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ManageEngine Rolls Out Autonomous AI Capabilities Across Its Suite to Power Digital Enterprises

The Company's Commitment to Data Privacy and Sovereignty Helps Customers Adopt AI Agents With Confidence

ManageEngine, a division of Zoho Corporation, announced the rollout of Zia Agents, the company's proprietary AI-powered autonomous agent, across its digital enterprise management suite. 

Built within a secure and privacy-compliant framework, these agents can orchestrate and execute tasks without the need for intervention. This marks a milestone in the company's vision of enabling truly autonomous IT environments.

"The frontier models are great for all-purpose use but are not often efficient for specific areas like enterprise IT. We take great care in building AI technology that is not only purpose-built, but also provides value in terms of cost and long-term use. We are excited to bring autonomous AI capabilities to our offerings and provide a reliable platform for our customers to achieve efficient outcomes," said Rajesh Ganesan, CEO, ManageEngine.

Key Highlights of ManageEngine's Autonomous AI Capabilities:

  • Prebuilt agents deployed in a single click, while Zia Agent Studio lets users build custom agents from scratch or configure them through NLP. Agents are fully customizable; users control configuration, tools, and the knowledge base.
  • For complex workflows, multi-agent orchestration lets a master agent coordinate specialized subagents, routing the right work to the correct agent seamlessly.
  • Customer data is never used to train any AI model. Administrators can define guardrails for agent behavior, and built-in observability provides a complete audit of agent actions.
  • ManageEngine tools support standard MCP, which allows customers to make them work with third-party LLMs and agentic platforms.

Redefining Enterprise IT Management With Zia Agents

With the launch of Zia Agents, the focus shifts from AI-enabled assistance to autonomous execution across IT service management, full-stack observability, endpoint management, and security operations. These agents are built on the same Zia agentic platform shared across the ManageEngine suite, making it easy to enable native cross-product intelligence without custom integration overhead.

  • Enabling IT and business service workflows: In the realm of service management, teams can build AI agents for use cases spanning IT and business processes, ranging from a resolution assistant or an HR assistant to a CI health and impact analyzer, all in just a few steps. These agents, which can be orchestrated by a master agent, connect to multiple IT and business applications; grounded with contextual knowledge, the agents operate within set guardrails to autonomously execute and take ownership of tasks end to end. Prebuilt agents like the L1 service desk specialist, PIR generator, and KB article generator deploy in minutes.
  • Driving self-diagnosing IT operations: The agents also add an action layer on top of the visibility layer, thereby moving enterprises away from traditional observability and significantly reducing remediation times. The agents can help in troubleshooting incidents by finding the root cause behind them to paving the way for automating the recovery process. Meanwhile, agents in the company's cloud cost management solution help to investigate unexpected cost surge and also compute the combined costs incurred across different cloud accounts.
  • Automating tasks to accelerate security operations: Zia Agents automate security tasks such as user reviews, alert correlation, and multi-step investigations, bringing hours of manual work down to minutes. Organizations can build custom agents rooted in their organizational knowledge, processes, and risk priorities to reduce false positives. By connecting across the IT suite, agents can pull cross-domain context to correlate anomalies, evaluate vulnerabilities, and map device risk in one comprehensive assessment for a faster, more confident response. Teams can talk to their log and alert data in plain English, calling on prebuilt or custom agents to do the work for them.
  • Keeping endpoints compliant and up to date: Some of the prebuilt agents take care of EDR event triage, device diagnosis, patch troubleshooting, and compliance. While the EDR Event Triage Agent correlates telemetry, maps attack chains to MITRE ATT&CK, and recommends prioritized next steps before analyst intervention, the Device Investigation Agent delivers complete root cause diagnosis the moment a ticket lands, with no manual querying required. Teams can also leverage Zia Agents to run deep-dive analysis on any failed endpoint, with full-fix troubleshooting steps and device context delivered on demand, along with an analysis on deployment gaps and a sequenced roadmap to 100% compliance. Also, there is an option to build custom agents for other endpoint-specific workflows.

Addressing the Data Privacy and Governance Concerns

While AI agents are already a proven technology delivering great business value, concerns around data privacy are holding back enterprise adoption. However, the introduction of Zia Agents in ManageEngine's suite, backed by the company controlling the entire stack, ensures proper governance.

"The privacy principles adopted by ManageEngine for the last two decades in building our stack now stands vindicated even more in the age of AI agents. Our commitment to upholding the principles of data privacy and sovereignty gives assurance to our customers to adopt AI agents with confidence," said Umasankar Narayanasamy, vice president, ManageEngine. 

