Risotto’s new Autonomous IT Engineer investigates complex support requests, executes approved fixes, and converts successful resolutions into reusable automated workflows that could reduce repeated engineering work.
WHAT’S HAPPENING
Artificial intelligence is moving beyond answering routine IT support questions toward investigating complicated technical problems and creating automated solutions that can be used again.
AI-native helpdesk company Risotto has introduced its Autonomous IT Engineer, a system designed to address complex Tier-2 technical support requests that traditionally require experienced IT personnel.
For years, businesses have used automation to handle relatively simple technology requests.
Password resets, basic software access, account provisioning, and routine troubleshooting can often be completed through established procedures.
But when an employee encounters an unfamiliar technical problem, the process becomes more complicated.
An IT engineer may need to examine system logs, review account permissions, investigate software configurations, identify the source of a failure, and determine an appropriate solution.
That investigation can require substantial time and expertise.
Risotto’s new system attempts to automate portions of this work using AI agents capable of examining information from connected business systems.
The Autonomous IT Engineer is designed to investigate technical problems, recommend corrective actions, and execute approved solutions.
But the company’s most important development involves what happens after the problem is resolved.
Risotto converts successful resolutions into reusable automated workflows, allowing future occurrences of similar problems to be addressed without repeating the entire investigation.
These workflows, commonly called runbooks, contain documented procedures for handling specific technical situations.
Instead of preserving a solution only within a closed support ticket, the system can turn it into an operational process that may be reused.
Risotto has also introduced a capability called Past Ticket Mining.
This feature examines approximately 90 days of historical support tickets to identify recurring problems and opportunities for additional automation.
The company says users can describe desired workflows in ordinary language, allowing its platform to generate and test automation connected to existing business applications through APIs or Model Context Protocol (MCP) endpoints.
The platform also supports proactive operations, including scheduled tasks, monitoring, and employee onboarding workflows.
Risotto identifies companies including Gusto, Instacart, Docker, Retool, ThoughtSpot, Patreon, and Ironclad among its customers.
The company reports that its existing platform has achieved substantial automation of certain support requests.
For example, Risotto cites a 91% automation rate for Ironclad access requests and reports rapid initial responses across customer deployments.
However, these figures involve different customers, support categories, and implementation conditions.
They should not be interpreted as evidence that the newly introduced Autonomous IT Engineer already resolves 90% of unfamiliar Tier-2 engineering problems.
The central development is not simply faster IT support. It is the ability to transform previously solved technical problems into reusable automation that can reduce repeated engineering work.
WHY IT MATTERS
Businesses depend on increasingly complicated technology environments.
Employees use cloud applications, internal systems, collaboration platforms, security tools, customer databases, and specialized software.
When these systems experience problems, productivity can suffer.
An employee unable to access an important application may be unable to complete assigned work.
A configuration error can interrupt an entire department.
A recurring software problem can consume hours of technical support even when the same issue has already been resolved.
Traditional IT support systems rely heavily on service tickets to organize these requests.
A ticket documents the problem, assigns responsibility, and records the eventual resolution.
But that process does not automatically preserve the solution in a form that can be executed the next time the issue appears.
Businesses may repeatedly pay employees to solve problems their organizations have already solved.
That creates an opportunity for AI-driven operational automation.
Risotto’s approach attempts to capture knowledge generated during technical investigations and convert it into reusable procedures.
Consider an employee who repeatedly loses access to a business application because of a particular configuration problem.
An IT engineer might investigate the issue, identify the incorrect setting, restore access, and close the ticket.
If the same issue affects another employee, the investigation may need to be repeated.
An AI system capable of recognizing the recurring pattern and applying an approved workflow could potentially resolve the next incident more efficiently.
The economic benefit would come from reducing repetitive investigation, shortening interruptions, and allowing experienced engineers to concentrate on unfamiliar or higher-priority problems.
That distinction is important.
Many businesses already maintain knowledge bases containing instructions for resolving common problems.
Risotto is attempting to move beyond storing written instructions toward creating procedures that software can execute.
This introduces a more advanced form of automation.
Instead of simply following workflows created by human engineers, AI agents may help develop new workflows from successfully completed work.
The approach could also help preserve institutional knowledge.
In many organizations, experienced employees understand technical systems through years of troubleshooting.
When those employees leave, some of their knowledge disappears with them.
Support tickets may contain portions of that experience, but the information is often difficult to locate or reuse.
Converting proven solutions into documented automation could make organizational expertise more accessible.
However, technical complexity introduces risks.
Not every problem should be resolved automatically.
Changes to account permissions, network configurations, security settings, or business-critical systems can have unintended consequences.
An AI agent that incorrectly identifies a problem could make the situation worse by applying an inappropriate solution.
For that reason, human approval and clearly defined execution permissions remain important.
Businesses must also ensure that automated runbooks are valid for the systems and circumstances in which they operate.
A solution that works in one environment may not work safely in another.
The business opportunity depends on combining automated investigation with reliable controls, accurate system information, and procedures for handling unexpected outcomes.
The broader significance extends beyond corporate IT departments.
The same principle could apply wherever organizations repeatedly solve similar operational problems.
Customer service, employee onboarding, account administration, software provisioning, and business process management all involve procedures that could potentially be captured and reused.
If AI agents become better at converting successful work into repeatable operations, the economics of business automation could change substantially.
