The Kognitos platform delivers unprecedented value in enterprise agentic automation efforts by introducing the hyperautomation lifecycle (HAL) which manages the complete lifecycle of agents and drastically reduces the maintenance that has limited the viability of automation in the past.
As demand for agentic automation explodes, Kognitos empowers CIOs to overcome the limitations of incumbent automation technologies and to become the champions of AI efficiency within their organizations. The hyperautomation lifecycle—comprising auto-write, auto-test, auto-deploy, auto-monitor, and auto-debug capabilities—allows organizations to scale automation initiatives to virtually unlimited potential.
To developers, software debugging is widely regarded to be the most time consuming aspect of the software development lifecycle (SDLC), and every CIO knows that debugging is a massive expense. A 2017 Cambridge University study reported that even at that time, over $312B dollars was spent annually in finding, fixing, and mitigating software bugs. That figure has only grown since then.
Automations are a form of software like anything else, so imagine what it would be like if your process automations performed their own-debugging without so much as a mouse click from your developers?
Kognitos auto-debug a fully automated debugging capability that helps users quickly identify and resolve errors in their automation processes without requiring deep technical expertise. In short, it quickly and easily solves the biggest pain point in the lifecycle.
Let’s assume, conservatively, that developers spend roughly 30%-50% of their time debugging. Instead of spending time identifying, reproducing, analyzing logs, testing, and implementing code changes, Kognitos uses the auto-debug feature to:
When Kognitos needs help from a human, it asks for input. As time goes on, the auto-debug feature grows more robust as it learns from previous exceptions and autonomously applies bug fixes to resolve any issues that arise, reducing the need for continued human intervention.
Because all debugging happens in plain English, the Kognitos platform is auditable and maintains a system of record accessible to business users. Not only is the automation self-healing, but it is self-aware enough to identify and fix bugs in real-time, without intervention, and then can explain exactly how and why it fixed an issue.
Furthermore, business users can request fixes or incremental improvements to code, eliminating the risk of future downtime and allowing team members to quickly and easily scale automation initiatives rather than devoting precious time and resources to constant debugging. The auto-debug feature allows organizations to support orders of magnitude more automation by removing the most painful part of the automation process as many organizations know it today with incumbent technologies like robotic process automation (RPA).
Kognitos’ auto-debug feature dramatically reduces expensive maintenance costs of RPA—largely driven by the time and energy spent debugging automations. Our customers estimate that switching to Kognitos has helped save up to 90% of maintenance costs. Combined with the rest of the hyperautomation lifecycle, the impact is monumental. Kognitos becomes more efficient and resilient on its own, giving your already constrained team up to half of their time back, or more.
Your organization can finally ditch brittle bots and fragile RPA workflows with Kognitos. Our end-to-end agentic process automation platform creates self-healing AI agents capable of handling complex automations.
If you’re a forward-leaning CIO looking to incorporate agentic automation into your organization’s 2025 AI initiatives, reach out to the Kognitos team for a personalized demo of how we can address your use cases, or sign up for our community platform to experience it for yourself.
Kognitos’ innovative hyperautomation lifecycle (HAL) platform offers an end-to-end solution that automates the entire lifecycle of automation and sets Kognitos apart as the leader in agentic process automation (APA). Built on a serverless infrastructure, HAL combines generative AI with deterministic logic to deliver reliable, repeatable, and hallucination-free AI agents.
The hyperautomation lifecycle begins by auto-writing a workflow based on an SOP, simple instructions, or a predefined prompt. Your process becomes a powerful AI agent, completely managed within the HAL platform. Processes are then auto-deployed with the click of a button, instead of months of setup and testing as with traditional RPA deployment. Any updates to your AI agents are auto-tested to validate that they will function reliably under any conditions, including edge cases.
Then what? Let’s explore the next stage of the hyperautomation lifecycle: auto-monitor.
As soon as an automation is deployed, HAL’s auto-monitor stage continuously observes and assesses performance, health, and security of every active automation within the platform.
The auto-monitor stage of HAL creates an accessible system of record in plain English. Business users can easily understand the status of their automations—no technical expertise or coding knowledge required. This reduces IT bottlenecks and simultaneously democratizes access for key stakeholders within the organization.
