You can see the car crash frame by frame. You just can’t grab the wheel.
The AI observability market is booming.
Dozens of startups will sell you dashboards to monitor your LLM applications. Token counts. Latency distributions. Cost breakdowns. Prompt traces. Response quality scores.
You can watch everything your AI does, in beautiful real-time visualizations, with alerting and anomaly detection and historical trending.
And when something goes wrong, you can watch it go wrong. In high resolution. With timestamps.
What you can’t do is stop it.
The Observability-Controllability Gap
Traditional software observability makes sense. You observe, you understand, then you act. The action part is straightforward - roll back a deployment, scale up capacity, fix a bug in code.
AI systems break this model.
flowchart LR
subgraph "Traditional Software"
O1[Observe Problem] --> U1[Understand Cause]
U1 --> A1[Act on Code/Config]
A1 --> F1[Fix Deployed]
end
subgraph "AI Systems"
O2[Observe Problem] --> U2[Understand... Maybe?]
U2 --> A2[Act on... What?]
A2 -->|"???"| F2[Fix Deployed?]
end
style A2 fill:#fee2e2
style F2 fill:#fee2e2
When your LLM misbehaves, what’s your remediation path?
- Change the prompt? Takes time to iterate, test, and deploy. Meanwhile, the problem continues.
- Retrain the model? Even longer. And you probably can’t anyway if it’s a third-party model.
- Add a filter? Reactive and brittle. The next failure will be slightly different.
- Roll back? To what? The model itself didn’t change - the inputs did.
The observability gives you visibility into a system you can’t actually control in real-time.
What Your Observability Dashboard Tells You
Let’s be concrete. Here’s what a typical AI observability setup provides:
| Metric | What It Tells You | What You Can Do |
|---|---|---|
| Latency spike | Requests are slow | Wait, or pay for faster tier |
| Cost increase | Spending more money | Spend less (but how?) |
| Quality score drop | Outputs getting worse | Investigate (not fix) |
| Hallucination detected | Model made something up | The user already saw it |
| Toxicity alert | Model said something bad | The user already saw it |
| Drift warning | Behavior is changing | Watch it continue |
Notice the pattern? Observability tells you what happened. It doesn’t let you change what happens.
By the time you see the hallucination alert, the user has already received the hallucinated response. By the time you notice the drift, the model has already been drifting for days or weeks. By the time you see the cost spike, you’ve already spent the money.
Observability without controllability is a very expensive rear-view mirror.
The Real Problem: No Control Plane
Traditional infrastructure has control planes. Kubernetes doesn’t just observe pods - it creates, destroys, and reconfigures them. Load balancers don’t just observe traffic - they route it. Circuit breakers don’t just observe failures - they stop cascades.
AI systems, as typically deployed, lack this control layer.
flowchart TB
subgraph "Traditional Infrastructure"
direction LR
CP1[Control Plane] -->|"Create/Destroy/Configure"| WL1[Workloads]
MON1[Monitoring] -->|"Feeds into"| CP1
end
subgraph "Typical AI Stack"
direction LR
MON2[Observability] -->|"Reports to"| DASH[Dashboard]
DASH -->|"Human reads"| HUMAN[Human]
HUMAN -->|"Manually tweaks"| APP[AI Application]
end
subgraph "What's Missing"
direction LR
MON3[Observability] -->|"Triggers"| CP3[AI Control Plane]
CP3 -->|"Adjusts in real-time"| AI[AI Behavior]
end
style CP3 fill:#dcfce7
style DASH fill:#fee2e2
style HUMAN fill:#fee2e2
The gap isn’t visibility. It’s the ability to act on what you see, in real-time, automatically.
What Controllability Actually Looks Like
If observability is watching, controllability is intervening. Here’s what a real AI control plane enables:
Runtime Behavior Adjustment
When you detect a problem, you should be able to adjust model behavior immediately - without retraining, without redeploying, without waiting for a human to rewrite prompts.
