LogicMonitor Innovation & Technology Culture

How do your teams stay ahead of emerging technologies or frameworks?

We make it a core part of how we build. The Edwin AI team runs a continuous discovery loop where engineers evaluate new models, agent frameworks and tooling every week. We prototype fast, measure against real customer workflows and ship only what outperforms our current baselines on latency, accuracy, cost and safety.

Everyone contributes to our internal tech radar to track shifts in LLM capabilities, multimodal models, evaluation methods and agent orchestration patterns. Strong foundations in observability, evaluation harnesses and safety let us explore without risking product stability.
Most importantly, we learn in the open. Engineers demo experiments weekly, share wins and failures and influence the roadmap. Customer feedback from Edwin AI pilots grounds our choices so we focus on tech that solves real problems, not hype. This mix of autonomy, fast cycles and real impact keeps us on the cutting edge of AI.

Our team is involved in various industry events and meet-ups as participants and speakers. We also conduct internal hackathons to bring new, innovative ideas to life. Some team members even teach AI-related college courses and learn from other academic and industry experts.

Can you share a recent example of an innovative project or tech adoption?

Our data science team is pushing the boundaries of AI ops by applying advanced data science to real-world infrastructure challenges. This year, we developed vector embedding models for alert data that fundamentally improve how incidents are detected and understood in real time. These models employ state-of-the-art scientific methods to automatically group related alerts, strengthening the performance and accuracy of our correlation engine.

What makes this especially impactful is that the models learn directly from each customer’s environment. This generates highly tailored insights previously unattainable with static rules or generic AI, delivering earlier signal discovery, richer context and faster paths from insight to action.

This novel approach establishes the foundation for future predictive capabilities. By recognizing emerging alert patterns, the platform can anticipate what is likely to happen next and empower teams to resolve issues before they escalate into major incidents. This is a substantial leap forward in operational intelligence and incident prevention and is exemplary of our commitment to push the boundaries of AIOps and deliver transformative customer value.

How does your culture support experimentation and learning?

Our culture is built on rapid experimentation and continuous learning from real-world behavior. We run small, controlled tests often, from early root-cause reasoning prototypes to low-risk remediation attempts in sandboxed environments. Each run generates practical lessons, like how to tune context windows and better combine signals from logs, metrics and topology.

We validate ideas through structured offline evaluation, iterating on prompts, comparing before-and-after results and incorporating feedback from customers and domain experts. We invest heavily in tooling that accelerates prototyping and makes results visible to everyone. Tools like promptfoo, Lovable and Langfuse give shared clarity into what is working and why, encouraging broad participation in experimentation.

Cross-functional reviews are routine. Product, engineering and customer teams analyze real incident transcripts to improve correlation speed, reasoning clarity and explanation quality.

We also protect time for deeper technical exploration. The focus is on safe, lightweight experimentation that keeps the culture curious, fast-moving and focused on steadily increasing Edwin’s intelligence and reliability.