Technological Plasticity and Observability: The IT Ecosystem’s Sensory Cortex

When we talk about Modern full-stack observability systems/platforms, tools like Instana are tasked with monitoring complex, distributed applications across infrastructure, middleware, and user experience. The challenge is immense: as software complexity and data volume explode and continuously scale up, human operators [SREs, DevOps, among others] struggle to keep up with floods of metrics, logs, and alerts. Observability can arguably be seen as a “central nervous system” of applications, similar to how our brain and nerves monitor and control our bodies and actions. Humans make and design tools that resemble their understanding of their own nature and limitations, there is no exception at the time of developing observability tools. As a former neuroscientist now working as technical product manager for the IBM Automation portfolio this comparison intrigued me. In the frame of celebrating the Instana’s 300 posts where we debate, provoke, propose, let’s navigate the analogy of comparing observability tooling to cognitive and sensory processing, for instance, ingesting sensory data, focusing attention on important signals, storing memories of past events, and making decisions (often automatically) to maintain system health based on prior experiences/data. Even while cognitive processes may unveil how humans interact with the world, these dynamics change, evolve and get extended and refined over time. In a constantly evolving IT Landscape, the bet and the challenge can be expressed in the same lines. IT Ecosystems need solutions capable to integrate as seamlessly as possible and scale up while learning from internal and external signals such as business specific context and industry standards. This piece will proposed the concept of technological plasticity as the capacity of a technological system or platform to adapt, evolve, and reconfigure in response to internal changes or external stimuli such as new requirements, tools, environments, or disruptions without requiring a complete redesign.

Vision and Pattern Recognition in IT Systems

Humans process vast amounts of sensory information, yet our visual system quickly recognizes patterns and anomalies (a face in a crowd, a red light at an intersection) with remarkable speed and accuracy. The brain dedicates huge capacity to vision and other senses, using specialized regions to handle different inputs. Similarly, Instana serves as the “eyes” of an IT environment. It collects telemetry data from multiple sources, such as infrastructure metrics, application traces, logs, user experience , analogous to different senses. The key is not just raw data collection but intelligent pattern recognition. Modern observability tools like Instana leverage AI/ML to identify meaningful patterns in this deluge, such as detecting an anomaly or performance regression that humans might miss.In fact, human brains excel at pattern recognition from complex data (like spotting trends or outliers, unknowns, threats), an ability observability tools mimic in a way. For example, IBM’s Instana Observability continuously monitors application performance in real time and provides AI-powered insights into issues. It’s supposed to provide a “detailed health report” of your applications, automatically identifying where things might be going wrong. By imitating the brain’s visual cognition, observability systems use dashboards and algorithms to map out normal vs. abnormal system behavior, quickly highlighting issues much like our visual cortex spots a suspicious pattern in a scene.

Instana can progressively enhance the IT system’s “vision” by incorporating multi-modal analysis and integrating with more and more data streams/systems of the Companies’ IT Operational Ecosystem. Just as the brain fuses inputs (sight, sound, etc.) into a coherent picture, observability platforms should correlate metrics, logs, traces and multi source data to present an accurate view of system state, or in Neuroscience terms, a state of the world.

Selective Attention: Filtering Signal from Noise

Going to a second level, our brains are equipped with a powerful attention mechanism that filters out most of the sensory input as “background noise” and focuses on what matters most at any given moment. You don’t consciously notice every sound or sight in your environment, attention zeroes in on a relevant conversation or a flashing warning light, for instance. In IT operations, this selectivity is crucial: among  thousands of alerts and log entries, operators need to find the one clue that explains an outage. A key goal of observability is to reduce the cognitive overload on humans by separating the signal from the noise. In fact, and paradoxically, in complex IT systems today “the bottleneck is now the human brain” because it’s hard to know where to look when something breaks. Too often, teams face alert storms where important warnings are buried under a flood of minor notifications. Cognitive science tells us that selective attention prioritizes relevant information and filters out the rest, therefore, observability tools must do the same by intelligently suppressing noise and highlighting anomalies that require action based on the rules that govern the state of the world.

Advanced systems like Instana, use anomaly detection and event correlation to cut through the noise. Instead of hundreds of independent alerts, an AI algorithm can group symptoms and point to a likely root cause, effectively saying “all these signals are related to the same incident.” ScienceLogic’s platform describes ending “the tyranny of endless alerts” by connecting predictive insights to actionable responses, rather than flooding operators with raw warnings. In cognitive terms, the observability platform uses an “attention filter” to route only the crucial information to engineers, much like our brain brings an urgent stimulus into conscious awareness. The benefits are tangible: reducing alert fatigue and split attention leads to faster incident resolution and fewer missed critical events. Importantly, our brains weight our knowledge of the world but also our own priors, so here any cutting-edge observability tool should consider its own context/ prior knowledge to weight how noise is filter and not just the industry/external/expert level coming from all around. Indeed, Observability tools can get better at attention management by using contextual awareness and priority scoring, rthis, inspired by how the brain’s attention is guided by both external cues and internal goals.

Memory and Learning from Incidents

At the core of any cognitive processes, including sensory correlates, memory is fundamental. Our brains retain past experiences, it is stored as a Prior or precedent, both short-term working memory and long-term knowledge, which inform how we react to new situations. When you encounter a problem similar to one you’ve seen before, you subconsciously draw on that memory to respond faster. Full-stack observability systems need memory, too. This means not only storing historical monitoring data, but truly learning from past incidents to improve future detection and response. Traditional monitoring might log an incident and that’s the end of it, but an AI-driven approach analyzes the incident post-mortem and remember the signature/prior for next time. Indeed, many tools now incorporate continuous learning: they retrain anomaly models on new data and update knowledge bases with resolution steps. Over time, this creates a form of “IT institutional/organic memory” that makes the system smarter with each incident, just like us, humans with each experience.

