By Sadia Tahseen, Oracle ACE
For decades, enterprise monitoring was built around a relatively predictable question:
Did the transaction complete successfully?
An order entered one system, an integration transformed the message, and an ERP application created the corresponding transaction. When something failed, support teams examined an error code, reviewed a payload, corrected the data, and restarted the interface.
Agentic AI changes this operating model.
An AI agent may interpret an employee’s request, retrieve information from multiple sources, decide which tool to invoke, ask another agent for assistance, pause for human approval, and then initiate a transaction through an integration. The process is no longer a single deterministic path. It is a chain of decisions involving probabilistic AI, governed application services, integration logic, business rules, and human judgment.
In this environment, conventional monitoring is necessary but insufficient.
Monitoring Detects a Problem; Observability Explains the Process
Monitoring and observability are related, but they are not identical.
Monitoring typically checks known conditions: whether a service is available, whether an integration failed, whether latency exceeded a threshold, or whether an agent session ended in an error.
Observability goes further. It uses traces, logs, metrics, business identifiers, decision records, tool invocations, and workflow history to reconstruct what happened across the complete process.
The central question is therefore no longer merely:
Did the system work?
It becomes:
Did the agents, integrations, and people collectively produce the correct, authorized, and useful outcome?
A mature observability model must examine four connected layers:
- Integration execution
- Agent behavior and decision quality
- Human intervention and accountability
- Business outcomes
Layer One: Observing Integration Execution
Integration remains the transactional foundation of agentic automation.
An AI agent may interpret intent and recommend a next action, but deterministic integrations typically perform the controlled work of retrieving project data, validating suppliers, creating invoices, updating records, or submitting transactions.
Enterprise integration platforms can provide runtime observability through execution records, errors, activity streams and business identifiers. In Oracle Integration, for example, primary and secondary business identifiers allow teams to locate an integration instance using meaningful business values rather than relying solely on a technical instance number. A support analyst can, for example, search using an invoice number, employee identifier, project code, filename, or other configured business value. [1]
This distinction matters. Business users rarely report that “integration instance 184726 failed.” They report that:
- Invoice 104783 was not created.
- Project PRJ-2089 was not updated.
- Supplier ABC Services is missing and so on.
Business identifiers connect technical telemetry with the language of the business.
Oracle Integration also supports reviewing the activity stream for an integration instance. The activity stream can show the path followed by the flow and provide diagnostic information about individual steps. Oracle permits activity-stream data to be sent to OCI Logging, allowing organizations to analyze integration activity using centralized cloud logging rather than relying only on the immediate Oracle Integration interface. [2]
Oracle’s 26.07 product update also reports that user viewing of integration activity streams can be audited. The same update describes Log Analytics integration for human-in-the-loop workflows, enabling longer-term persistence, searching, and dashboarding of workflow and task information. Because Oracle release capabilities can depend on rollout status, configuration, region, edition, or feature enablement, customers should confirm availability in their own environments.[3]
These capabilities form the first observability layer: what integration ran, what business transaction it represented, which steps succeeded, and where the execution failed.
But an integration trace alone cannot explain why an AI agent selected that integration or whether it used the right information. That requires a second layer.
Layer Two: Observing Agent Behavior
AI agents introduce new dimensions that traditional application monitoring was not designed to capture.
An agent may:
- Interpret natural-language instructions
- Retrieve contextual information
- Select among multiple tools
- Delegate work to another agent
- Generate a recommendation
- Ask a clarifying question
- Retry an action
- Escalate the decision to a person
Observability must therefore capture not only whether the agent ran, but also how it interacted with its approved tools and information sources.
Oracle Fusion AI Agent Studio includes monitoring and evaluation capabilities for agent runs. Oracle documentation identifies metrics including error rates, session counts, P50 and P99 latency, input and output token usage, correctness, groundedness, answer relevance, and context relevance. Monitoring is intended to show production behavior, while evaluation is used to assess an agent before deployment and after changes such as a model update. [4]
These metrics answer different questions.
Error rate indicates whether sessions are ending unsuccessfully.
Latency shows whether the agent can respond within acceptable operational timeframes.
Token usage helps reveal expensive prompts, excessive retrieved context, or inefficient agent design.
Correctness compares an agent’s result with an expected reference answer during evaluation.
Groundedness evaluates whether an answer is supported by the retrieved source material.
Answer relevance examines whether the response addresses the user’s question.
Context relevance assesses whether the information retrieved for the agent was appropriate and useful.
No single metric is sufficient. An agent may respond quickly but incorrectly. It may produce a relevant answer that is not grounded in authoritative data. It may provide an accurate recommendation but consume excessive resources. Observability requires examining performance and quality together.
