As software systems increasingly rely on machine learning, AI, and automated decision making, organizations often hear two terms used interchangeably: observability and explainability. While closely related, they serve distinct purposes: observability helps system owners see what a system is doing, while explainability helps them understand why a system reached a particular outcome.
Observability is about visibility. It enables organizations to monitor system health, performance, and runtime behavior through instrumentation and telemetry signals such as metrics, logs, traces, and events. In AI systems, observability extends beyond uptime and latency to include model-and workflow- specific behavior, and helps answer questions such as:
- Is the system behaving as expected in production?
- Where are failures occurring (retrieval, tool execution, orchestration)?
- Is performance degrading over time due to drift or changing inputs?
- Are there security, privacy, or policy concerns in prompts, outputs, or tool usage?
These insights are essential for reliability, operations, and incident response.
For AI systems, observability is especially important because behavior can change even when the underlying software does not. Small shifts in prompts, retrieved context, tool availability, model versions, or user inputs can cause large changes in outputs. This is amplified in multi-step and agentic workflows, where failures may emerge from subtle interactions across retrieval, planning, tool calls, and generation. High-quality observability provides end-to-end traceability across workflows, linking user intent to intermediate steps and final outputs so teams can quickly distinguish between model issues, data issues, orchestration bugs, and policy violations.
Visibility is not the same as understanding. Even with full traces and rich workflow telemetry, organizations still need to understand why an AI system produced a specific result, especially when outputs influence decisions, trigger actions, or affect customers. That is where explainability becomes essential.
Explainability focuses on the “why” behind an AI system’s behavior. It provides insight into the factors that led to a specific outcome and supports confidence that the system is behaving appropriately. Explainability can support debugging and model improvement, but its real impact is governance by supporting validation, accountability, and oversight.
In practice, explainability looks different depending on the type of AI system. What works for traditional machine learning may not translate directly to generative AI or agentic workflows.
Traditional machine learning systems typically make bounded decisions such as classifications, predictions, scores or rankings, based on structured inputs. Because the input-output relationship is bounded, explainability is often more straightforward to implement and evaluate.
Generative AI shifts the explainability problem. Instead of producing a bounded decision, the system generates content, often probabilistically, and the output can vary meaningfully based on context, prompt phrasing, or retrieved data. For GenAI, explainability is less about variable-to-output contribution and more about tracing which context such as prompts, retrieved sources, tool results, and policies, influenced what the model generated.
Agentic AI introduces the hardest explainability challenge because it is not limited to generating content since it can take actions over time. An agent may interpret a goal, create a plan, call tools, retrieve data, choose between options, and execute multiple steps in sequence. The final outcome may reflect a chain of decisions made across time.
Observability and explainability are equally important for trusted AI, but they are not equally easy to implement across all systems. In many cases, particularly for agentic workflows, observability is more straightforward to instrument and measure because actions, tool calls, and system events can be captured as telemetry. Explainability is harder. It must account for the reasoning and tradeoffs that produced the outcome, not just the sequence of steps. Observability tells organizations what happened, explainability helps them assess whether the outcome was justified.
One of our portfolio companies, Arize helps enterprises achieve observability and explainability through a platform that instruments applications so stakeholders can understand internal behavior from external outputs. For AI applications, this means capturing every LLM call, tool execution, retrieval operation and generation – along with inputs, outputs, latency, and token usage. With this level of observability, organizations do not have to speculate about what went wrong; they can review the execution trail and see what happened. In addition, Arize supports explainability by identifying the most important factors that drove a model’s output and making it easier to understand why a particular prediction was made. This allows organizations to see which input signals most influenced a given prediction, supporting both model validation and auditability.
As AI systems are deployed deeper into core workflows, both observability and explainability become essential. Observability provides the operational evidence needed to detect issues and improve reliability. Explainability provides the insight needed to justify outcomes, support governance, and enable oversight. Together, they help organizations manage AI systems with greater transparency and confidence.
