Key Takeaways
Self-healing automation is the focus of this post, which introduces Kognitos’s hyperautomation lifecycle (HAL), auto-write, auto-test, auto-deploy, auto-monitor, and auto-debug, to manage the full lifecycle of automation agents and drastically cut the maintenance that has historically limited automation. It frames debugging as the most time-consuming and expensive stage of the software development lifecycle, citing a 2017 Cambridge study estimating over $312B spent annually finding and fixing bugs, and notes developers spend an estimated 30–50% of their time debugging. Kognitos auto-debug addresses this by analyzing intermediate values captured during monitoring to pinpoint why a run deviated, proposing contextual fixes in natural language, applying them to the current or a parallel workflow, and verifying changes through automated testing before deployment. The takeaway is that automations which debug themselves let CIOs scale initiatives while freeing developers from manual troubleshooting on the Kognitos platform.
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?
Intelligent Problem-Solving
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:
- Analyze intermediate values recorded during auto-monitoring to pinpoint why a specific run deviated from expectations
- Propose contextual fixes using natural language, such as adjusting data validation rules or modifying workflow logic
- Apply solutions either to the current automation (via auto-write) or create new parallel workflows
- Verify changes by auto-testing various scenarios and edge cases before deploying updates
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.
Ready to Experience Self-Healing Automations?
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.
How to Deploy Self-Healing Automation with Kognitos Auto-Debug
- Understand the failure modes that self-healing automation addresses. Traditional RPA requires developer intervention when automation fails. Self-healing automation detects failure, diagnoses the root cause, and resolves it autonomously. The primary failure modes self-healing addresses are: changed UI elements, unexpected data formats, and temporary system unavailability.
- Configure Kognitos Auto-Debug on high-maintenance automation workflows. Deploy Auto-Debug on the automations with the highest current break-fix maintenance burden. Auto-Debug is most valuable where the maintenance cost is highest. Configure it on the 5 highest-maintenance automations first to maximize the immediate maintenance reduction.
- Set up monitoring for self-healing events and their outcomes. Every self-healing event should be logged: what failed, what diagnosis was applied, whether the resolution was successful. Monitor self-healing logs to identify patterns: recurring failure types that indicate a deeper configuration issue.
- Define the escalation path for failures the automation cannot self-heal. Self-healing automation has limits: some failures require human decision or developer attention. Define the escalation path for failures Auto-Debug cannot resolve. Clear escalation prevents self-healing systems from silently failing on complex failure modes.
- Measure maintenance hours reduction before and after Auto-Debug deployment. Track developer and operations team hours spent on automation maintenance before and after Auto-Debug deployment. Maintenance reduction is the primary self-healing automation metric. Publish the reduction to demonstrate ROI.