HAL’s proactive approach to exception handling identifies potential issues without breaking the automation. The system notes exceptions, pauses, and asks for human guidance if needed. The review process is quick and easy, so AI agents can deliver consistent results at peak efficiency.
Auto-monitor’s impacts extend beyond simply observing and flagging active automations. The hyperautomation lifecycle is a self-maintaining ecosystem where each stage can actively communicate with the others. For example, auto-monitor notes issues and gets a human involved when it needs help. Over time, the need for human intervention reduces. The Kognitos brain learns how to auto-debug similar exceptions, auto-write new automations to address issues, and auto-test edge cases after adjustments are made.
This creates truly autonomous AI agents capable of improving performance over time without the need for manual intervention.
With HAL, organizations can:
Transform the way your organization approaches automation by ditching fragile RPA workflows and exploring HAL. Kognitos stands apart as an end-to-end APA solution that automates tasks, but also manages the entire lifecycle of AI agents.
If you’re a CIO or technology leader looking to adopt AI that can provide massive ROI in months, not years, reach out to our team to book a custom demo of Kognitos’ HAL.
Organizations using robotic process automation (RPA) know the time and effort that goes into successfully deploying a new process. In fact, there is a long-standing statistic from EY that reports that over 50% of RPA projects fail, many of those never getting out of an early proof of concept phase. Understanding why that is the case is important to appreciate how AI-native solutions like Kognitos completely transform the experience for today’s IT teams.
Let’s cover the challenges to the incumbent RPA process deployment. Assuming there’s already a server in place, it looks something like this for a Windows-deployed bot:
This is a simplistic view of the effort required and doesn’t even account for the time spent developing, debugging, testing, and packaging the automation prior to deployment. Until now, this RPA process has been the de facto automation solution on the market because of its ability to handle automation workflows, despite the challenges presented by actually deploying a single process and the limitations in complexity for the use cases it serves.
Kognitos offers an alternative to the time-consuming deployment process of traditional RPA solutions with our hyperautomation lifecycle (HAL) platform.
Kognitos’ HAL platform solves even the most complex automation challenges in natural language, without the headaches of initial deployment and the maintenance challenges that inevitably arise with RPA. Powered by a neurosymbolic brain, HAL combines the creativity of generative AI with deterministic logic to create powerful, self-maintaining AI agents.
Enterprise organizations looking to streamline workflows or eliminate the roadblocks of other, legacy automation solutions should look to HAL as an end-to-end agentic process automation solution. HAL is capable of automating the entire lifecycle of automation during these five core stages of the lifecycle:
Auto-deploying workflows to production on HAL is wildly different from deploying traditional RPA. It can dramatically shorten implementation time and reduce maintenance headaches. Here’s what it looks like to deploy your first automation with Kognitos’ HAL:
It’s that simple.
HAL auto-deploys workflows from playground to production effortlessly. The platform runs on an invisible cloud infrastructure, so there’s minimal setup required. And when AI agents are deployed to production with the click of a button, there’s no risk for human error during packaging or deployment
Auto-deploy saves time, reduces costs, and improves reliability. HAL is revolutionizing how businesses approach enterprise-grade automation. For CIOs looking to stay on the forefront of agentic process automation, Kognitos provides a powerful solution.
If you’re ready to experience the power of Kognitos’ HAL platform, sign up for our community version of HAL, or reach out to a member of our team for a personalized demo for your use cases today.
If your organization relies on traditional robotic process automation (RPA) tools like UiPath, you might be grappling with hidden costs, fragile workflows, and developer bottlenecks. It’s time to ask: is there a smarter way to automate?
At Kognitos, we’re redefining automation with our AI-native HAL (hyperautomation lifecycle) platform that turns simple instructions into self-maintaining AI agents. We’re automating the most complex processes in natural language with enterprise scalability. Forward-thinking CIOs are making the switch. Here’s why you should consider it, too.
There’s no doubt that UiPath revolutionized RPA, but its limitations have become impossible to ignore:
Sound familiar? You’re not alone.
