Observability alone isn’t enough. Orchestration defines what happens next.
For years, observability has been positioned as the key to solving operational complexity in IT. With the right metrics, logs, and traces, teams were told they would finally gain control over the environments they manage. And in many ways, they did. They know when systems degrade, when services fail, and where anomalies originate. Yet despite this visibility, outages still last for hours. Major incidents continue to disrupt operations worldwide. Postmortems still end with the same conclusion: the issue was detected quickly, but response and resolution took too long.
In many organizations, identifying an issue is no longer the hardest part. Aligning teams, tools, and actions after detection is where delays occur. As environments scale, the gap between knowing there’s a problem and coordinating an effective response and resolution continues to widen. This is the point at which observability reaches its limits and where a different operating model becomes necessary.
Why do major IT incidents persist even with modern observability tools?
The reality of modern IT environments
From human coordination to system-level response
Orchestration should be the standard operating model for modern IT
What if orchestration is paired with agentic AI?
The shift to adaptive operations
In the past year alone, the industry has witnessed several large-scale IT incidents that weren’t caused by a lack of visibility:
Global outages triggered by configuration changes that propagated faster than teams could intervene, such as the Google Cloud global outage, where an automated quota update was incorrectly applied and rapidly distributed across regions, resulting in widespread rejection of external API requests despite systems being actively monitored.
Cloud disruptions driven by cascading failures after routine changes, even with alerting in place, can occur. For instance, the Cloudflare incident revealed a latent bug in a service that was exposed by a routine configuration update, resulting in widespread network and service degradation.
Widespread service disruptions driven by unresolved failures within automated systems, such as the AWS DynamoDB incident, where an empty DNS record in the US-EAST-1 region failed to self-repair and led to outages across multiple industries globally.
In each case, signals were available early, and teams were aware that something was wrong. After issues were detected, response and resolution still depended on:
Manual handoffs between teams
Human interpretation of alerts
Bridge calls, Teams channel threads, and ticket reassignments
Engineers logging into multiple systems and tools to understand what happened and determine what to do next
That’s where delays begin to accumulate.
Observability tools excel at revealing what is happening and where impact is occurring. However, they do not define how systems should respond, which actions should happen and in what order, or how recovery should be coordinated across teams once a failure begins. As environments become more complex, these gaps place increasing strain on teams and limit an organization’s ability to respond quickly and resolve issues at scale.
In other words, major IT incidents persist because insight alone doesn’t guarantee timely resolution, particularly in globally distributed environments.
Modern IT environments are no longer bounded by systems that a single team can fully understand or control. They are hybrid by default, spanning on-premises infrastructure, multiple cloud providers, SaaS platforms, and managed services. Workloads are distributed across regions. Configurations propagate globally in seconds. A single change can affect dozens of services — many of which are owned by different teams or operated by external vendors.
When environments change continuously and ownership is fragmented, response cannot depend on manual interpretation and coordination. Teams need a way to define in advance how the system should react when certain conditions occur.
Orchestration provides that structure. It turns response from a series of improvised decisions into a governed, repeatable process. Instead of asking engineers to piece together what to do next during an incident, orchestration encodes that logic ahead of time.
In modern IT environments, events are rarely isolated. Failure in one layer quickly becomes a dependency issue in another. A regional service degradation can quickly affect users worldwide. What begins as a localized fault can turn into a major incident impacting global operations.
Orchestration provides clarity far beyond what observability alone can provide. It adds context and correlation to reveal the true severity of an event. This enables a dynamic assessment of the situation, allowing the environment to determine the most appropriate recovery action to perform rather than blindly executing automation triggered by an event. By evaluating dependencies, impact, and current system state, orchestration ensures that response is deliberate — applying guardrails to automated actions, enforcing approvals where required, escalating to human intervention when necessary, and executing coordinated actions across systems to prevent localized issues from escalating into major incidents.
Orchestration brings structure, governance, and repeatability to incident response, but it still assumes that response paths can be fully defined in advance. Agentic AI extends orchestration by adding the ability to reason when situations don’t match predefined expectations.
Instead of reacting only to static triggers, agentic AI continuously evaluates context as events unfold. It correlates signals across systems, assesses the scope and potential impact of an event, and adjusts its understanding as new information emerges. This allows response to evolve dynamically, rather than stalling when conditions diverge from known patterns.
When paired with orchestration, agentic AI does not operate independently. Orchestration establishes the boundaries — what actions are allowed, how they should be sequenced, and when human approval is required. Agentic AI operates within these constraints, helping determine which response path is most appropriate given current conditions.
The result is not autonomous IT, but adaptive operations. IT administrators remain in control, but their role shifts from passive monitoring and manual coordination to directing and refining response strategies, and stepping in only when human judgment is required.
In this model, orchestration provides the operating framework, and agentic AI provides the adaptive intelligence. Together, they enable modern IT environments to turn insight into action and respond to change with speed, context, and control — at a scale that observability alone cannot sustain.
SAP environments amplify many of the challenges discussed throughout this article. They are deeply interconnected and business-critical. A single change can ripple across systems and directly impact core business operations.
In practice, this means SAP teams can move beyond reacting to isolated technical alerts and instead respond based on business context. Orchestration makes it possible to correlate SAP application behavior with integration layers, cloud infrastructure, and recent changes, while agentic AI supports impact assessment and response selection when symptoms are ambiguous. The result is faster, more deliberate recovery — with actions executed in the correct sequence, approvals enforced where required, and full traceability maintained across SAP and non-SAP systems.
In these environments, visibility alone is not enough. What matters is the ability to assess impact, coordinate response, and act deliberately across SAP and non-SAP components. This is where orchestration, paired with agentic AI, becomes especially valuable — bringing structure, governance, and adaptive response to some of the most complex enterprise workloads.
Beyond response and recovery, orchestration and agentic AI also enable a preventive operating model. Many large-scale SAP incidents originate not from unknown failures, but from misconfigurations, inconsistent settings, or insufficient quality checks before changes are deployed.
By combining AI-assisted analysis with orchestration, organizations can shift left — continuously scanning configurations, transports, and environment states for risk or deviation from known-good baselines. Agentic AI can highlight potential misalignments, assess blast radius, and recommend corrective actions before changes are approved and executed, while orchestration enforces controlled deployment, sequencing, and approvals. In this model, prevention becomes a collision-avoidance control plane, reducing the likelihood that issues ever reach production rather than reacting after impact occurs.
Listen to the conversation between our CEO, Linh Nguyen, and CTO, David Stavisski, as they discuss why agentic AI is redefining SAP operations and moving organizations beyond passive observability.
Orchestration bridges the gap between insight and action by defining how systems should respond, not just what is happening or which tasks can be automated. It coordinates actions across tools, services, and teams, ensuring response is deliberate, sequenced, and governed rather than improvised.
Most organizations start by orchestrating a small set of high-impact, repeatable scenarios, such as self-healing recovery workflows, while integrating with existing tools and processes. Orchestration can be layered incrementally without replacing current monitoring, automation, or ITSM systems.
Incidents that span multiple systems, teams, or regions benefit most from orchestration, especially cascading failures, configuration-related outages, and recovery scenarios that require coordinated action across dependencies.
Control is maintained through guardrails, including defined response paths, approval requirements, execution limits, and auditability. AI assists with assessment and recommendation, while orchestration enforces when and how actions are allowed to execute.
Safeguards include policy constraints, human-in-the-loop escalation, validation checks before and after actions, and continuous feedback from system signals. Its recommendations are continuously validated against real-time system state, with automatic rollback, escalation, or human intervention triggered when conditions fall outside safe boundaries.
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