WHO BENEFITS
– Corporate IT departments: Could reduce repetitive troubleshooting and allow engineers to concentrate on complex technical problems that genuinely require specialized expertise.
– Employees experiencing technology problems: May receive faster assistance and spend less time waiting for access or technical issues to be resolved.
– Businesses with large software environments: Could benefit from more consistent support procedures across applications, departments, and locations.
– IT service providers: May gain opportunities to deliver managed support services using AI-assisted investigation and reusable automation.
– Risotto: Gains an opportunity to differentiate its platform through automated investigation and workflow creation rather than routine question answering alone.
– AI automation developers: Could benefit from growing interest in systems that generate and maintain operational procedures.
– Companies with experienced technical employees: May be able to preserve more institutional expertise by converting successful investigations into reusable runbooks.
– Small and midsize businesses: Could eventually gain access to technical support capabilities that previously required larger internal IT teams.
– Business software providers: May benefit from increased demand for integrations that allow authorized AI agents to perform approved actions across connected systems.
WHO LOSES
– Traditional helpdesk providers limited to basic automation: Could face competitive pressure from platforms capable of investigating more complicated technical issues.
– Organizations relying exclusively on manual troubleshooting: May experience higher support costs and slower response times than competitors using effective automation.
– IT service providers charging primarily for repetitive support activities: Could face pricing pressure if customers increasingly expect common problems to be resolved automatically.
– Businesses with poorly documented systems: May struggle to benefit from AI automation if technical information is incomplete, outdated, or inconsistent.
– Organizations deploying AI without appropriate controls: Risk creating additional problems when automated systems perform incorrect or unauthorized technical actions.
– Employees whose roles focus on routine support work: May experience changes in responsibilities as companies automate more repetitive troubleshooting tasks.
– Companies purchasing unproven automation systems: Could incur implementation and maintenance expenses without achieving the productivity gains they expect.
– Software vendors with limited integration capabilities: May face competitive pressure from platforms that allow authorized AI systems to interact more easily with business applications.
WHAT HAPPENS NEXT
Risotto’s next important test will be demonstrating how reliably its Autonomous IT Engineer can resolve complex technical problems in real business environments.
The company has reported significant automation results from its existing platform, but the more advanced ability to investigate unfamiliar Tier-2 problems requires separate evaluation.
Several factors will determine whether this approach becomes an established part of business IT operations.
Complex problem resolution
Businesses will need evidence showing how frequently the system can successfully investigate problems that do not already have documented solutions.
Resolving routine account requests is different from diagnosing unfamiliar software failures.
Quality of generated runbooks
Automatically created procedures must be accurate, repeatable, and appropriate for the systems they are designed to manage.
Companies will need ways to evaluate runbooks before allowing them to operate more independently.
Human approval and oversight
Businesses must establish which actions an AI agent can recommend, which it can execute after approval, and which require direct intervention by qualified personnel.
Integration with existing software
The platform’s usefulness will depend on how effectively it connects with enterprise applications, identity systems, monitoring tools, and other technology infrastructure.
APIs and MCP connections may support these integrations, but the reliability and permissions of each connection remain important.
Knowledge preservation
Organizations will want to measure whether automatically generated workflows reduce repeated investigations and improve consistency across support teams.
Financial performance
Businesses should compare the complete cost of AI automation with the operating expenses it reduces.
Relevant measures include engineer time, system downtime, support costs, software subscriptions, implementation expenses, and maintenance.
Security and reliability
An automated system capable of changing account permissions or software configurations requires appropriate access controls, testing, monitoring, and recovery procedures.
The more responsibility an AI agent receives, the more important these safeguards become.
Expansion beyond IT support
The underlying technology could eventually support other business functions where recurring problems require investigation and procedural responses.
Employee onboarding is one example.
An organization may repeatedly perform the same sequence of account creation, software provisioning, permission assignment, and employee notifications.
AI-assisted workflow creation could potentially help standardize those activities.
Customer service presents another possibility.
When a representative solves an unfamiliar customer problem, the successful process might be converted into an approved procedure for future cases.
However, each application would require appropriate testing and operational controls.
The more important long-term development may involve how organizations manage accumulated expertise.
Historically, companies have attempted to preserve knowledge through manuals, standard operating procedures, training programs, and internal documentation.
AI agents could make portions of that knowledge easier to organize and apply.
A technical problem would no longer necessarily produce only a completed support ticket.
It could also produce an improved process.
Over time, a business might develop an expanding library of automation based on recurring operational experience.
That creates a different relationship between employees and business software.
Human specialists would continue investigating difficult issues, but their successful solutions could become reusable capabilities that benefit the entire organization.
For AI technology providers, this creates opportunities to develop systems that do more than answer questions or execute predefined commands.
The next competitive advantage may involve helping organizations convert operational knowledge into reliable automated actions.
The commercial winners will be those that can demonstrate measurable improvements without compromising security or reliability.
The larger shift is clear: Artificial intelligence is moving from answering workplace questions toward capturing and automating the processes used to solve them. Risotto’s Autonomous IT Engineer introduces an approach in which complicated IT investigations can produce reusable runbooks rather than isolated ticket resolutions. If the technology proves reliable at scale, businesses could reduce repeated technical work, preserve institutional expertise, and gradually build automation from their own operating experience. The critical test will be whether these systems can consistently turn successful problem-solving into safe, measurable improvements across real business environments.
