Last year, a platform engineering team deployed an internal AI assistant to help investigate application issues.
The results were promising. The assistant summarized incidents, suggested likely causes, opened tickets, and automated a handful of low-risk operational tasks. Mean time to triage improved, engineers embraced it, and leadership wanted to expand its role.
Then one morning, the assistant recommended restarting a service experiencing elevated latency. The restart itself wasn't the problem, the recommendation was based on incomplete context. The assistant had pulled information from multiple systems, missed a failed dependency check, and acted on a tool response that wasn't properly instrumented.
When the team investigated, they found prompt logs, tool traces, and API records scattered across multiple systems, but no complete record of the workflow that led to the action.
That's the challenge many organizations face as AI systems become operational participants rather than passive assistants.
Most teams are still observing model calls when they should be observing agent workflows.
Why model observability isn't enough
Early AI observability efforts focused on what was easiest to measure:
Prompts
Responses
Token consumption
Latency
Cost
Those metrics matter, but they only describe a small part of the system.
Modern AI applications increasingly operate as agents. They retrieve information, invoke tools, evaluate results, make decisions, and sometimes take action in production environments. The model is only one step in a larger workflow.
When an agent investigates an outage, updates a configuration, routes data, or triggers automation, operators need answers to different questions:
What information influenced the decision?
Which tools were called?
What failed or was retried?
Were approvals enforced?
What action was taken?
What changed as a result?
Those are workflow questions, not model questions.
If your telemetry cannot answer them, your AI observability strategy is incomplete.
As agents move beyond chat interfaces and into operational workflows, observability leaders face a familiar challenge: understanding not just outputs, but execution paths.
The missing layer: Workflow telemetry
Agent workflows span multiple systems.
A single workflow may involve monitoring tools, knowledge bases, orchestration platforms, internal APIs, ticketing systems, and automation frameworks. Each produces telemetry, but rarely in a way that's easy to correlate.
The result is fragmented visibility, with prompt logs in one place, tool traces in another, audit records somewhere else, and operational events stored separately.
Many organizations are treating agent observability as a prompt-logging problem. That's roughly equivalent to debugging a distributed system by staring at a single application log and ignoring the trace.
This is a problem observability teams have already solved elsewhere. Individual logs rarely explain a production incident. Traces provide the context.
Agent workflows require the same evolution. Instead of observing isolated model interactions, organizations need visibility into the entire path from request to action.
What observable agent workflows look like
The unit of analysis should not be the prompt but the workflow itself. Effective workflow observability captures five layers.
Request layer
Every workflow starts with an initiating event, whether it's a user request, an alert, a scheduled task, or another agent. Understanding where a workflow originated provides the context needed to interpret everything that follows.
Context layer
Before making a decision, agents gather information from logs, metrics, traces, runbooks, tickets, configuration data, or knowledge articles. Because decisions are only as good as the information available, visibility into retrieved context is often more valuable than visibility into the final answer.
Tool layer
Agent workflows depend on tools. Organizations need visibility into which tools were called, what they returned, how long they took, and whether they failed or produced incomplete results. A tool failure upstream can easily explain a bad decision downstream.
Decision layer
Agents evaluate information and choose a path forward. Operators should understand which path was selected, what evidence supported it, and whether escalation or approval thresholds were met. The goal isn't exposing every token of reasoning but understanding how the workflow moved from one state to the next.
Action layer
Workflows ultimately produce outcomes. An agent might open a ticket, notify a team, enrich data, modify a workflow, or trigger automation. Once agents can act, action telemetry becomes essential operational data because mistakes are often discovered only after systems have changed.
A simple example
Imagine an agent responding to a Kubernetes latency alert. It queries deployment history, checks recent configuration changes, retrieves similar incidents from a vector store, and calls an internal health-check API before recommending a restart.
The workflow might look like this:
Alert received from a monitoring platform
Retrieval of related incidents
Query against deployment history
Dependency-health check failure
Recommendation generated
Human approval requested
Restart action executed
If the workflow later contributes to an outage, operators can reconstruct exactly what happened and why. Without workflow telemetry, they see only the recommendation and are left guessing about everything that came before it.
That's the difference between debugging a workflow and investigating fragments.
Where Cribl fits
Agent workflows generate telemetry across a growing number of systems, and the challenge isn't simply collecting that data, it's making it useful.
This is where Cribl's telemetry pipeline architecture becomes valuable.
Organizations can use Cribl to collect workflow signals, normalize data from different systems, govern sensitive information, route telemetry efficiently, and correlate prompts, tool calls, approvals, and actions into a single operational view.
Instead of treating AI telemetry as a separate category, teams can manage it like any other critical operational dataset. That means they can:
Correlate workflow activity across systems
Reduce noise and unnecessary data volume
Apply redaction and governance controls
Retain workflow telemetry cost-effectively
Investigate incidents without manually assembling evidence from multiple tools
The result is visibility into the complete workflow rather than isolated events.
What good looks like
Mature organizations evaluate more than model accuracy, asking whether workflows are observable, explainable, governed, and reconstructable.
In practice, that means:
Every workflow has a traceable origin
Every tool invocation is visible
Approval boundaries are documented
Actions are linked to the decisions that produced them
Sensitive information is governed appropriately
Investigations can be performed without stitching together evidence by hand
Trust doesn't come from assuming agents are correct. It comes from making their workflows visible enough for humans to validate, improve, and govern over time.
The real shift
The future of AI observability isn't about watching what models say; it's about understanding what workflows do. That's the difference between experimentation and operations.
Demos celebrate answers.
Operations own outcomes.
As AI systems move from generating content to executing tasks, workflow telemetry becomes critical infrastructure.
The most important question is no longer:
"Can we log the prompt?"
It's:
"Can we see the path from prompt to action clearly enough to trust it?"
If the answer is no, then you don't have AI observability yet.
You have fragments.
And fragments are exactly what fail you when you need the full story most.
