AI agents are rapidly becoming a stimulus behind intelligent enterprise workflow automation. From customer inquiries to multi -agent orchestration, the multi -phase of business processes organize. But since these AI agents carry out more responsibilities, their performance is firmly available on how fast they can recover and implement data in the enterprise system.
The same Parallel processing Is a game changer. Introduced in the tool builder of the Core. Agent platform, this capacity allows AI agents to perform multiple tasks simultaneously with tools instead of implementing each step in order. Result? Fasten, smart and more efficient agents who respond in real time and on the enterprise scale.
Setting upright issues
Before parallel processing, AI agents were restricted by the sequel task model. We say an agent needs to bring information about the user. In the traditional workflow design, the agent will be forced to wait for the first recovery to be completed before starting the second start, etc.
Each step can take 5 seconds, resulting in a Delayed in 15 seconds. Before the agent takes the next action. This delay directly affects the user’s experience and the real-time AI-driving aid promise is harassed.
What is parallel to AI agents?
Parallel processing AI fixes this barrier by enabled to launch agents Free work simultaneously. As the required input – as the user’s ID is available, the agent can take advantage of the tools tools to mobilize the data simultaneously, which is obtained from multiple systems without waiting for someone to complete before the next start.
Since these systems (such as, sales force, CRM, and help desk) work freely and they have no rely on each other, the agent can inquire about them simultaneously. Instead of 15 seconds of waiting, the agent receives all the necessary data On average only 5-6 secondsTIME it takes time The longest Of parallel requests to resolve.
This fundamental change in implementation increases the performance of AI agents dramatically. They not only recover the information quickly but also work on it faster, which makes better decisions and more fluid conversations or processes. It’s not just sharp – it’s a scale operational intelligence.
Parallel execution example: AI agent in Customer Service
Create a Virtual Customer Service Agent image designed to help users with personal support. Being effective, the agent should understand the current status of consumers, recent purchases and historical interactions – which live in many backward systems.
With Parallel processingThe agent immediately sends three parallel data requests. Within 5 seconds, the agent receives and syntheses a full customer profile, which allows the user to respond quickly and accurately.
On the contrary, a traditional agent working with the ordering process will take three times more time to collect the same information-delay the answer, harass the user experience, and possibly cause drop-off or frustration.
Parallel implementation opens the reaction to a new level, which gives AI agents the option of providing sharp, personal and context-familiar dialogue-whether in customer service, sales, or internal operations. This can be used in conjunction with customer service agents AI for serviceA business solution to automatically, personalize and distinguish customer service interactions.
Key benefits of parallel processing for AI agents
Parallel execution just doesn’t make the workflow faster – it makes AI agents Smart and more expanded. When the agents can collect, process and process the data obtained from a number of sources simultaneously, the entire automation pipeline becomes more efficient.
It also helps reduce the burden and resources consumption by eliminating unnecessary waiting hours. AI agents who had to “wait in the line” to perform the tasks first, can now work with their full potential, which provides real -time insights and actions throughout the enterprise.
How does it work in Corey.i’s Toll Builder
Kore.ai Agent Platform Now supports creation Independent workflow branches Its nine code inside the toll builder. Each branch represents a work or action that does not depend on others. When parallel processing is activated, AI agents can start all these branches at the same time.
Once all branches are complete, the platform changes the results with intelligence, which enables the agent to move forward with the next steps – whether it presents information to a user, decides, or triggers another system action. The logic of such execution is essential for the construction of powerful, context -familiar agents that are on a scale with the complexity of the enterprise.
Why is it necessary for AIwork Flow Automation
Since enterprises measures their use of AI agents in departments and workflits, speed and performance are no longer good with good. Those missions are fragile. Whether it is reducing the waiting hours in customer support, accelerates the process of processing the ship in HR, or enabling the operations rapidly in operations, the reaction is directly linked to business results.
Parallel processing AI works one of the largest friction points of automation: delay from sequence processing. By eliminating artificial delays between the stages, parallel implementation ensures that AI agents can work with the speed and intelligence needed in today’s, multi -system enterprise environment.
Why does it matter here:
- Real -time response: In the scenarios where counting of every second – such as routing support tickets, handling fraud warnings, or action -related inquiries – helps parallel to respond immediately.
- Extended automation: As the workflows are more complex, it includes dozens of tools and systems, the ability to run tasks simultaneously ensures that performance does not deteriorate with complexity.
- Better user experience: Fastest agents mean smooth, more natural conversations and processes – high satisfaction, engagement and maintain.
- Input increases: When the agents complete the work faster, you can handle more volume with the same infrastructure – reducing operational costs while increasing capacity.
Recently. , Parallel processing AI agents convert to task runners into intelligent archeters. This is a basic ability to scale AI-driving automation without compromising performance or user experience.
Want to see parallel processing in action? Request a demo Or discover how your AI agents can change the way you can.