.
When you build an AI agent in Appian, you can see every process where that agent is used. You’ll know which instances have been run, where they were run, and who ran them, and what data the process interacted with. These logging capabilities keep you in control and able to explain AI agent actions.
You’ll also need end-to-end accountability: Who approved the decision that the AI agent made? When you have a human in the loop to review important decisions, you protect your processes and their outcomes. For example, an employee can prevent an AI agent from releasing inaccurate information that would cause issues downstream.
In Appian, every AI action and human response is tied to specific agents, users, and workflows. When you build agentic workflows in Appian, you get to make these choices for how you will build checks and balances into the process.
5. Scalability and performance.
You may want to use your AI agents to tackle high-volume, high-throughput processes. But not all agents are built to handle that volume of activity. With Appian, you can use Autoscale, an Appian Cloud capability that dynamically adjusts process execution capacity based on demand. It can run over 10 million processes an hour without over-provisioning resources. For example, if you want to use agents to extract document text, Appian AI agents can handle hundreds of millions of documents.
6. Continuous optimization.
Keep in mind: today's AI is quickly outdated. Your agent workflows and architecture will need to be continually refined. Rather than constantly researching new models for every task, rely on a platform like Appian. We are continually enhancing our platform, with new features and improvements released on a quarterly basis.
