Agentic AI in Action: How OpenAI, Tribe AI and LogicMonitor See Enterprises Preparing for Autonomous IT
A grounded look at how LogicMonitor, OpenAI, and Tribe AI are bringing agentic AI to life—showing what real enterprise adoption, measurable ROI, and human-centered automation look like today.
Agentic AI is moving from concept to production. LogicMonitor’s Edwin AI proves it.
Autonomous agents are now enterprise-ready. With reasoning and guardrails, they execute complex, repeatable tasks across IT operations safely and at scale.
AI success isn’t plug-and-play. Leaders at OpenAI and Tribe AI emphasized that measurable ROI demands executive sponsorship, the right use case, and continuous iteration.
The fastest movers launch one workflow, measure outcomes, and expand based on real value, not theory.
Proof is in production. Edwin AI delivered up to 88% alert noise reduction and 70% fewer ServiceNow tickets across LogicMonitor customers within weeks.
Recommendation: Focus your next AI initiative on one high-impact workflow. Measure, iterate, and scale.
Agentic AI has quickly become the next frontier of enterprise automation. Instead of static AI tools that wait for human prompts, agents act on behalf of users by autonomously reasoning, sequencing steps, and taking action within defined guardrails. In our recent webinar, leaders from OpenAI, Tribe AI, and LogicMonitor explored what this shift means for the enterprise and what it takes to move from experimentation to real outcomes.
The discussion featured Kaitlin MacRae, Director of Partnerships at OpenAI; Jaclyn Rice Nelson, Co-Founder and CEO of Tribe AI; and Karthik SJ, General Manager of AI at LogicMonitor. Together, they broke down how reasoning-enabled models are creating a new category of automation—one capable of handling complex, repeatable workflows across IT, customer operations, and product environments.
Over the hour, the panel shared lessons from the field: what defines an agent, why enterprises struggle to realize value, and how companies like LogicMonitor are already putting agentic systems into production through Edwin AI, the company’s AI agent for ITOps. The conversation moved beyond hype and into practice—showing how OpenAI’s model breakthroughs, Tribe AI’s deployment expertise, and LogicMonitor’s applied engineering are converging to make autonomous IT a reality.
See leaders at OpenAI, Tribe AI, and LogicMonitor explore how agentic AI is transforming enterprise operations.
For years, enterprise AI has been limited to static automation in the form of scripts, workflows, or copilots that respond within a narrow context. The new wave of agentic AI changes that dynamic. Instead of reacting, agents initiate. They understand objectives, reason through steps, and act within clear constraints to achieve outcomes on behalf of users.
Kaitlin MacRae, Director of Partnerships at OpenAI, explained that this shift was made possible by a fundamental breakthrough: reasoning.
“Through a series of unlocks, we were able to give our frontier models the ability to reason. That opened the door to building self-driving agents—systems that don’t just respond but take initiative. Once a model can reason, it can plan and act, and that unlocks task automation at scale. For enterprises, that’s the game changer: automating repeatable, structured work that employees do every day.”
Kaitlin MacRae Director of Partnerships, OpenAI
MacRae noted that reasoning is what makes autonomy composable. A single agent can execute a simple action—like booking an appointment or validating an expense report—but when multiple agents work together, they begin to mirror the structure of the enterprise itself. One agent can triage, another can route, and others can execute tasks across systems and teams. Reasoning provides the judgment layer that determines what to do next, while guardrails and context keep every action aligned with policy and intent.
In practice, this is how enterprises move from task-level automation to coordinated autonomy. Single agents streamline routine work; networks of agents handle multi-step processes end-to-end. These connected systems enable autonomous triage, escalation, and resolution—all while continuously feeding real-world feedback back to the model teams. That closed loop is shortening iteration cycles from months to weeks and pushing enterprise AI toward true operational autonomy.
Why Enterprise Adoption Succeeds—or Stalls
Enterprise AI projects rarely fail because of weak technology. They fail because the surrounding structure—leadership, priorities, and measurement—doesn’t support the transition from concept to production. Tribe AI, which has built hundreds of large-scale systems, has seen this pattern play out repeatedly. Co-founder and CEO Jaclyn Rice Nelson noted that while the value is real, reaching it requires discipline.
Before unveiling Tribe’s framework for success, she was blunt about the challenge, saying “The answer is—it’s not easy to get to that value.”That reality has shaped how Tribe approaches every deployment. Their data points to five consistent levers that determine whether an AI initiative drives ROI or stalls at pilot stage:
Executive sponsorship that commits real budget and visibility—not just lab resources.
Problem selection is tied directly to metrics that matter to the business.
Measurement built in from the first sprint to track progress and impact.
Context plumbing that gives agents access to relevant data and systems.
Enablement and change management to ensure teams adopt and extend what’s built.
OpenAI’s MacRae added a perspective that grounds those operational lessons in purpose. Too many organizations, she said, still misunderstand what agentic AI is designed to do.
“[Organizations] think that agentic AI is about replacing people, when in reality its strength lies in augmenting teams by automating workflows—not displacing human judgment.”
Kaitlin MacRae Director of Partnerships, OpenAI
That distinction matters. The companies succeeding with agentic systems treat agents as extensions of their teams, not as replacements. They start with one defined workflow, measure outcomes, and use those learnings to expand incrementally. Early missteps aren’t wasted effort—they’re data. Each iteration tightens feedback loops and sharpens alignment between human decision-making and autonomous execution.
In short: success depends less on how advanced the model is and more on how thoughtfully it’s integrated into the organization around it.