Kognitos isn’t just another RPA tool—it’s a paradigm shift toward agentic process automation that’s actually in production. Here’s how we outpace UiPath:
Kognito’s HAL platform doesn’t require specialized developers to create a workflow. Business users input simple instructions using natural language and HAL auto-writes the automation. For example, writing an automation might be as simple as typing “Process invoices from Outlook, validate amounts, and update SAP.”
There’s no learning curve with HAL’s intuitive interface. CIOs and technology leaders can choose to empower HR, Finance, and Operations departments to build automations themselves.
Our AI agents are adaptive, automatically adjusting and addressing any UI changes, differing data formats, or updates to processes—without manual intervention. Automations can become fully autonomous while also maintaining a trusted system of record.
HAL’s auto-debug and auto-test features fix errors in real-time, reducing maintenance costs by up to 80%.
Kognitos can integrate directly via API with your systems, like SAP, Salesforce, and more, for faster and more reliable workflows.
Our serverless infrastructure provides scalability to your organization, while also maintaining security with staging environments, role-based access, and audit trails to ensure compliance.
Deploy automations in days, not the months you can expect with UiPath. One Kognitos Fortune 500 client automated their invoice processing workflows in three days, versus six weeks on UiPath.
Provide a faster path to ROI with 50% lower total cost of ownership (TCO) as compared to incumbent RPA solutions like UiPath. Overnight, Kognitos can help slash licensing, developer, and maintenance.
Legacy RPA tools like UiPath aren’t AI-native, and are trying to adapt to the AI era. As competitors adopt agentic AI and hyperautomation, clinging to outdated platforms risks loss of efficiency, missed opportunities, and stagnation of innovation.
Let’s play out a scenario. You’re a CIO tied to UiPath. Manual bot upkeep drains IT and developer resources. Not only do they lose efficiency on maintaining brittle RPA workflows, they become a bottleneck for the organization. You miss opportunities at every turn to automate complex, dynamic processes, because you don’t want to overwhelm already strained developers. IT teams are stuck fixing bots, not driving strategy. IT is seen as simply a cost center, instead of a team bringing innovation to the organization.
Worried about disruption? Don’t be.
Kognitos offers a free pilot program, so CIOs can test-drive the platform with their most painful UiPath workflow. As your company migrates from UiPath, our team supports in converting existing automations to achieve zero downtime. Lastly, our dedicated onboarding team will provide you with enterprise training and on-demand resources to set your team up for success.
The future of automation isn’t about more code—it’s about more clarity.
Kognitos turns business users into automation heroes, slashes costs, and keeps workflows agile in the face of change. Are you ready to leave UiPath’s limitations behind? Request a 15-minute demo or sign up for free community trial access to HAL to see how Kognitos can transform your automation strategy in weeks, not months.
Automate smarter. Automate simpler. Automate with Kognitos.
Agentic process automation is quickly becoming reality, and businesses are actively pursuing applications in high value use cases in order to stay competitive. Testing workflows for reliability across multiple scenarios—not to mention accounting for edge cases—can be time-consuming and expensive.
Kognitos’ auto-test feature is a game-changer for automation as we know it. It is designed to automatically simulate various scenarios and edge cases to ensure the automation will perform as expected, simplifying and accelerating the process of creating autonomous automation workflows.
Kognitos is an end-to-end neurosymbolic AI platform that automates every stage of the automation lifecycle into one seamless system, eliminating the need for prolonged implementations and taxing upkeep. It combines cutting-edge generative AI with deterministic logic to create AI agents capable of automating even the most complex processes.
Business users are empowered to automate any process without bots, specialists, coding expertise, or extensive infrastructure. This blog dives deeper into the auto-test feature.
As automations progress through Kognitos’ patented process refinement, AI agents continuously optimize workflows as needed. Auto-test is akin to regression testing in the traditional software development lifecycle. As changes are automatically applied to an automation, auto-test validates that workflows will continue to function reliably under any conditions, particularly after significant updates to an automation already in production. Here’s how it works:
After code changes or updates during the auto-write or auto-debug stages, auto-test will generate and run simulations for a multitude of scenarios, including edge cases. This ensures that automations can grow to be fully autonomous where desired, while continuing to perform as expected under different conditions without manual intervention.
