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JAMS Incident Management Released

JAMS Software released JAMS Incident Management, an on-call alerting and incident management software for IT operations, service desk, and engineering teams. 

The product accepts alerts from any system that can send an email, call a webhook, or reach an API. It delivers those alerts over voice, SMS, push, email, Microsoft Teams, and Slack. It then escalates through the on-call rotation until a person acknowledges and tracks the incident through to resolution. The product is generally available now, following a customer early access period that opened on August 1.

The JAMS Incident Management dashboard shows notification delivery across SMS, voice, Microsoft Teams, and Slack, alongside the two-way integrations that opened each incident. Acknowledgements and resolutions recorded in JAMS sync back to Azure Monitor, ServiceNow, Zendesk, and JAMS Scheduler.

JAMS Incident Management escalates alerts over voice, SMS, push, email, Microsoft Teams, and Slack until an on-call engineer acknowledges. The product is generally available September 1, 2026, and is free for teams of up to five users.

JAMS Incident Management natively integrates with JAMS Scheduler. Teams running JAMS Scheduler can open an incident automatically from a failed job and restart that job directly from the phone call or the text message with JAMS Incident Management. The two products are fully independent, and neither one requires the other.

"We built JAMS Incident Management for people like us," said Peter Hegland, chief executive officer of JAMS Software. "A service desk team using Zendesk that needs an on-call schedule and escalation path for human team members. A batch processing team that needs to know when a critical job fails or when it's about to miss its SLA. A DevOps or SRE team that needs to know an outage has happened and that someone is already on it. It works for us, and we hope it works for you."

JAMS Incident Management can connect to any monitoring, orchestration, or IT service management system through email, webhook, or REST API. JAMS Incident Management also has prebuilt two-way integrations for Azure Monitor, ServiceNow, Zendesk, and JAMS Scheduler with more integrations coming soon.

JAMS Incident Management is available now.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

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JAMS Incident Management Released

JAMS Software released JAMS Incident Management, an on-call alerting and incident management software for IT operations, service desk, and engineering teams. 

The product accepts alerts from any system that can send an email, call a webhook, or reach an API. It delivers those alerts over voice, SMS, push, email, Microsoft Teams, and Slack. It then escalates through the on-call rotation until a person acknowledges and tracks the incident through to resolution. The product is generally available now, following a customer early access period that opened on August 1.

The JAMS Incident Management dashboard shows notification delivery across SMS, voice, Microsoft Teams, and Slack, alongside the two-way integrations that opened each incident. Acknowledgements and resolutions recorded in JAMS sync back to Azure Monitor, ServiceNow, Zendesk, and JAMS Scheduler.

JAMS Incident Management escalates alerts over voice, SMS, push, email, Microsoft Teams, and Slack until an on-call engineer acknowledges. The product is generally available September 1, 2026, and is free for teams of up to five users.

JAMS Incident Management natively integrates with JAMS Scheduler. Teams running JAMS Scheduler can open an incident automatically from a failed job and restart that job directly from the phone call or the text message with JAMS Incident Management. The two products are fully independent, and neither one requires the other.

"We built JAMS Incident Management for people like us," said Peter Hegland, chief executive officer of JAMS Software. "A service desk team using Zendesk that needs an on-call schedule and escalation path for human team members. A batch processing team that needs to know when a critical job fails or when it's about to miss its SLA. A DevOps or SRE team that needs to know an outage has happened and that someone is already on it. It works for us, and we hope it works for you."

JAMS Incident Management can connect to any monitoring, orchestration, or IT service management system through email, webhook, or REST API. JAMS Incident Management also has prebuilt two-way integrations for Azure Monitor, ServiceNow, Zendesk, and JAMS Scheduler with more integrations coming soon.

JAMS Incident Management is available now.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...