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ManageEngine Rolls Out Autonomous AI Capabilities Across Its Suite to Power Digital Enterprises

The Company's Commitment to Data Privacy and Sovereignty Helps Customers Adopt AI Agents With Confidence

ManageEngine, a division of Zoho Corporation, announced the rollout of Zia Agents, the company's proprietary AI-powered autonomous agent, across its digital enterprise management suite. 

Built within a secure and privacy-compliant framework, these agents can orchestrate and execute tasks without the need for intervention. This marks a milestone in the company's vision of enabling truly autonomous IT environments.

"The frontier models are great for all-purpose use but are not often efficient for specific areas like enterprise IT. We take great care in building AI technology that is not only purpose-built, but also provides value in terms of cost and long-term use. We are excited to bring autonomous AI capabilities to our offerings and provide a reliable platform for our customers to achieve efficient outcomes," said Rajesh Ganesan, CEO, ManageEngine.

Key Highlights of ManageEngine's Autonomous AI Capabilities:

  • Prebuilt agents deployed in a single click, while Zia Agent Studio lets users build custom agents from scratch or configure them through NLP. Agents are fully customizable; users control configuration, tools, and the knowledge base.
  • For complex workflows, multi-agent orchestration lets a master agent coordinate specialized subagents, routing the right work to the correct agent seamlessly.
  • Customer data is never used to train any AI model. Administrators can define guardrails for agent behavior, and built-in observability provides a complete audit of agent actions.
  • ManageEngine tools support standard MCP, which allows customers to make them work with third-party LLMs and agentic platforms.

Redefining Enterprise IT Management With Zia Agents

With the launch of Zia Agents, the focus shifts from AI-enabled assistance to autonomous execution across IT service management, full-stack observability, endpoint management, and security operations. These agents are built on the same Zia agentic platform shared across the ManageEngine suite, making it easy to enable native cross-product intelligence without custom integration overhead.

  • Enabling IT and business service workflows: In the realm of service management, teams can build AI agents for use cases spanning IT and business processes, ranging from a resolution assistant or an HR assistant to a CI health and impact analyzer, all in just a few steps. These agents, which can be orchestrated by a master agent, connect to multiple IT and business applications; grounded with contextual knowledge, the agents operate within set guardrails to autonomously execute and take ownership of tasks end to end. Prebuilt agents like the L1 service desk specialist, PIR generator, and KB article generator deploy in minutes.
  • Driving self-diagnosing IT operations: The agents also add an action layer on top of the visibility layer, thereby moving enterprises away from traditional observability and significantly reducing remediation times. The agents can help in troubleshooting incidents by finding the root cause behind them to paving the way for automating the recovery process. Meanwhile, agents in the company's cloud cost management solution help to investigate unexpected cost surge and also compute the combined costs incurred across different cloud accounts.
  • Automating tasks to accelerate security operations: Zia Agents automate security tasks such as user reviews, alert correlation, and multi-step investigations, bringing hours of manual work down to minutes. Organizations can build custom agents rooted in their organizational knowledge, processes, and risk priorities to reduce false positives. By connecting across the IT suite, agents can pull cross-domain context to correlate anomalies, evaluate vulnerabilities, and map device risk in one comprehensive assessment for a faster, more confident response. Teams can talk to their log and alert data in plain English, calling on prebuilt or custom agents to do the work for them.
  • Keeping endpoints compliant and up to date: Some of the prebuilt agents take care of EDR event triage, device diagnosis, patch troubleshooting, and compliance. While the EDR Event Triage Agent correlates telemetry, maps attack chains to MITRE ATT&CK, and recommends prioritized next steps before analyst intervention, the Device Investigation Agent delivers complete root cause diagnosis the moment a ticket lands, with no manual querying required. Teams can also leverage Zia Agents to run deep-dive analysis on any failed endpoint, with full-fix troubleshooting steps and device context delivered on demand, along with an analysis on deployment gaps and a sequenced roadmap to 100% compliance. Also, there is an option to build custom agents for other endpoint-specific workflows.

Addressing the Data Privacy and Governance Concerns

While AI agents are already a proven technology delivering great business value, concerns around data privacy are holding back enterprise adoption. However, the introduction of Zia Agents in ManageEngine's suite, backed by the company controlling the entire stack, ensures proper governance.

"The privacy principles adopted by ManageEngine for the last two decades in building our stack now stands vindicated even more in the age of AI agents. Our commitment to upholding the principles of data privacy and sovereignty gives assurance to our customers to adopt AI agents with confidence," said Umasankar Narayanasamy, vice president, ManageEngine. 

The Latest

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...