Let’s explore an example. A user might automate 1,000 runs a day processing insurance claims. When Kognitos meets an exception, a business user reviews and applies a learning to that specific run using the auto-debug and auto-write features. Because the automation has now changed substantially, the AI automatically reviews similar runs before applying that learning to all future runs. Let’s imagine that Kognitos chooses, say, 200 previous cases to auto-test. The results validate that the expected outputs are produced with the updated automation in place, and that it can be applied to all future runs.
Any business user can initiate and review tests. The interface displays test results in plain English, so automations are accurate and reliable without the need for technical coding expertise. Within the test suite, Kognitos shows an easy visual comparison for automation outputs following a change. When outputs are validated against expected results, the run is color-coded green to make it easy to read at a glance.
In contrast to the hours or days spent regression testing legacy robotic process automation (RPA), Kognitos can auto-test workflows in minutes. As AI agents auto-write and auto-debug automations, auto-test serves as a litmus test before applying similar changes to all future runs. It finds use cases for previous runs and tests them, for example, 200 times to validate the updated automation. If there are unexpected outputs, a business user can quickly validate and make adjustments based on the test results, then instantly run the simulations again.
Manual testing is labor-intensive and prone to human error. But beyond that, AI systems have been known to hallucinate from time to time. Auto-test addresses each of these concerns in turn. Not only has your business saved significant time and resources, but this process also verifies the integrity of the results. Kognitos is revolutionizing how businesses approach automation by making it faster, simpler, and more reliable.
If you’d like to learn more about Kognitos, reach out to a member of our team or sign up for free community trial access today.
Kognitos’ HAL (hyperautomation lifecycle) platform is a game-changer for organizations looking to implement agentic AI automation, save time, deliver value, and reduce technical debt. As point solutions for automation and AI agents emerge, Kognitos stands apart as a platform that meets complexity head-on, while remaining user friendly.
HAL is automating the entire lifecycle of automation, beginning with its innovative auto-write feature.
At its core, hyperautomation enables organizations to automate processes at scale. The demand for automation has always been high, but the resources required to support traditional automation kept large-scale adoption out of reach. HAL is designed to bring together each stage of the hyperautomation lifecycle, providing a single, integrated solution that not only automates tasks, but also manages and optimizes them.
The platform is built upon a cutting-edge, neurosymbolic hybrid AI that combines the creativity of generative AI with deterministic logic to deliver accurate, repeatable, and hallucination-free AI agents. In short, HAL is an end-to-end automation solution. The five steps of the hyperautomation lifecycle are:
Let’s dive deeper into HAL’s auto-write functionality.
Each new automation created in HAL begins with the auto-write feature. Users can create sophisticated workflows from simple instructions, eliminating the need for complex coding or technical expertise.
Where in the past a new process automation may have required months of planning and design, with HAL you can have an automation with as little as a prompt or SOP (standard operating procedure) document. Here’s how it works:
Users input their SOPs or simple instructions into the platform in plain English. HAL can even prompt users to auto-generate instructions for common use cases like generating reports in Excel, processing invoices, data reconciliation, and more.
HAL reviews the prompt or the instructions you provide, then generates a detailed plan in deterministic English, so both the machine and the human can understand. This crucial, intermediate step is what makes AI trusted, auditable, and safe for use in mission critical business processes.
After outlining the steps above, HAL auto-writes the automation. You review the automation, and simply click “Apply” if everything looks correct.
To run your automation through the rest of the stages of HAL, click “Accept all” and then run the process. It’s that simple. Start to finish, you can create your agent in minutes.
Auto-write makes creating powerful enterprise automations simpler than ever before. It can streamline a variety of use cases. Our customers have found particular success with intelligent document processing (IDP) and Excel-based tasks, such as invoice processing and data consolidation.
Instead of investing time and resources on programming bots, HAL can create self-sustaining AI agents capable of automating complex business processes. If you have an SOP, you now have an AI agent.
HAL and its auto-write feature are revolutionizing the way the industry does automation. If you’re ready to experience the power of auto-write and the full capabilities of HAL, sign up for free community trial access to HAL, and create your first AI agent today.
