Uptime is only one dimension of agent health
A traditional application can be unhealthy when it stops responding.
An AI agent can be unhealthy while every API continues returning HTTP 200.
The agent responds.
But the answer quality is falling.
Retrieval is returning stale information.
Tool calls are failing.
Latency has increased.
The agent is repeating actions.
Token consumption has changed.
A new version is producing different behavior.
Users are abandoning tasks.
None of those problems is captured by a simple availability check.
Observability should connect three views
BICloud Tech recommends three operational views.
Availability, latency, errors, dependency failures, resource health, and API failures.
Tool calls, retries, orchestration steps, model calls, retrieval behavior, action sequence, and token consumption.
Task completion, groundedness, relevance, quality evaluation, safety evaluation, human overrides, and user feedback.
An agent can look healthy in one view and unhealthy in another.
Microsoft Foundry currently combines evaluation, monitoring, and tracing
Microsoft describes AI observability as the ability to monitor, understand, and troubleshoot AI systems throughout their lifecycle.
Its current Foundry model combines three core capabilities.
- Evaluation measures quality, safety, and reliability.
- Monitoring examines production behavior and operational signals.
- Tracing captures the execution path.
That combination is important because no single telemetry source can explain every AI problem.
Use the Signal → Context → Decision rule
Collecting data is not observability by itself.
For each signal, ask:
- Signal — What was observed?
- Context — What information is required to understand why it happened?
- Decision — What action could the team take?
For example:
Signal: latency increased.
Context: which model, tool, retrieval operation, dependency, or orchestration step changed?
Decision: optimize, roll back, investigate a dependency, or accept the behavior.
This prevents teams from accumulating telemetry that nobody uses.
Trace the business path
An AI response can involve several components.
User request → Application → Agent → Retrieval → Model → Tool → API → Business system → Model → Response
If total latency is high, the model may not be the problem.
If output is wrong, the prompt may not be the problem.
Tracing helps teams understand which component contributed to the result.
Microsoft’s current Foundry tracing capabilities capture information such as agent inputs, outputs, tool usage, retries, latency, and cost information for supported scenarios.
Monitor tool behavior
Tools give agents business authority.
They therefore deserve their own operational view.
- call volume;
- failure rate;
- timeout rate;
- retry rate;
- unexpected tool selection;
- downstream errors;
- authorization failures;
- human overrides where applicable.
A model that answers well but repeatedly invokes the wrong tool is not a healthy agent.
Separate technical success from task success
A technical request can succeed even when the business task fails.
The API returned successfully.
The agent generated a response.
But the user did not get the intended result.
BICloud Tech recommends tracking both.
Technical success: Did the system execute?
Task success: Did the intended business activity complete correctly?
This distinction is especially important for agents that perform multiple steps.
Quality needs production evidence
Preproduction evaluation is necessary.
It is not sufficient forever.
Production introduces new user phrasing, new documents, changed data, unexpected tool combinations, new process conditions, and volume variation.
Organizations therefore need a way to compare production behavior against accepted expectations.
Microsoft Foundry currently supports production monitoring and continuous evaluation patterns for supported scenarios.
The exact features and availability should be confirmed against current documentation, particularly where Microsoft labels components as preview.
Build a baseline before alerts
Alerting without a baseline creates noise.
Before choosing thresholds, understand normal behavior.
- Typical latency.
- Normal token consumption.
- Normal error rate.
- Typical tool-call patterns.
- Expected task completion.
- Expected evaluation range.
Then define alerts around meaningful deviation.
A fixed threshold may work for one workload. Another workload may need trend-based detection.
The objective is not to alert on everything unusual. It is to alert when an unusual condition requires attention.
Use the Evidence Triangle
When investigating an AI issue, BICloud Tech recommends combining three evidence sources.
What did the system record?
Was the output acceptable?
Did the result help complete the task?
This creates an Evidence Triangle.
Any one source can mislead.
Strong operational judgment uses all three.
Watch for silent degradation
One of the most important AI monitoring problems is silent degradation.
Nothing crashes.
The user experience simply becomes worse.
- source content changes;
- retrieval changes;
- model changes;
- prompt changes;
- permission changes;
- tool changes;
- increased load;
- downstream API changes.
This is why AI monitoring should include comparative quality evidence instead of relying only on infrastructure alerts.
Version every meaningful change
If the team cannot identify what changed, diagnosing regression becomes difficult.
- instructions;
- model;
- tools;
- retrieval;
- data source;
- index;
- workflow;
- permissions;
- agent version.
When quality changes, the investigation can then compare behavior against configuration history.
Monitoring needs ownership
A dashboard cannot own an incident.
Decide:
- Who responds to latency?
- Who responds to tool failures?
- Who owns quality regression?
- Who owns data-source problems?
- Who owns access failures?
- Who owns security alerts?
- Who decides whether the agent should be rolled back?
An agent may span several teams.
The observability model should map signals to owners.
Create an Agent Health Card
BICloud Tech recommends summarizing each important production agent with a lightweight Agent Health Card.
- Business purpose.
- Owner.
- Current version.
- Usage.
- Technical success.
- Task success.
- Latency.
- Error rate.
- Tool health.
- Quality measure.
- Cost or consumption indicator.
- Known issue.
- Current lifecycle state.
The card should not replace detailed telemetry.
It gives leaders and operators a common view of whether the agent deserves attention.
A practical observability sequence
- Define the business task.
- Identify critical dependencies.
- Establish baseline behavior.
- Instrument system telemetry.
- Enable tracing where appropriate.
- Define evaluation measures.
- Monitor important tools and actions.
- Connect signals to owners.
- Define meaningful alerts.
- Record version changes.
- Investigate deviations with telemetry, evaluation, and user evidence.
- Feed validated findings into optimization.
That final step matters.
Observability is not the destination.
It provides evidence for decisions.
Microsoft Foundry’s current monitoring model
Microsoft currently documents production agent monitoring through Foundry with operational metrics and evaluation results.
The Agent Monitoring Dashboard can surface signals including token usage, latency, success rates, and evaluation outcomes.
Microsoft notes that parts of the current dashboard experience are preview and therefore subject to preview limitations.
Application Insights is a core telemetry dependency in the documented setup, with Log Analytics permissions required for certain log-based views.
That is useful current platform context, but the architecture principle remains broader:
Monitoring is useful only when the organization can explain what a signal means and what action follows.
Where BICloud Tech can help
BICloud Tech Azure Monitor and Observability services help organizations improve monitoring, telemetry, dashboards, troubleshooting, and operational visibility across Azure environments.
BICloud Tech AI Enablement can help connect observability requirements to the broader AI architecture and operating model.
Where monitoring reveals deeper architecture problems, Architecture Review can provide a path for structured analysis.
Observe enough to make a decision
AI observability should not become an exercise in storing every possible signal.
Start with the business task. Identify the behaviors that matter. Collect evidence that helps explain failure. Connect the evidence to an owner. Use it to decide whether the system should be fixed, optimized, rolled back, restricted, or accepted.
If a production AI signal cannot lead to understanding or action, it is telemetry—not yet observability.