Artificial intelligence is moving from answering questions to performing work.
That distinction changes almost everything about how businesses need to manage the technology. A chatbot that produces an inaccurate summary creates one kind of problem. An AI agent that updates a customer record, triggers an operational workflow, changes a software configuration or calls another system creates something considerably more consequential.
Once AI begins acting rather than merely advising, companies need to understand not only what the system produced, but how it arrived there, what information it used, which tools it accessed and what happened afterward. This is creating growing interest in a new layer of enterprise technology: AI observability.
The concept borrows from traditional software observability, where engineering teams monitor applications, infrastructure and system behaviour. Applied to AI, the objective is broader. Companies want visibility into model inputs and outputs, retrieval steps, tool calls, permissions, costs, latency and the sequence of actions performed by increasingly autonomous systems.
From Model Monitoring to Workflow Monitoring
Early enterprise AI monitoring concentrated heavily on the model itself: accuracy, hallucinations, latency and response quality. Those concerns remain important. But autonomous systems create a larger monitoring problem because the model is only one component. NIST’s Generative AI Profile extends the AI Risk Management Framework to generative AI and emphasises governance, measurement and risk management across the AI lifecycle.
An AI agent may use a language model to interpret an instruction, retrieve information from a company database, call an external API, update another application and then notify an employee. Every stage can create risk. The database may contain outdated information. The API may return an unexpected result. The model may select the wrong tool. A permission may be broader than necessary. The final action may technically succeed while producing the wrong business outcome.
Observability therefore needs to extend across the entire workflow rather than stopping at the model boundary.
Tracing Is Becoming a Core Control
One of the most valuable capabilities is tracing. A trace records the sequence of actions that occurred during an AI workflow. OpenTelemetry’s work on AI agent observability describes efforts to create common semantic conventions for agent applications and frameworks, helping teams interpret traces, metrics and logs more consistently across systems.
Suppose an AI procurement assistant recommends replacing one supplier with another. Management may need to know which supplier records it reviewed, whether current pricing information was used, which risk criteria influenced the recommendation and whether any restrictions were overlooked. The final answer is only one part of the decision trail.
Tracing turns an opaque event into something that can be investigated. An employee may discover that a customer record was changed incorrectly, but without a trace it may be unclear whether the problem began with incorrect source data, an ambiguous instruction, a faulty tool call or poor model judgement.
Audit Trails Matter More When AI Can Act
For regulated businesses, observability may gradually become part of compliance infrastructure. Financial institutions, insurers, healthcare organisations and other regulated companies already operate under extensive record-keeping expectations. AI agents will not remove those requirements. In many cases, they may increase the need for clear records of who or what performed a material action.
If software begins participating directly in decisions, organisations may need records showing what the system did, what information it relied on, which controls were applied and where human authorisation occurred. Internal audit teams could eventually review AI workflows in much the same way they review human-operated processes.
The objective is not to save every token indefinitely. It is to preserve enough evidence to investigate material events, demonstrate that controls operated as intended and identify when a system moved outside agreed boundaries.
Cost Visibility Is Becoming Part of the Problem
AI observability is not only about risk. It is also becoming important for cost control. Generative-AI workflows can involve several model calls, retrieval steps and tool invocations. OpenTelemetry’s generative-AI guidance highlights telemetry such as token counts, request volume and latency as useful signals for understanding both performance and cost.
One workflow may call several models. It may retrieve large amounts of data. An agent may repeat tasks, invoke tools unnecessarily or use an expensive model for work that could have been handled by a smaller one. At small scale, these inefficiencies may be difficult to notice. Across millions of automated tasks, they can become significant.
Companies therefore need visibility at workflow level. Which agents consume the most tokens? Which tasks create repeated model calls? Where are premium models being used unnecessarily? Which automated processes actually create measurable business value? Without observability, AI expenditure can spread across departments faster than finance teams can connect it with outcomes.
Permissions Will Become a Major Control Point
The usefulness of an AI agent depends partly on what it can access. A customer-service agent may need customer records. A finance agent may need accounting information. A software agent may need access to development environments. Those permissions create corresponding risk.
Businesses are likely to adopt more granular permission architectures. An agent might be allowed to read customer data but not modify it. Another could prepare a transaction but require human approval before execution. A third might operate autonomously only below defined financial thresholds.
Observability helps organisations determine whether these boundaries work in practice. If an agent repeatedly attempts actions outside its authorisation, or if permissions expand over time without deliberate review, the monitoring layer should make that visible.
Human Oversight Needs Evidence
Human oversight is frequently presented as the solution to AI risk. But businesses also need to know whether that oversight actually happens. If a system recommends a high-risk action that technically requires employee approval, companies may need evidence of who reviewed it, what information was available and whether the recommendation was modified.
This creates a combined human-machine audit trail. The purpose is not to monitor employees excessively. It is to ensure accountability remains clear when automated systems and employees jointly perform a process.
Without that clarity, responsibility can become ambiguous. The employee assumes the system checked something. The system is designed on the assumption that the employee will verify it. A failure can sit precisely between those assumptions.
Observability Creates a Feedback Loop
The same data used for control can improve AI systems. If an agent repeatedly escalates the same category of transaction, the workflow may need redesigning. If a model frequently retrieves irrelevant documents, the knowledge architecture may need improvement. If employees consistently override a particular recommendation, that behaviour may indicate a deeper problem with the model, instructions or data.
Observability therefore creates a feedback loop. Instead of evaluating AI only during initial testing, organisations can study how systems behave in real operating environments. This becomes increasingly important as models are updated, business conditions change and connected systems evolve.
AI Agents Will Create New Operational Dependencies
Autonomous AI will also become part of the company’s wider technology architecture. A single agent may depend on several models, cloud services, databases and external APIs. If one of those components changes, the agent’s behaviour may change as well.
This makes dependency visibility important. Companies need to understand not simply whether an AI application is available but which external services determine how it operates. An external model can change even when the business has not modified its own code.
Observability can help detect whether outcomes shift after provider updates, data changes or integration modifications.
AI Incident Management Will Need Its Own Playbook
Businesses already maintain procedures for cybersecurity incidents and technology outages. AI is likely to require an adjacent incident-management discipline. A problematic model output is not necessarily an incident, but repeated incorrect actions, unexpected access to sensitive information or autonomous behaviour outside intended boundaries could become one.
Companies will need methods for stopping agents, reviewing activity, restoring affected records and identifying why the failure occurred. Without good observability, that process becomes considerably harder.
From AI Adoption to AI Operations
The first phase of enterprise AI was largely about experimentation. Teams tested copilots, content tools and internal assistants. The next phase is becoming more operational. NIST continues to frame AI risk management as an ongoing process rather than a one-time deployment decision through its AI Risk Management Framework.
Model quality will remain important. But organisations will also need visibility. They need to know what agents are doing, what they can access, how much they cost, when they fail and where humans remain accountable.
This suggests that one of the most important enterprise AI technologies may not be another model at all. It may be the infrastructure that allows companies to understand the models they already use. As autonomous systems become more capable, observability is likely to move from a specialist engineering feature to a basic requirement for operating AI safely and economically at scale.
References
1. NIST — Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
2. NIST — AI Risk Management Framework — https://www.nist.gov/itl/ai-risk-management-framework
3. OpenTelemetry — AI Agent Observability: Evolving Standards and Best Practices — https://opentelemetry.io/blog/2025/ai-agent-observability/
4. OpenTelemetry — OpenTelemetry for Generative AI — https://opentelemetry.io/blog/2024/otel-generative-ai/