Hyperautomation and agentic AI will allow organizations to streamline processes, boost efficiency, and stay competitive in ways that haven’t been seen before. Enterprise technology leaders have a clear opportunity to harness these recent technological developments to drive organizational success by combining the power of hyperautomation with the execution of AI agents.
Originally coined by Gartner, hyperautomation is more than just another buzzword in the automation landscape (see: agentic AI)—it’s an enterprise-level strategy that enables automation at scale. Whatever can be automated, must be automated.
Hyperautomation was originally conceived of as a network of tools to automate virtually every process within an organization. In the years since Gartner first introduced the concept, the definition has shifted slightly. Rather than relying on a complex tech stack to automate workflows within an organization, many leaders are looking to consolidate incumbent platforms while still achieving the same level of automation.
Incumbent automation providers have met challenges every step of the way with slow progress, high costs, limited scale, and fragility. Hyperautomation has the potential to overcome these challenges.
While many solutions offer automation components that work in tandem with additional platforms to hyperautomate processes, Kognitos’ HAL platform provides an integrated solution that encapsulates every stage of the hyperautomation lifecycle. HAL is designed not just to automate tasks, but also to manage and optimize them.
The concept of hyperautomation looks beyond what automation can achieve today to emphasize a continuous lifecycle of improvement. The hyperautomation lifecycle provides a systematic approach over these key stages:
HAL has created a unified automation ecosystem, as opposed to the disparate systems and platforms in legacy hyperautomation efforts. The cloud-based, scalable platform seamlessly integrates with existing technologies, allowing CIOs to execute upon hyperautomation strategies without overhauling their current tech stack.
Minimize exposure to external threats with HAL’s scalable infrastructure. Most endpoints, except for user interfaces (UI) and management APIs, are inaccessible through the public internet. The UI endpoints are safeguarded through API gateways, ensuring an added layer of protection. All this is made possible by the fact that our platform is underpinned by a serverless technology, allowing you to avoid maintaining underlying infrastructure, reducing costs and driving down total cost of ownership.
We combined intuitive generative AI with deterministic programming to create HAL—the trusted platform that combines accuracy with flexibility. Throughout the lifecycle, HAL can make autonomous decisions, but recognizes when it needs human input, and trusts the expertise of the team.
When HAL meets an exception, it asks for human input in natural language. Our patented conversational exception handling allows anyone who knows the process to provide the platform with feedback, so it can quickly adapt to drive perpetual refinement
For technology leaders eager to leverage cutting-edge technologies, the potential rewards of hyperautomation are immense: greater efficiency, enhanced operational agility, and the capacity to foster a culture of constant learning and adaptation.
As the landscape of business continues to change, hyperautomation is not just an opportunity—it’s a mandate. With Kognitos’ Hyperautomation Lifecycle platform, organizations have the means to not only catch up with the future but to lead it. Embrace hyperautomation with HAL and craft the dynamic, intelligent enterprise of tomorrow.
Agents. Agents. Agents. They’re everywhere. With the growing hum of agentic solutions tickling our eardrums, enterprise leaders are excited about all of the promising attributes of agents taking over their business processes. That excitement is warranted, because agents offer an excitingly low barrier to entry to what was previously a challenging automation space with solutions like RPA, IDP, or even Low Code/No Code. And then we start throwing out terms like self-healing, and that would lead anybody linked with enterprise automation to catch themselves drooling at least a little bit.
However, the excitement is influencing leaders into overlooking one primary, critical flaw in agentic AI solutions: process. In the people, process, technology framework, agentic addresses people and technology, but completely overlooks process. When an agentic solution uses a large language model (LLM) to execute a “process,” it looks for creative solutions to a problem. So if a user relies on an agent to execute a process 100 times, it will vary slightly each time. These are the glaring issues that come from a lack of process in automation that no one is talking about yet and why CIOs and other leaders need to tread carefully into an agentic future.
Imagine a car without a steering wheel. That’s essentially what enterprises are doing when they implement agentic AI solutions without proper human oversight. They feel like they’re in control, because they create the prompts and check some boxes to build the agent. But, what happens after the agent lacks control mechanisms like a steering wheel in a car, leaving users unable to effectively review or modify the AI’s planned actions.
