Engineering Resilience & Automation in your observability stack
Modern observability stacks can generate thousands of alerts, but visibility without action doesn’t improve uptime. Engineering resilience means detecting issues early, prioritising what matters, automating response, and proactively testing failure scenarios before they become incidents.

Critical Response & Automation
Teams are often overwhelmed by high volumes of alerts, leaving them to manually investigate issues and coordinate fixes. The result is slow incident response, longer outages, and inconsistent remediation across teams.
The solution is critical response and automation that quickly pinpoints what’s failing, prioritises what matters, and automates repeatable actions so teams can resolve incidents faster, reduce noise, and restore services with confidence.
Observability
As systems grow more distributed and complex, teams struggle with limited visibility into how services behave and interact. This lack of insight makes it difficult to detect issues early or understand root cause when something breaks.
The solution is observability providing real time visibility across metrics, logs, and traces, helping teams quickly understand what’s happening in their systems, identify issues before users are impacted, and keep services healthy and reliable at scale.
Chaos Engineering
Modern systems are complex and failure is inevitable, yet many teams don’t know how their applications will behave under real world stress. This uncertainty creates risk when outages or unexpected conditions occur.
The solution, chaos engineering addresses this by intentionally introducing controlled failures to test system resilience, validate assumptions, and expose weaknesses before they impact customers helping teams build confidence that their systems can withstand disruption.
Critical Response & Automation
Problems
Lot’s of alerts, slow resolutions, manual responses.
Solution
Understand what’s wrong going on in your system fast and fix quickly.
Observability
Problems
No insights into complex systems.
Solution
Understand what’s going on in your system, detect issues, and keep everything healthy.
Chaos Engineering
Problems
Resilience uncertainty.
Solution
Breaks things on purpose to test resilience.
Together...
LogicMonitor LM Envision unifies hybrid observability with agentic AIOps to reduce noise, speed resolution, and help prevent downtime across cloud and on‑prem environments.
PagerDuty turns those signals into coordinated incident response with on call scheduling and incident management, and adds AIOps and automation to remove manual, repetitive work running diagnostic or remediation actions and triggering runbook automation when seconds matter.
Gremlin completes the loop with controlled chaos experiments and reliability testing, helping teams find and fix availability risks before users are impacted.
Together, LogicMonitor, PagerDuty and Gremlin create a repeatable resilience operating model:
Observe > Prioritise > Respond > Automate > Learn.
Customers can standardise runbooks, significantly shorten MTTR, and continuously harden critical services while improving customer experience.
Industry Awards
LogicMonitor Recognized as Leader in AI and Cloud in SiliconANGLE’s 2025 TechForward Awards

LogicMonitor Recognized by G2 - for its comprehensive monitoring capabilities and ease of use.

PagerDuty Recignised by G2. G2 is proud to share our 2025 list of the Best IT Management Software Products

PagerDuty Recognised as CRN's - The 20 Hottest AI Cloud Companies: The 2025 CRN AI 100

DORA & NIS2
Not only is downtime expensive, in large organisations compliance initiatives like NIS2 & DORA also need to be enforced & we’re seeing this across nearly all industries that have various levels of digital first transformations.
Financial Services/FinTech
Insurance
Healthcare
Public Sector
Telecommunications
Energy
Retail/Ecommerce
Technology/SaaS/ISVs
Transportation/Logistics
Roles
Who cares about Engineering Resilience & Automation in the observability stack?
Platform Engineering Manager
Kubernetes Platform Owner
SRE Lead
Reliability Engineering Manager
DevOps Lead
Platform Engineering Manager
IT Operations (ITOps) Manager
NOC Lead
Major Incident Manager
Service Delivery Manager
CTO/VP Engineering
Head of Infrastructure

Key Discovery Questions
Answering these questions helps uncover risks and align your strategy with best practices in Engineering Resilience & Automation in the observability stack.
|
1 |
What does your current observability stack look like today (monitoring, logs, alerts), and what’s missing for your most critical services? |
|
2 |
How much of your alert volume is actionable vs noise, and how do you currently deduplicate or prioritize incidents? |
|
3 |
What is your incident process end to end detection > triage > escalation > resolution > post incident review and where does it break down? |
|
4 |
How do you execute remediation today: manual runbooks, scripts, or automated workflows and how quickly can you take safe action during an incident? |
|
5 |
Do you proactively test resilience (e.g., game days/chaos engineering) to validate how systems behave under failure before the next release? |
Key Discovery Questions
Answering these questions helps uncover risks and align your strategy with best practices in Engineering Resilience & Automation in the observability stack.
|
1 |
What does your current observability stack look like today (monitoring, logs, alerts), and what’s missing for your most critical services? |
|
2 |
How much of your alert volume is actionable vs noise, and how do you currently deduplicate or prioritize incidents? |
|
3 |
What is your incident process end to end detection > triage > escalation > resolution > post incident review and where does it break down? |
|
4 |
How do you execute remediation today: manual runbooks, scripts, or automated workflows and how quickly can you take safe action during an incident? |
|
5 |
Do you proactively test resilience (e.g., game days/chaos engineering) to validate how systems behave under failure before the next release? |

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