Oracle’s Agent Studio testing environment also provides a detailed trace showing the sequence of an agent run, including the tools called, the time taken by individual steps, and token-related metrics. Administrators can compare previous test runs and refine the configuration before publishing an agent team.[5]
The goal should not be to expose or preserve unrestricted private model reasoning. Instead, organizations should retain the operational evidence required to assess the action:
- The originating request
- The agent and version used
- Relevant instructions and policy configuration
- Information sources retrieved
- Tools invoked
- Tool inputs and outputs, subject to privacy controls
- Validation and policy results
- Final recommendation or action
- Errors, retries, and escalations
This provides decision traceability without treating hidden model reasoning as a dependable audit record.
Layer Three: Observing Human Decisions
Human-in-the-loop controls are often presented as safeguards for AI, but they are also an important source of operational intelligence.
Oracle Integration describes human in the loop as an approval-orchestration capability for obtaining human approval, feedback, and oversight at critical points in agentic automation. Oracle identifies use cases such as reviewing a high-stakes action before execution, helping an uncertain agent determine the next step, and intervening when a tool used by the agent fails. [6]
A human approval record should capture more than a binary approve-or-reject result.
Useful observability data includes:
- Why the task was escalated
- Which policy or threshold was triggered
- Who received the task
- Whether it was reassigned
- How long the decision took
- What supporting information was displayed
- Whether the approver accepted or modified the recommendation
- Comments or reason codes
- Whether the task breached its service-level target
- What happened after the decision
- Whether the decision was subsequently reversed
These records help answer important questions.
Are agents escalating too many routine transactions?
Are reviewers consistently overriding the agent’s recommendations?
Does one approval group create a major process bottleneck?
Are particular suppliers, projects, or expense categories generating disproportionate exceptions?
Are users approving recommendations without reviewing the evidence?
Human intervention should not be treated simply as an interruption in automation. It is feedback about where the automated process is uncertain, poorly configured, insufficiently trusted, or correctly identifying risk.
Oracle’s agentic AI documentation includes an example in which an integration calling a human-approval workflow is registered as an agentic AI tool. The agent pauses until a person approves or rejects the expense, demonstrating how integration activity, agent actions, and workflow decisions become part of one execution path. [7]
Layer Four: Observing Business Outcomes
An agent can perform correctly at a technical level without creating meaningful business value.
An expense agent may reduce processing time but increase incorrect coding. An invoice agent may raise the rate of touchless processing while allowing more downstream corrections. A project-analysis agent may produce compelling explanations that do not improve forecasting decisions.
Observability must therefore connect system performance with operational and financial outcomes.
Oracle Fusion AI Agent Studio includes a Value dashboard for published agent teams. Oracle documentation describes metrics for usage and estimated time and cost savings. Importantly, the estimated value depends on configured assumptions such as time saved and cost saved per run. These measures can support portfolio-level analysis, but they should not automatically be interpreted as independently verified financial savings. [8]
Organizations should combine platform metrics with business measures such as:
- Percentage of transactions completed without manual correction
- Exception and reversal rates
- Average approval time
- Duplicate-invoice rate
- Accuracy of property and project coding
- Time required to resolve failed transactions
- Number of policy violations prevented
- Financial-close impact
- Forecast accuracy
- User satisfaction
- Cost per completed business transaction
The most important metric is rarely the number of agent runs. It is whether those runs produced accurate, timely, compliant, and valuable outcomes.
Security Is Part of Observability
Observability systems frequently contain sensitive information: employee requests, supplier details, financial transactions, integration payloads, agent prompts, approval comments, and operational errors.
Access to observability data must therefore be governed.
Oracle documents role-based capabilities for different areas of the Oracle Integration Observability section. Oracle Fusion AI Agent Studio similarly requires specific permissions to view monitored agent records, including a permission group associated with generative AI workflow execution. [9][10]
A sound design should ensure that:
- Users see only the telemetry required for their roles.
- Sensitive payload fields are masked or excluded.
- Access to logs and traces is itself audited.
- Retention periods reflect regulatory and operational needs.
- Agent-development access is separated from production oversight where appropriate.
- Human comments do not expose unnecessary personal or confidential information.
- Correlation identifiers do not become an uncontrolled source of sensitive data.
Observability should increase accountability without creating a secondary repository of ungoverned enterprise information.
References:
[1] Oracle: Search for Business Identifier Values
[2] Oracle: Use OCI Logging to Analyze Integration Activity
[3] Oracle Integration 26.07 update
[4] Oracle: Monitor and Evaluate AI Agents
[5] Oracle: Edit and Test AI Agents Iteratively
[6] Oracle: Human in the Loop for Agentic AI
[7] Oracle: Using Agentic AI in Oracle Integration 3
[8] Oracle: View the Measured Value of Agents