This absence of control is particularly alarming in domains dealing with sensitive financial, legal, or healthcare matters. Think about how sensitive we are about mistakes of this nature with our flesh-and-bone human employees. We’re not very forgiving are we?
Just as no one would trust a self-driving car without an emergency override, enterprises cannot afford to rely on AI agents that operate as black boxes, making decisions that could have far-reaching consequences while we remain in the dark, particularly when there can be minor variations each time.
The whizbang features of generative AI are, in general, focused on the first part of all lifecycles and agents are no different. Agentic solutions are pitching how quickly and easily they can be spun up and get to work. The concept of “velocity to value” is thrown around wantonly. But again, this isn’t how the enterprise operates.
Maintaining AI agents is akin to building a skyscraper on quicksand. The challenge lies not just in the initial implementation but in the ongoing management and adaptation of these systems. Current agentic models offer no clear solution for maintenance, despite the fact that up to 95% of automation work after the initial creation lies in maintaining the processes.
The problem is compounded by the potential for cascading changes when modifying high-level prompts. Because users can’t easily control how agents function in detail, they must go to what they can control via prompt engineering. A small tweak to the prompt could lead to an entirely new execution plan by the agent, with no clear visibility into the details. This lack of granular control makes it nearly impossible to implement minor adjustments without risking unintended consequences across the entire system. To that end, does the agentic solution have the testing to understand those impacts at the scale of thousands of automations per day? Perhaps not.
For humans, 95% is pretty good most of the time. But for AI agents, we won’t be able to overlook an error rate of 5% or even 10% in complex use cases. AI systems are fundamentally imperfect. This inherent unreliability makes agentic AI solutions a ticking time bomb in environments where precision is paramount.
We don’t allow for many mistakes in multi-million dollar transactions in a financial services organization, nor should we. Even if that accuracy rate grows from 95% to 99% accuracy rate, a large enterprise could face hundreds of errors monthly, each potentially leading to significant financial losses or legal issues. If that was your bank, would you trust it? The reputational damage might represent the worst of it. The stakes are simply too high for such a margin of error.
The rise of citizen development in AI poses a significant risk to enterprise governance. Without proper oversight, employees across the organization could create their own AI agents, leading to a chaotic landscape of uncontrolled automation without clear process. CIOs only recently returned to glory after the era of shadow IT, and now they face their toughest adversary yet in shadow AI.
This scenario is analogous to allowing every employee to create their own version of critical business processes. It undermines the carefully crafted workflows designed by process owners and introduces inconsistencies that could jeopardize compliance and operational integrity. Agentic solutions suggest that everyone should create business process automations, and that’s simply not true for an enterprise. Rather, the thinking should be that every person should technically be able to create automations through the use of natural language and disappearance of complex coding bottlenecks, but only a select few should actually have that privilege with visibility from IT and operational leadership.
We all know that agents offer the value of increased adaptability and resilience in the context of handling exceptions, but that may not be enough. Enterprises are dynamic entities, constantly evolving in response to market changes and internal improvements. The lack of a clear learning philosophy and lifecycle management for AI agents means that as businesses change, these systems may become increasingly out of sync with organizational needs.
This misalignment could result in AI agents making decisions based on outdated information or obsolete processes, potentially leading to costly mistakes or missed opportunities. If businesses opt to simply transition from one agent to a new version, they will need to consider what that change management looks like.
The potential of agentic AI is undeniable, but the current state of agentic solutions makes it a risky choice for enterprise adoption, particularly in areas where accuracy and accountability are non-negotiable. The lack of process, human oversight, complex maintenance requirements, inherent reliability issues, governance challenges, and difficulties in adapting to business evolution all contribute to a perfect storm of potential failures.
At Kognitos, our HAL (hyperautomation lifecycle) platform provides the same benefits of agentic solutions without the challenges. Process is incorporated as the backbone of our platform, offering the same speed to value and cost-consolidation that has made agentic solutions an alluring option, and we have addressed the issues outlined here in ways that other agentic solutions simply can’t match.
