Most internal AI projects for IT operations next exit pilot. Budgets stretch, priorities shift, key hires fall through, and what started as a strategic initiative turns into a maintenance burden—or worse, shelfware.
Not because the teams lacked vision. But because building a production-grade AI agent is an open-ended commitment. It’s not just model tuning or pipeline orchestration. It’s everything: architecture, integrations, testing frameworks, feedback loops, governance, compliance. And it never stops.
The teams that do manage to launch often find themselves locked into supporting a brittle, aging system while the market moves forward. Agent capabilities improve weekly. New techniques emerge. Vendors with dedicated AI teams ship faster, learn faster, and compound value over time.
Edwin AI, developed by LogicMonitor, reflects that compounding advantage. It’s live in production, integrated with real workflows, and delivering results for our customers. Built as part of a broader agentic AIOps strategy, it’s engineered to reduce alert noise, accelerate resolution, and handle the grunt work that slows teams down.
What follows is a breakdown of what it actually takes to build an AI agent in-house—what gets overlooked, what it costs, and what’s gained when you deploy a product that’s already proven at scale.
The Complexities and Costs of Building an AI Agent In-House
Building an AI agent sounds like control. In reality, it’s overhead. What starts as a way to customize quickly becomes a full-stack engineering program. You’re taking on a distributed system with fragile dependencies and fast-moving interfaces.
You’ll need infrastructure to run inference at scale, models that stay relevant, connectors that don’t break, testing frameworks to avoid bad decisions, and enough governance to keep it all stable in production.
None of this is one-and-done. AI systems require constant tuning. As environments shift, so does the data. AI systems degrade fast. Environments shift. Data patterns break. The agent falls out of sync.
Staffing alone breaks the model for most teams. Engineers who’ve built agentic systems at scale are rare and expensive. Hiring them is hard. Retaining them is harder. And once they’re on board, they’ll be tied up supporting internal tooling instead of moving the business forward.
LogicMonitor’s internal data shows that building your own AI agent is roughly three times more expensive than adopting an off-the-shelf product like Edwin AI. The top cost drivers are predictable: high-skill staffing, platform infrastructure, and the integrations needed to stitch the system into your existing environment.
The real cost is your team’s time and focus. Every hour spent maintaining a custom AI agent is an hour not spent improving customer experience, strengthening resilience, or driving innovation. Unless building AI agents is your core business, that effort is misallocated. Time here comes at the expense of higher-impact work.
The Advantage of Buying an Off-the-Shelf AI Agent for ITOps
Buying a mature AI agent allows teams to move faster without taking on the overhead of building and maintaining infrastructure. It removes the need to architect complex systems internally and shifts the focus to applying automation instead of construction.
The cost difference is significant. The majority of build expense comes from compounding investments: engineering headcount, platform maintenance, integration work, retraining cycles, and the ongoing support needed to keep pace with change. These are decisions that create operational weight that grows over time.
Off-the-shelf agents are designed to avoid that drag. They’re built by teams focused entirely on performance, tested in diverse environments, and updated continuously based on feedback at scale. That means less risk, shorter time to impact, and lower total cost of ownership.
The Benefits of Agentic AIOps
The power of agentic AIOps lies in the value it delivers across your organization. Products like Edwin AI aren’t just automating workflows—they’re transforming the way IT teams operate, enabling faster resolution, less noise, and a more resilient digital experience.
At the core of agentic AIOps are four high-impact value drivers:
- Improved Operational Efficiency
- Improved Employee Productivity
- Improved Customer Experience
- Reduced License and Training Costs
To go deeper, the reduction in alert and event noise directly translates to avoided IT support costs. With Edwin AI, organizations have reported up to 80% reduction in alert noise, cutting down the number of incidents that reach human teams and freeing up capacity for more strategic work.
Generative AI capabilities—like AI-generated summaries and root cause suggestions—not only improve Mean Time To Resolution (MTTR), they also minimize time spent in war rooms. The result? A 60% drop in MTTR, faster incident triage, and fewer late-night escalations.
By catching issues before they escalate, organizations also reduce the frequency and duration of outages—translating to avoided outage costs and improved service reliability. Fewer incidents means fewer people involved, fewer systems impacted, and happier end users.
Then there’s license and training optimization. By consolidating capabilities within a single AI observability product, companies are seeing reduced licensing overhead and fewer hours spent training teams across disparate tools.
See the data behind the shift to AIOps. See the infographic.
What Edwin AI Delivers Off-the-Shelf
Edwin AI, developed by LogicMonitor, is one example of what an agentic product looks like in production. It’s deployed across enterprise environments today, already delivering outcomes that internal teams often struggle to reach with homegrown tools.
Edwin delivers:
- Event Intelligence: Features like alert noise reduction, ITSM integration, third-party data ingestion, and support for open, configurable AI models. These are built to surface relevant incidents faster and reduce alert fatigue without sacrificing context.
- AI Agent: Tools like AI-generated titles and summaries, root cause analysis, and Actionable Recommendations. Upcoming additions include predictive insights and agent-driven automation—designed to reduce manual triage and improve decision quality.
Buying an agentic product like Edwin AI removes the engineering burden, and it gives teams a system that scales with them, adapts to their stack, and starts delivering value on day one. No internal build cycles. No integration firefights. Just function.
Take the Edwin AI product tour.
Don’t Just Take our Word for It: Customers are Sharing their Success with Edwin AI in Production
The benefits of Edwin AI are playing out in real production environments across the globe. Companies in diverse industries are turning to Edwin AI to solve for a number of use cases, including simplifying operations, eliminating noise, and accelerating time to resolution. The results speak for themselves.
- Syngenta saw value within just one hour of implementation, a testament to Edwin AI’s low time-to-value and out-of-the-box intelligence.
- Chemist Warehouse achieved an 88% reduction in alert noise, allowing their teams to focus on real issues instead of chasing false alarms.
- Nexon reduced incidents by 67%, streamlining IT workflows and dramatically improving service reliability.
- Devoteam reported a 58% improvement in operational efficiency, thanks to Edwin AI’s ability to correlate signals across complex environments.
- MARKEL achieved 68% deduplication, cutting through redundant alerts and surfacing what truly matters.
These are consistent indicators of how agentic AIOps, delivered through Edwin AI, transforms IT operations.
So Should You Build or Buy an AI Agent for ITOps?
Building an AI agent for ITOps is a resource-intensive initiative. It requires sustained investment across architecture, infrastructure, staffing, and maintenance—often without a clear timeline to value. Teams that take this path often find themselves maintaining internal systems instead of solving operational problems.
Edwin AI takes that complexity off the table. It’s already in production, already integrated, and already delivering results. Internal analysis shows it’s roughly three times more cost-effective than building from scratch, with real-world returns on investment that include 80% alert noise reduction and a 60% drop in mean time to resolution.
These gains are happening now, in live environments, under real pressure.
For organizations focused on reliability, efficiency, and speed, products like Edwin AI remove friction and deliver impact without adding overhead.
Few teams have the time or capacity to support a product this complex. Most don’t need to. So when budgets are tight and expectations are high, shipping value quickly matters more than owning every component.
Edwin AI solves ITOps biggest challenges with agentic AI.
Get a demo
Margo Poda leads content strategy for Edwin AI at LogicMonitor. With a background in both enterprise tech and AI startups, she focuses on making complex topics clear, relevant, and worth reading—especially in a space where too much content sounds the same. She’s not here to hype AI; she’s here to help people understand what it can actually do.
Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.
