Observability Platform for AI
Building Systems That Watch AI Systems
In my last article, I explored the idea of a Permitting Agentic Workflow — a coordinated system of AI agents that could manage complex infrastructure permitting from site control to approval. The prototype imagined how such a workflow might actually function across real agencies and data sources.
What I didn’t explore was the harder part: how to design trust into such a system.
Permitting isn’t forgiving. A single error in a zoning clause or an outdated statute reference can delay a project for months or cause expensive penalties. When agents begin performing those tasks autonomously, the question shifts from can they do it to can we prove they did it right.
That’s where observability comes in.
What Observability Means in the Age of AI
In traditional software, observability helps engineers monitor system health: latency, throughput, and error rates. It tells you whether the code is running per design.
In AI systems, observability must answer why a system behaved the way it did. It must explain the thought process.
Did a model cite the right source?
Was a computer vision model consistent under poor lighting?
Did the forecasting model drift after new data arrived?
AI observability is the design discipline that connects inputs, reasoning, and outputs into an explainable chain of evidence. It ensures that the outcomes accomplished were decided on for the right reasons
Why This Has Become a Product Problem
When AI systems handle low-risk tasks like summarizing notes or classifying photos, we care mostly about convenience and speed. But once they handle decisions tied to money, safety, or regulation, the focus shifts to accuracy & audit-ability.
A single wrong output can carry real-world consequences — a rejected permit, a safety inspection failure, significant fines or a missed compliance filing. In that context, observability is no longer an engineering tool. It becomes table stakes. It’s how users, auditors, and regulators learn to trust what’s inside the box.
The problem with explaining AI models is essentially they are complicated black boxes. We can only observe inputs and outputs. We have to make reasonable and educated guesses based on mathematical models that can give us some confidence in the consistency of the thought process.
Architecture of a Monitoring Platform for AI
A modern observability platform for AI systems has three core layers.
Test Plane
Applies a testing framework using real world use cases to ensure it can understand what inputs generate which outputs.
Tells us how well a model is suited for a specific use case.
Data Plane
Collects everything — model inputs, prompts, outputs, intermediate activations, and human feedback.
Every trace is timestamped, versioned, and stored in a way that can be replayed later.Control Plane
Applies rules and policies to those traces.
It checks whether the model used approved data sources, met confidence thresholds, or stayed within regulatory limits.Experience Plane
Translates that information into human understanding.
This is where dashboards, alerts, and evidence trails live.
A user can open a timeline, see each model’s decisions, and trace them back to their sources.
When these layers are connected, the platform does more than monitor performance — it monitors behavior.
What the Platform Observes
The observability system keeps a continuous watch on several dimensions of AI behavior:
Accuracy and grounding: Are the outputs correct and linked to verifiable evidence?
Model drift: Has performance changed since deployment?
Data lineage: Which sources influenced each decision?
Policy adherence: Were confidence and compliance thresholds respected?
Human oversight: Did required approvals occur before critical actions?
Each event is captured as a structured record.
When an auditor or user asks why did the system decide this?, the platform can reconstruct the answer in full.
Evaluating the Observability AI
Ironically, the platform that observes others also uses AI internally. It employs its own models to detect anomalies, summarize traces, and highlight probable root causes.
These models don’t have to perform the regulated work butthey evaluate and interpret it.
Their performance is measured on different axes:
Detection accuracy: How well they identify real failures versus false alarms.
Correlation precision: Whether related issues are grouped correctly.
Summarization fidelity: How accurately they describe what happened.
Explainability completeness: Whether each alert links to evidence.
In practice, these models act like an internal analyst. They help humans see patterns faster, while staying fully auditable themselves.
Building Guardrails & Reflexes
The most useful observability systems will in addition to observation and reporting will also react to the outputs. They will have reflexes that protect the workflow from cascading errors.
If confidence in a model output drops below a defined threshold, the system should pause automation and ask for human guidance.
If a required human review is skipped, the task is quarantined.
If the model begins pulling from outdated sources, the platform alerts both engineers and compliance teams.
Those reflexes turn monitoring into a form of control — the system keeps itself inside safe bounds.
Governance and Separation of Roles
Observability works only when accountability is clear. That means separating ownership of three layers:
Primary systems: the AI models doing the regulated or operational work.
Observability AI: the layer that monitors, evaluates, reports & enforces guardrails on them.
Governance and compliance teams: the human layer that sets policies and verifies alerts.
Each layer has visibility into the one below, but cannot silently modify it.
This separation ensures that the entire process remains trustworthy.
What Success Looks Like
A mature observability platform becomes the memory and conscience of an AI ecosystem. It can reconstruct any decision, show evidence behind every claim, and quantify reliability over time.
It surfaces early warnings when drift begins and provides proof when compliance questions arise.
When done well, this infrastructure turns accountability into a competitive advantage.
Customers get an intelligent system that can justify its intelligence.
The Next Adoption Driver is Oberservability
AI observability began as an engineering discipline. It’s now becoming a cornerstone of product design and governance. The systems that will endure and get adopted by organizations & users are those that can be explain.
The observability platform will play a key role in the long term adoption of Agentic AI. A full end to end system that can review, audit & help improve the AI
How have you thought about how to trust the outputs of an AI system?