Agentic AI is becoming a hot topic in intelligent automation circles. It is sparking more excitement than even Generative AI. This enthusiasm is understandable because the technology adds to the ever increasing range of business process automation tools that organisations have at their disposal to optimize operations and move the dial on strategic achievements.
Agentic AI is a reimagined blend of different types of AI, generative or otherwise, integrated with other intelligent automation tools and guardrails to automate business processes end-to-end.
Agentic AI (also referred to as AI agents or autonomous AI) is not a new concept, for example we already have it in the form of autonomous vehicles and even autonomous recruitment agents. Moreover, some Robotic Process Automation (RPA) vendors have offered pre-coded task specific bots, like accounts payable bots, in their robot marketplaces for years. What’s different today is that organisations can build intelligent agents powered by sophisticated models thanks to developments such as transformers.
Process intelligence software has been around for decades too but has become more powerful with more advanced AI embedded in it.
When process intelligence is combined with agentic AI the idea of self-improving and adaptive processes edge closer to reality.
Read more: Process Intelligence: The Ultimate Guide to Transform Business Operations in 2025
What is Agentic AI?
Agentic AI or autonomous AI is a type of artificial intelligence that can complete a process and make decisions in relation to it independently of humans. These systems learn from data, adapt to changing conditions, and perform tasks that historically require human intelligence. When complemented with process intelligence, they can become adaptive to dynamically respond to issues to improve their processes.
What is Process Intelligence?
Process intelligence refers to actionable insights generated by software solutions like KYP.ai when they capture process data and analyze it. The findings highlight inefficiencies or bottlenecks. They help managers and agentic AI make informed process refinements to optimize workflow, ultimately enhancing business outcomes.
How Agentic AI and Process Intelligence Could Work Together
You can make agentic AI more powerful by combining it with process intelligence. The following points highlight how the two would work together:
- Process intelligence can automatically discover and document manual workflows, creating virtual models that show the steps involved. The information enables agentic AI to understand the process context and workflow, helping it learn and start to make smarter, informed decisions.
- Process intelligence can monitor the activities of AI agents where they take advantage of traditional RPA to interact with other software through their user interfaces, or by analysing enterprise software logs. Accordingly, process intelligence can deliver insights into the performance of AI agents and highlight any points of friction in the workflow for further action and rectification.
- Thanks to process intelligence, agentic AI can monitor how its outputs affect downstream tasks. For example, if it is processing cases faster than they can be handled by downstream tasks and creating a bottleneck. This creates a feedback loop that enables AI agents make real-time adjustments to their own work to maintain smooth operations.
Use Cases for Agentic AI and Process Intelligence
Examples of how agentic AI and process intelligence could work together include:
- Order Fulfilment: In one organization an AI agent automates order fulfilment processes that include routing, inventory tracking, and delivery scheduling. Process intelligence monitors the entire process flow, identifying bottlenecks such as frequent slow responses received from a component supplier. The agentic AI can check these insights then make a decision about what action to take, for example if it should purchase from another approved supplier of components.
- Claims Processing: Agentic AI automates insurance claims processing by reviewing cases, verifying information, and making decisions. Process intelligence tracks processing times, highlights inefficiencies, and provides data-driven insights to the AI agent to optimize the operations. For example, in health insurance, process intelligence highlights a recurring problem of wrong or duplicated invoices sent in by a provider that leads to exceptions and rework. The AI agent can then make a decision to raise the issue to a human or add extra checks earlier in the process for that specific provider in order to prevent problems later.
Benefits of Combining Agentic AI and Process Intelligence
When you combine agentic AI with process intelligence you create a capability to continuously optimize and automate processes. You move into a new realm of process design and modernization – beyond the traditional “define once and update occasionally” approach, creating self-aware, adaptive workflows.
Key benefits include:
- Built-in Efficiency: Agentic AI and process intelligence working together can create a continuous loop of monitoring and refining, driving ongoing efficiency gains.
- Cost Reduction: When AI agents are integrated with process intelligence you get improved efficiency on a loop that reduces operational costs.
- Increased Productivity: It is a given that automating repetitive tasks frees human workers to do higher value work. Process intelligence takes the opportunities for improvement further by providing employees with insights on how to improve their own performance and productivity. If given access to low-code development tools, employees would be able to create their own AI agents for their specific use, guided by process intelligence. In fact, some organizations that provided RPA development tools to their employees are considering the possibilities of doing the same with low code agentic AI kits as they become available. Their aim is to empower employees to help themselves but strictly within guardrails and governance frameworks.
- Better Decision-Making and Auditability: Informed by process intelligence, agentic AI can make better decisions. The organization in turn gains better visibility into the decisions that AI agents make and their auditability, e.g. at what point in the process did an AI agent make a decision and what factors were at work at that point?
- Enhanced Satisfaction: Efficient processes make for better customer and employee journeys. The result is higher satisfaction levels by both sets of stakeholders.
Why Agentic AI and Process Intelligence Are Greater Together
In summary, the combination of agentic AI and process intelligence, as delivered by KYP.ai, can be far more powerful than either technology on its own. More importantly, it is crucial to swiftly identify valuable opportunities through data-driven assessments at scale, thereby securing the expected agentic ROI and business impact. This synergy provides organizations with significant improvements in business outcomes, productivity, cost savings, and overall customer and employee experiences. As AI agent technology continues to evolve, the pairing of these technologies is set to avoid old pitfalls and more importantly unlock new, innovative applications in the future.
Becky Brown 
Sarah Burnett 
