Observability: A critical ingredient in making AI and agents work for you

Mitigating Risks of Agents

New technology comes with new risks. AI systems carry model, data, use, and infrastructure risks, as well as risks related to the processes that house AI systems and legal or compliance considerations. AI agents pose variants of these risks that may require additional attention: accountability gaps due to increased autonomy, cascading errors, integration risks with existing systems, and unpredictable behavior.

One capability needed to help monitor these risks is observability, in complement with an evolved AI governance framework, holistic testing practices, and clear management criteria, among others.

Observability: what it is and why it matters

Observability is the practice of collecting data from each AI action to enable AI system transparency and understandability—so organizations can see not just what is happening, but also why. Observability tools collect and analyze meaningful signals including logs, traces, model outputs, and data flows throughout its life cycle. These signals are interpreted into metrics and alerts relevant for business leaders, helping to turn technical data into actionable business insights.

Without observability, organizations may operate with limited visibility into production of AI agent systems. Because AI differs from traditional software—complete input testing isn’t feasible, and AI actions may change over time—certainty about system behavior in every circumstance isn’t possible. Observability is part of what is needed to fill this gap, to monitor changes or deviations in expected performance, and identify enhancements to further improve performance.

Let’s consider our example of a chatbot backed by AI agents deployed in an enterprise cloud-based platform. Observability tools capture interactions this agent has with data sources, environments, and other agents into a log and then processes that log to help us understand what changed. Did a data source fundamentally change? Did a user trigger a different workflow that conflicted with this agent? Have the asks of the agent fundamentally changed from the inquiries the agent was designed around?

Observability helps leaders quickly figure out what went wrong and why

Instead of guessing, you can get clear evidence: Was it a software update? A change in the data? A glitch with the deployment infrastructure? With observability, you can trace the problem to its source—maybe a new version of the chatbot’s underlying large language model (LLM), provided by a third party, was deployed without proper testing, or the data feeding the AI became outdated overnight. Or maybe the owners of the chatbot decided to trial a new underlying LLM without running standardized predeployment quality checks.

Observability tooling can capture metadata from the individual actions taken by the chatbot and surface metrics that help the team trace the issue. Alerts that identify access to unfamiliar or unapproved data sources can indicate unexpected system activity that deserves attention.

Observability helps make AI systems transparent by capturing raw signals in data and providing the capability to turn those signals into auditable controls that can prevent issues, detect risks, evidence compliance, and strengthen governance.

Components of an Observability System

Systems for observability are designed to capture four main categories of data surrounding an AI system to facilitate decision-making: system behavior, system health, user access, and data flow.

Observability data can reveal what is happening inside AI systems: what they do (e.g., prompts, context, outputs) and how they do it (e.g., latency, token usage, reasoning). By collecting and connecting this evidence, organizations can spot risks early, reduce hallucinations, build trust, and utilize AI safely.

Connecting this evidence can drive faster decisions:

• Roll back changes: Identify issues with new models and features in real time through alerts, so you can revert quickly to previously reliable processes or models.
• Raise guardrails: Detect undesired behavior and tighten controls.
• Notify stakeholders: Trigger alerts when key performance indicators (KPIs) dip so leaders can act before issues escalate.

It also can drive direct value:

• Cost governance: Reduce costs to help achieve successful outcomes (e.g., tokens per successful AI response, retries per success).
• Capacity planning: Manage AI capacity to actual peak loads (e.g., response times, error rates).
• Innovation: Rapidly experiment with new models, prompts, or features, and iterate, so you can catch issues before they become big problems.
• Business alignment: Directly measure ROI by understanding impact to business outcomes, such as revenue, user satisfaction, and compliance.

Each function plays a role

Technical data should be collected, processed, and converted into business insights that can drive growth and innovation. While there is no single owner of observability, each function plays a meaningful role:

• IT: Establish infrastructure, tooling, and capabilities for effective data capture of AI and agent activity. Consider cybersecurity requirements (e.g., role-based access controls, network monitoring) and data governance (data access, accuracy, platform modernization), and reflect new requirements given AI goals.
• Product owners: Align on KPIs and metrics relevant to the AI system. Establish predeployment testing practices, facilitate measurement of metrics in production, and monitor and manage alerts and flags.
• Users: Provide feedback on performance to product owners to support continuous improvement. Perform human-in-the-loop responsibilities as expected.
• Compliance, risk, and internal audit: Support AI teams and product owners by identifying risks and facilitating mitigation plans. Perform independent testing and monitoring using observability to provide real-time feedback.

Getting started on your observability journey

When done right, observability can increase confidence to deploy AI capabilities in more autonomous settings, accelerate decision-making, and drive the value we are aiming for with AI.

The insights that observability afford can improve confidence and trust in AI systems—both internally and by customers. Observability for AI systems also supports more rapid speed to market through improved confidence in AI decision-making, monitoring, and intervention. A few actions should be taken to get started:

• Identify critical checkpoints: What are the key moments in your workflows where success or failure matters? Instrument these checkpoints with observability events.
• Define KPIs that tie to business goals: What metrics effectively reflect your business goals and organization needs? Track them in real time using dashboards and analytics tools.
• Choose the right tools: Invest in observability platforms that integrate with your enterprise systems and enable data capture from the tools you use. Explore both embedded observability tooling from AI and technology platforms as specialized tooling like DataDog that integrate cybersecurity features with application monitoring.
• Integrate observability into workflows: Embed observability in development, deployment, and incident response processes.
• Review and improve: Regularly analyze observability data to find opportunities for optimization and innovation.

AI observability is a central component to building trust, protecting the business, and unlocking the full value of AI. It’s not just for tech professionals; it’s for any leader who wants to make AI work for their organization. By making AI systems transparent and understandable, observability enables early detection of issues, smarter decisions, and continuous improvement. We can help turn your AI from a black box into a business engine you can trust.