Decision-Making: Reflexes and Reasoning in Automation

Beyond observing, storing and analyzing, the human brain decides and acts. Some decisions are reflexive (touch a hot stove and we will take off our hand away without thinking), while others are deliberative (weighing options based on experience and future predictions). Similarly, observability doesn’t stop at the insight level right? Powerful and accurate insights open the floor to increasing automated actions to remediate issues or optimize performance. This is where analogies to human decision processes, from instinctual reflexes to learned strategies, then this become particularly useful.

Early IT automation often relied on static rules (e.g., if CPU > 90% for 5 minutes, add a server). This is linked to hardwired reflexes or simple heuristics, effective for known scenarios but really light/futile or even risky for complex ones. Humans, however, also use priors (prior knowledge) and contextual judgment to make better decisions. In AI terms, this is the equivalent to machine learning models that can incorporate probabilities and past learnings rather than fixed rules. Modern “smart” systems like Instana aim to combine both approaches: they execute predefined playbooksfor well-understood events (like a reflex), and use AI recommendations for novel or complex situations (like cognitive reasoning). Of course, is hard to say if automations can include such smart systems that can weight these powerful recommendations and solve very complex problems, but it seems like we are making steps towards to it, or at least, give the human much more depth and accuracy while much less cognitive overload and risk.

Critically, effective decision-making in observability combines fast and slow thinking (borrowing psychologist Daniel Kahneman’s terms). The “System 1” fast thinking corresponds to automated, immediate actions for routine problems (e.g., auto-restart a crashed service, scale out for a sudden load spike). The “System 2” slow thinking corresponds to in-depth analysis and planning for unusual or high-stakes issues (e.g., analyzing why a memory leak is happening and devising a code fix or patch). A robust observability platform should facilitate both: automated self-healing for known issues and rich tools (or AI assistants) for engineers to perform deeper troubleshooting on new problems.

In any case, IBM Instana Observability could be thought as the “sensory cortex” of the stack, the node that connects the IT System Elements/Components. It provides real-time monitoring of applications with deep visibility and automated root cause analysis. Instana continuously collects telemetry (metrics, traces, etc.) and alerts on performance issues, essentially acting as the eyes of the IT environment, our afferent and efferent connections. With AI-powered insights, Instana can pinpoint issues in complex microservices, one might say like the brain’s visual system identifying a specific object in a busy scene that is relevant to achieve a goal. Instana’s role maps to perception and short-term memory: it observes what’s happening now and retains a recent timeline of events to understand the immediate context of an anomaly. Instana is central to potentialize other tooling that can act, automate [Terraform, Ansible, Turbonomic] and orchestrate to solve more complex problems and enhance resilience [Concert]

In anycase, thinking in Instana as resembling a cognitive system, the diagram illustrates Instana’s pipeline, inspired by how the human brain processes information. It shows how raw telemetry from sensors flows through real-time ingestion, adaptive baselining (attention models), and causal AI (reasoning) to produce prioritized, actionable insights. A full-fidelity historical memory loop continuously refines detection and root-cause accuracy. Instana’s open APIs and integrations allow this intelligence to trigger automation and align with tools like Terraform, Turbonomic, Concert, etc., enabling observability that not only sees but thinks and acts, which is not possible without the sensory cortex: Instana.

Plasticity and Integration: Building Adaptive IT Ecosystems

Resilience in IT ops can be viewed as an analog of the body’s homeostasis and the brain’s fault tolerance working together. Our autonomic nervous system keeps critical parameters in balance, and if something goes wrong (like dehydration or heat shock), there are emergency responses (sweating, thirst signals, etc.) to restore balance. Observability platforms with automation play a similar role: auto-scaling, self-healing, and failover mechanisms act to maintain equilibrium in system performance. Additionally, the brain’s resilience comes from redundancy and diversity – multiple pathways to process information, and the ability to route around damaged areas. Likewise, a smart observability system introduces redundancy by monitoring from multiple angles (if one signal fails, others can provide clues) and can route around failures by quickly switching traffic or instantiating backups (often triggered by automated runbooks). The intelligence to detect and act makes this self-resilience possible without always waiting for human intervention.

To maximize resilience, observability tools should pursue an autonomic operations architecture as an ideal, an idea first championed in IBM’s concept of autonomic computing. This involves closed-loop monitoring, analysis, planning, and execution (MAPE loops) all under continuous improvement. Concretely, tools could implement health “nerves” that constantly check vital signs (CPU, response time, error rates), reflex “muscles” that take instant action for quick fixes (restart a pod, kill a rogue process), and higher-level “brain centers” that coordinate complex response strategies (re-route traffic, degrade non-critical features to keep core services alive during peak load). Each of these can be enabled by AI: from anomaly detection (sense), to automated runbook execution (act), to AI-assisted incident command centers (plan/coordinate). For all this to work plasticity is central, the “technological plasticity” for a stack would be then equivalent to its capacity to integrate and connect with more tools, extend and scale up its capabilities to respond and predict signals from internal mechanisms and external triggers.

References

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·      Singh, A. (2023, February 28). The observability challenge: Limitations of the human brain. ScienceLogic. https://sciencelogic.com/blog/the-observability-challenge-limitations-of-the-human-brain

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