Steering vectors make this possible. You can shift model outputs along interpretable dimensions (more formal, less verbose, more cautious) at inference time.
flowchart LR
subgraph "Without Controllability"
I1[Input] --> M1[Model]
M1 --> O1[Problematic Output]
O1 --> U1[User Sees Problem]
U1 -.->|"Hours/days later"| P1[Prompt Revision]
P1 -.->|"Deploy"| M1
end
subgraph "With Controllability"
I2[Input] --> M2[Model]
M2 --> SV[Steering Vector]
SV --> O2[Adjusted Output]
O2 --> U2[User Sees Corrected]
end
style O1 fill:#fee2e2
style U1 fill:#fee2e2
style SV fill:#dcfce7
style O2 fill:#dcfce7
Automatic Fallbacks
When quality degrades below a threshold, the system should automatically route to a fallback - a different model, a cached response, a human escalation - without manual intervention.
Dynamic Guardrails
Guardrails shouldn’t be static filters bolted on after the fact. They should be dynamic policies that adjust based on context, user trust level, and observed behavior.
Rate-Responsive Routing
When costs spike, the system should automatically route lower-priority requests to cheaper models. When latency matters, it should route to faster options. These decisions should happen per-request, not per-deployment.
The Vendor Landscape Problem
The AI observability market has exploded because observability is easy to build and easy to sell.
- It doesn’t require deep model integration
- It works with any LLM provider
- It provides immediate, visible value (pretty dashboards)
- It doesn’t require customers to change their architecture
Controllability is harder.
- It requires integration at the inference layer
- It requires new abstractions (steering vectors, control planes)
- It requires customers to think differently about AI architecture
- The value is harder to demo (preventing problems is invisible)
So the market has optimized for observability vendors, leaving a controllability gap.
This is a market gap, not a feature. You shouldn’t have to buy observability and then figure out controllability separately.
The Synthesis: Observe to Control
Observability isn’t useless. It’s incomplete.
The right architecture uses observability as input to a control plane. You observe to understand. You understand to act. The action happens automatically, in real-time, before users see problems.
flowchart TB
subgraph "Observation Layer"
L[Logging] --> A[Aggregation]
T[Tracing] --> A
M[Metrics] --> A
end
subgraph "Analysis Layer"
A --> AD[Anomaly Detection]
A --> QS[Quality Scoring]
A --> DR[Drift Recognition]
end
subgraph "Control Layer"
AD --> CP[Control Plane]
QS --> CP
DR --> CP
CP --> SV[Steering Vectors]
CP --> RT[Routing Decisions]
CP --> GR[Guardrail Adjustment]
CP --> FB[Fallback Triggers]
end
subgraph "Execution Layer"
SV --> INF[Inference]
RT --> INF
GR --> INF
FB --> INF
end
style CP fill:#dbeafe
style SV fill:#dcfce7
style RT fill:#dcfce7
style GR fill:#dcfce7
style FB fill:#dcfce7
This is closed-loop control. Observation feeds analysis. Analysis feeds decisions. Decisions feed action. Action happens before the user is affected.
What We Built
At Rotascale, we were frustrated by the observability-only landscape. Guardian provides monitoring and anomaly detection - that’s table stakes. But it’s integrated with Steer for runtime control and with our routing layer for automatic fallbacks.
When Guardian detects drift, it doesn’t just alert you. It can automatically apply steering vectors to compensate, route to a more stable model, or escalate to human review - depending on policies you define.
That’s not observability. That’s control.
The Bottom Line
If your AI operations strategy is “deploy observability and react to dashboards,” you’re building an expensive notification system.
The question isn’t “can I see what my AI is doing?” The question is “can I change what my AI is doing, in real-time, before users are affected?”
Observability without controllability is voyeurism. You’re watching the system fail, in high definition, with no ability to intervene.
Build the control plane. Then the observability becomes useful.
Observation without control is voyeurism. Build systems that can act on what they see.
Ready for controllability, not just observability? Guardian + Steer provide closed-loop AI control - detect problems and fix them automatically. See how it works.