Most importantly, Kognitos offers businesses the chance to truly standardize and automate their processes, while also allowing for adaptability. We identify a creative solution on the front end, then replicate the process exactly until HAL encounters a reason it can’t repeat it, then asks for guidance and works that into the process moving forward. Learn more here.
Hyperautomation, a term coined by Gartner in 2019, is defined as “a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible.” Even Gartner has deviated from this definition recently with the introduction of Business Orchestration Automation Technologies (BOAT) at the Gartner AIBS Summit in May of 2024, which adds a layer of orchestration on top of automation technologies.
Some of the in-market incumbent RPA vendors are attempting to perform a pivot to Agentic Process Automation (APA) to signal that they have fully embraced Agentic AI adaptability and speed, but in doing so, they present several new challenges on the way to hyperautomation.
As Gartner intended it, hyperautomation occurs upon successful implementation of multiple technology solutions—AI, LLMs, RPA, IDP, BPM, iPaaS, and more—to automate as much as possible. But this acronym soup leaves a bad taste due to its fragility, high costs, and maintenance challenges. Rather than using disparate solutions to accomplish full-scale enterprise automation, companies turned back to their incumbent automation solutions and tried to overlook its faults.
Despite the fact that vendors abused the term as a buzzword instead of seeing its full potential (see: agentic AI in the 2024 automation market), hyperautomation is possible.
Conceptually, hyperautomation promised to revolutionize business processes by enabling end-to-end automation. In practice, however, moving beyond the concept proved challenging for several reasons:
Robotic Process Automation (RPA) and Intelligent Document Processing (IDP) have both been mislabeled as hyperautomation solutions, rather than components of a larger hyperautomation strategy. While both technologies contribute to hyperautomation as the market currently knows it, along with many other technologies like AI and iPaaS, they are not hyperautomation in and of themselves.
RPA excels at automating repetitive, predictable tasks, but it lacks cognitive capabilities to solve complex processes that require human-like decision-making ability.
IDP is great at processing unstructured data from documents, but is limited in terms of use cases it can serve and frequently fails to integrate with larger enterprise systems.
Neither of these solutions constitutes hyperautomation, though both could contribute to a hyperautomation strategy alongside other technologies.
In order to stay competitive, anything that can be automated, must be automated. Kognitos brings the original vision of hyperautomation to life by automating virtually any IT or business process you can dream up, all with a serverless infrastructure that maintains a system of record accessible to any business stakeholder who can read in plain English.
Our Hyperautomation Lifecycle (HAL) platform doesn’t require integrating multiple automation technologies, because we’ve done that work for you on the front end. HAL can automate the entire lifecycle of creating automations, truly bringing hyperautomation to life, without the cost and headaches outlined in the original Gartner definition. For the first time, end-to-end business process automation is possible with one solution. Here’s how it works:
If you’re interested in learning more about how your organization can simply automate more, reach out to a member of our team for a customized demonstration of how HAL can work for you.
Agentic process automation signifies a leap beyond robotic process automation (RPA) and even early intelligent process automation (IPA) solutions. At its core, it refers to systems where autonomous “agents”—software entities powered by advanced AI reasoning—can independently plan, execute, and adapt complex workflows. These agents don’t merely replicate human actions; they understand the intent behind a process and can dynamically adjust their actions based on real-time data and unforeseen circumstances.
Think of it this way: traditional RPA is like a trained parrot that repeats specific phrases. Agentic process automation is like a highly intelligent assistant who understands the context of a conversation, can answer novel questions, and can even anticipate your needs. This intelligence stems from sophisticated AI models, particularly large language models (LLMs), which enable these agents to comprehend instructions in natural language. This capability is pivotal for true workflow management that can handle exceptions without constant human intervention.
To truly grasp agentic process automation, it’s helpful to trace the journey of automation itself.
The earliest forms of automation involved hard-coded scripts designed for specific, repetitive tasks. While effective for simple, unchanging operations, these scripts lacked flexibility. Any minor change in the process required manual recoding, leading to significant overhead.
