Software failures are inevitable. But they should never become disasters that wreak nationwide havoc.
Whether a failure escalates into a major disruption or is immediately identified, diagnosed and remediated comes down to how well an organization prepares and responds.
VP, Portfolio & Strategy, Dynatrace.
Building and delivering robust, resilient software requires deep, AI-driven, end-to-end observability that provides a consistent, unified source of truth to how well software environments are performing and the source of any issue that jeopardizes that performance.
Today’s enterprise software environments are complex, spanning cloud-native applications, multi-cloud deployments, third-party services, APIs, and the growing influence of AI.
These layered environments introduce significant opacity into the software supply chain, making it harder to manage risk, performance and resilience at scale.
The risk of modern tech stacks
Research shows that 42% of organizations anticipate experiencing an incident caused by one of their suppliers. Too often, teams are left flying blind when something goes wrong, which can be frustrating and costly.
To operate with confidence, businesses must see across their entire digital supply chain, which is not possible with basic monitoring.
Unlike traditional monitoring, which often focuses on siloed metrics or alerts, observability provides a unified, real-time view across the entire technology stack, enabling faster, data-driven decisions at scale.
Implementing real-time, AI-powered observability covers every component from infrastructure and services to applications and user experience.
Observability is a strategic necessity
End-to-end observability is evolving beyond its current role in IT and DevOps to become a foundational element of modern business strategy. In doing so, observability plays a critical role in managing risk, maintaining uptime and safeguarding digital trust.
Observability also enables organizations to proactively detect anomalies before they escalate into outages, quickly pinpoint root causes across complex, distributed systems and automate response actions to reduce mean time to resolution (MTTR).
The result is faster, smarter and more resilient operations, giving teams the confidence to innovate without compromising system stability, a critical advantage in a world where digital resilience and speed must go hand in hand.
Resilient systems must absorb shocks without breaking. This requires both cultural and technical investment, from embracing shared accountability across teams to adopting modern deployment strategies like canary releases, blue/green rollouts and feature flagging.
Modern strategies only work if teams have real-time feedback and clarity, enabling organizations to understand what’s happening, why and what to do about it before customers ever notice a disruption.
Agentic AI: a new level of risk
We have entered the AI era, as organizations adopt generative and agentic AI to accelerate innovation, increase productivity and lower cost. They also expose themselves to new kinds of risks.
Agentic AI can be configured to act independently, making changes, triggering workflows, or even deploying code without direct human involvement. This level of autonomy introduces serious challenges that accompany the potential benefits of AI.
For example, a misconfigured agent or a malicious prompt can create far reaching downstream consequences at machine speed, whether that be cost overruns or anomalous behavior or full blown outages.
Small ripples can become waves, faster, broader and harder to contain. Real-time, AI-driven observability platforms are essential, not just for monitoring what the agents do, but for understanding how they act, how they interact with other systems and when intervention is needed.
Observability helps safely harness the potential of agentic AI and pave the way toward autonomous operations.
Safeguarding against disruption
Industry leaders must adopt new technologies including agentic AI to keep pace with their competition. At the same time, they must also adapt to new demands on security and compliance that come with operating under increasingly complex tech stacks.
The best way for organizations to handle this growing complexity and pressure is to treat observability as a strategic business driver and not simply as an IT capability. This ensures that every layer of the technology stack is transparent, accountable and resilient by design.
By prioritizing real-time, AI-powered observability, organizations can build lasting trust, adapt quickly and drive business growth, while avoiding wasting time and money firefighting damaging outages.
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The post Way too complex: why modern tech stacks need observability first appeared on TechToday.
This post originally appeared on TechToday.