High-Value Use Cases Seen in the Wild
Both Tribe AI and OpenAI leaders emphasized that agentic systems are already embedded in daily workflows across engineering, IT operations, and customer-facing functions. The most mature and repeatable patterns share one defining trait: they automate complex, structured work while maintaining awareness of context, history, and intent.
Coding agents are the clearest example. They generate, migrate, test, and review code—freeing engineers from repetitive development tasks while improving both output volume and quality. These agents act as intelligent assistants that learn team conventions and continuously refine codebases without expanding headcount.
In workflow orchestration, agents coordinate multi-step business processes such as onboarding, billing, or approval chains. By handling dependencies between systems, they reduce handoffs and latency, making the process smoother for both employees and customers.
Training and coaching agents are emerging as force multipliers for enablement teams. These role-specific copilots simulate the best performers in a given function, offering targeted guidance drawn from company data and performance analytics.
And in observability and IT operations, where LogicMonitor focuses, agentic products like Edwin AI detect, diagnose, triage, and remediate issues in real time. They reduce alert fatigue, cut mean time to resolution, and ensure that the right actions happen automatically—without endless paging or war rooms.
Across all these use cases, agents maintain state: they remember context, artifacts, and prior steps. That continuity means users don’t have to re-explain intent, and systems can act consistently from one interaction to the next. It’s the foundation that makes agentic automation reliable at enterprise scale.
Case Study: LogicMonitor’s Edwin AI (with OpenAI + Tribe)
LogicMonitor sits at the center of hybrid observability, monitoring both on-premises and cloud environments for some of the world’s largest enterprises. That position gives it access to enormous volumes of telemetry—metrics, logs, and events that describe the real-time health of business systems. The focus has always been to turn billions of signals into a clear understanding of what matters and what needs to happen next.
Building on top of the LM Envision platform, Edwin AI applies agentic workflows to filter out noise, isolate root causes in plain language, and trigger the right response across connected tools like ServiceNow, Slack, and automation platforms. Instead of flooding engineers with alerts, Edwin acts as an autonomous teammate capable of triaging, contextualizing, and remediating issues before they escalate.
As LogicMonitor’s General Manager of AI, Karthik SJ, summarized during the webinar, the approach is pragmatic:
“Focus on what makes your beer taste better. It’s easy to build AI pilots and agent demos—it’s much harder to operationalize them. Start by working with the right partners, know your secret sauce, and invest there. For LogicMonitor, that meant doubling down on clean, contextual data and trusting that foundation models would keep improving. Don’t chase what’s changing; focus on what won’t—data quality, clarity, and measurable impact.”
Karthik SJ GM of AI, LogicMonitor
That focus—on meaningful operational value—has driven measurable outcomes across customer deployments.
Chemist Warehouse achieved an 88 percent reduction in alert noise, allowing engineers to spend more time on improvement work.
Nexon Asia Pacific saw roughly a 70 percent decrease in ServiceNow ticket volume, streamlining support operations.
Syngenta realized time-to-value within an hour of activation; Edwin surfaced a long-missed issue the NOC hadn’t caught in three years.
From concept to production, the first Edwin deployments were completed in about 12 weeks. Once the system proved value, customers expanded usage quickly—creating a feedback flywheel where each new use case generated more data, sharper insights, and faster returns. What began as an experiment in intelligent observability is now a living, evolving agent network powering agentic IT operations.
See what happens when enterprise observability meets frontier AI.
Only a small percentage of enterprises are seeing sustained, measurable results from agentic AI today. What separates them is execution discipline. The organizations that succeed treat AI deployment like any other high-stakes transformation: deliberate, iterative, and data-driven.
The path usually starts small. Pick one painful, repeatable workflow—for example, alert triage, ticket deflection, or incident resolution—and define what success looks like in quantifiable terms. Connect the right data and systems so the agent can act autonomously but safely, with clear guardrails around scope and permissions.
From there, stand up a thin vertical slice of the workflow, moving from triage to action to confirmation. Instrument every step and share the numbers internally; visibility builds confidence and accelerates buy-in. Once value is proven, expand horizontally to related tasks and vertically to multi-agent networks that can manage more complex, cross-functional processes.
Tribe AI’s Jaclyn Rice Nelson advised that scale should never be the first goal. Scale is the byproduct of getting the fundamentals right.
“You don’t want to overcomplicate things. Begin small, build iteratively, and realize value along the way. The biggest mistake enterprises make is trying to deploy too much too fast. The wins come from one well-scoped workflow that proves impact, earns trust, and becomes the foundation for what comes next.”
Jaclyn Rice Nelson Co-founder & CEO, Tribe AI
Finally, balance top-down initiatives—flagship programs driven by leadership—with bottom-up enablement, giving teams access to AI solutions that improve daily work. This two-speed strategy keeps innovation grounded in business impact while nurturing the organizational muscle needed to sustain it.
See how agentic AIOps transforms IT from reactive to predictive
The next phase of enterprise AI is already taking shape. Instead of isolated copilots or single-task automations, organizations are beginning to deploy multi-agent systems. That means networks of reasoning agents that coordinate across departments, tools, and data sources under human oversight.
As this architecture matures, memory and state will become foundational elements of enterprise infrastructure. Agents will retain organizational context, learn from every interaction, and continuously improve performance. Off-the-shelf agents will handle standard processes such as ticket routing or report generation, while custom-built agents will focus on high-leverage, domain-specific workflows that directly drive competitive advantage.
LogicMonitor’s Karthik SJ summarized this shift as a matter of operational speed and precision: “What took days can now be cut down to hours—or even minutes.”
That acceleration captures what’s at stake. Agentic AI is redefining the pace at which enterprises can respond, adapt, and deliver value.