Robotic Process Automation (RPA) emerged as a significant advancement. RPA bots mimic human interactions with digital systems, automating rule-based, high-volume tasks. They operate at the user interface level, clicking, typing, and navigating applications just as a human would. RPA proved valuable for tasks like data entry, report generation, and basic invoice processing. However, RPA remains largely rule-based and rigid. It struggles with unstructured data, exceptions, and processes that require decision-making beyond simple “if-then” logic. It is not an intelligent process control agent.
Intelligent Process Automation (IPA) integrated AI components, such as optical character recognition (OCR) and machine learning (ML), with RPA. This allowed systems to handle unstructured data, categorize documents, and even learn from patterns. While a step forward, IPA often still relies on pre-trained models and can require significant upfront configuration and ongoing maintenance. Exception handling, though improved, often still funnels back to human operators for resolution. It marked an improvement in workflow management, but the core limitation remained: the system rarely reasons about the task itself.
Agentic process automation represents the natural progression. It moves beyond simply following rules or learning from structured data. An agentic process automation platform leverages AI reasoning to understand the goal of a process, not just its steps. This means the system can:
This distinct ability to reason and adapt is what differentiates agentic automation from its predecessors, making it a powerful tool for complex workflow management.
The operational mechanics of an agentic process automation platform are fundamentally different from traditional automation. Instead of being programmed with every possible scenario, agentic systems are given high-level objectives in natural language.
At the heart of an agentic process automation platform like Kognitos is an advanced AI reasoning engine, often powered by sophisticated LLMs. When a business user defines a process, for example, “process vendor invoices,” the agent doesn’t just look for a predefined script. Instead, it leverages its understanding of natural language to comprehend the request, break it down into sub-goals, and formulate a dynamic plan to achieve the desired outcome. This forms the basis of highly effective workflow management.
This comprehensive approach allows for truly dynamic and resilient workflow management, moving beyond static, rule-based operations.
For large enterprises, particularly those in accounting, finance, and IT, the benefits of adopting an agentic process automation platform are transformative. It’s not just about marginal gains in efficiency; it’s about fundamentally reshaping how work gets done.
The power of an agentic process automation platform becomes clear through practical applications across various departments within a large enterprise. The possibilities for advanced workflow management are vast.
Finance and Accounting is ripe for agentic transformation, addressing the perennial challenge of efficient workflow management.
Agentic process automation enhances IT capabilities and fortifies automation process controls.
Even HR, often seen as a highly human-centric function, can benefit from intelligent workflow management.
These examples illustrate how agentic process automation moves beyond repetitive tasks to manage and optimize entire business functions, demanding a rethinking of traditional workflow management.
Kognitos embodies the principles of agentic process automation, offering an enterprise-grade solution that stands apart from conventional approaches. Kognitos is not RPA; it’s not low-code/no-code, nor is it a generic AI platform. Instead, Kognitos is purpose-built for natural language process automation, driven by AI reasoning.
Kognitos believes that business users should be empowered to automate complex processes without being dependent on IT or programming expertise. Its platform allows users to define and manage sophisticated workflows in plain English, translating human intent into executable actions. This eliminates the need for backend-heavy development and rigid, rule-based systems.
Kognitos’ unique strengths lie in:
This makes Kognitos an ideal agentic process automation platform for organizations seeking to achieve profound efficiencies and strategic advantage through intelligent workflow management.
Adopting an agentic process automation platform requires a strategic approach beyond simply purchasing software. It’s about a fundamental shift in how an organization approaches workflow management and business operations.
By following these principles, enterprises can unlock the full potential of agentic process automation, achieving unparalleled levels of efficiency and strategic agility.
Agentic process automation is more than a trend; it’s a foundational shift in how businesses will operate. The ability of systems to reason, adapt, and autonomously manage complex workflows in natural language marks a new era. This next generation of automation moves beyond the limitations of rigid rules and manual interventions, enabling enterprises to truly transform their operational models.
For leaders in finance, accounting, and IT, the message is clear: embracing agentic process automation is no longer an option but a strategic imperative. It’s about building resilient, intelligent operations that can navigate an unpredictable world, empowering business users, and freeing human capital for higher-value activities. The future of efficient workflow management is agentic, and the time to explore its potential is now.
