Financial institutions are bringing LLM applications and AI agents into customer service, internal knowledge workflows and operational processes. Once an AI application reaches production, monitoring model availability is not enough.
A user may report that an assistant gave the wrong answer. An engineer may see response time increasing. A platform team may notice token consumption growing faster than request volume. These symptoms may come from different parts of the same execution path.
A useful AI observability platform connects the user interaction to the agent workflow, model calls, retrieval, tool execution and the application services underneath. It should also provide enough context to investigate what happened without exposing more sensitive data than necessary.
Here are eight areas to evaluate.
First, understand what you need to observe
A production AI request may cross several layers: session, agent workflow, LLM calls, retrieval, tools, application services and infrastructure. If these signals live in separate systems, an investigation becomes a manual correlation exercise.
The goal is to make the execution path understandable.
1. Can you trace an AI request end to end?
A request may involve multiple model calls, retrieval operations, tool invocations and downstream requests. A single model-call log does not explain the full workflow.
Check for Trace and Span relationships, timing, status, useful attributes, visibility into model/retrieval/tool operations and support for the frameworks your teams use.
Demo test: Give the vendor one slow AI request. Can they show the full path from the user request to the slow model call, retrieval step or downstream API?
2. Can you investigate a conversation, not just a request?
Earlier turns may establish context. A tool call may change state. Later turns may depend on information gathered earlier.
Session-level analysis should group related requests, link a session to its traces and help find recurring failures or unusually long interactions. A request trace explains one execution; session analysis adds the wider conversation context.
3. Can you understand model usage and token growth?
Token consumption can change because traffic increases, prompts become longer, retrieved context grows, a different model is selected or failed workflows trigger retries.
Evaluate input, output and total token visibility, breakdowns by model/service/time, and correlation with request volume, latency and errors.
Do not treat a token chart as a complete cost model. Actual cost may depend on provider pricing, model type, cached tokens and billing rules. Clarify which values are measured, estimated or imported.
4. Can you see what the agent did with its tools?
Suppose an assistant answers a delivery question. It retrieves an order record, calls a logistics API and summarizes the result. If the answer is wrong, the cause may be stale retrieval data, a failed API call, an unexpected tool response or a model interpretation issue.
Check whether you can see tool name, timing, status, agent handoffs, relevant metadata and the downstream service involved.
5. Where does telemetry go, and what sensitive data is collected?
Prompts, responses, retrieved content and tool arguments may contain customer or business information.
Ask where telemetry is stored and processed, which deployment options exist, whether fields can be masked or excluded, who can access detailed traces, and how retention, deletion and export work.
The objective is not to collect everything. It is to collect enough evidence to operate the application responsibly.
6. Can you verify the platform’s explanations?
If a platform surfaces a diagnostic suggestion, the explanation should lead back to evidence: the relevant trace or Span, associated metric or log, timestamp, request/session context and observed error.
A conclusion that cannot be checked against telemetry is difficult to use in a serious incident review.
7. Does it fit your existing engineering environment?
Evaluate OpenTelemetry and existing instrumentation, APM and infrastructure monitoring, incident management, ITSM, identity integration, APIs and data export.
Avoid creating a new silo for AI telemetry. Engineers should be able to move from an AI trace to the service, database or infrastructure evidence behind it.
8. Is the platform production-ready?
Ask about availability, scaling, data handling during interruptions, upgrades, access control and support. Clarify how data volume affects retention and cost. AI workloads can generate many spans and high-cardinality attributes; a small proof of concept does not prove production scalability.
A vendor evaluation exercise
Use the same scenario for every shortlisted vendor:
An AI assistant takes 12 seconds to answer a customer question and returns an incorrect result. Some requests retry, token consumption has increased, but the application service appears healthy.
Ask each vendor to demonstrate how to find the session, inspect the full trace, identify latency contributors, check tool failures and retries, review token usage, inspect data masking, correlate application/infrastructure signals and export the investigation.
This tests the actual investigation workflow, not just dashboard design.
How to start without overengineering
- Choose an application with a clear owner and measurable user impact.
- Establish baseline request volume, latency, errors and token usage.
- Instrument sessions, traces, model calls, retrieval and tools where supported.
- Test slow responses, wrong answers, tool failures, retries and token spikes.
- Validate access, masking, retention and deployment requirements.
- Expand after engineering and operations teams find the evidence useful.
Where Bonree ONE fits
Bonree ONE is one example of a platform combining AI observability with application and infrastructure monitoring. Its AI observability capabilities provide visibility into LLM and AI agent call chains, sessions and token consumption, helping teams examine AI execution alongside the services and infrastructure involved.
The right choice still depends on your own frameworks, architecture, data policies and operating requirements. Use the same real-world scenario to validate every platform.
The takeaway
For financial services, AI observability is not just about seeing whether a model call succeeded. It is about understanding how an AI interaction was executed, what affected its performance, which tools and services were involved, and how much resource it consumed.
Start with the workflow you care about. Ask vendors to trace it end to end. Choose the platform that can demonstrate the evidence your teams actually need.
