Healthcare AI // For Compliance and Clinical Leaders
Arize and CogniSwitch:
see what your AI did,
and prove it was allowed
CogniSwitch and Arize are complementary, not competing. Arize sees what your agents do across the whole population. CogniSwitch proves the one decision under question was allowed.
No, CogniSwitch is not an Arize alternative. They operate at different layers of the stack. Arize watches how your agents behave in production. CogniSwitch proves a specific decision followed the rules. In healthcare you need both: it's an and, not an or.
Why this matters
Why run Arize and CogniSwitch together?
Most health systems have AI agents stuck in pilots. The blocker is rarely the model. It's that no one can prove, to a regulator or an internal audit, that a given decision was correct and allowed.
The problem it solves
You can move agents out of pilot and into production, because you can now stand behind every decision they make.
The outcome
When an auditor or regulator asks why the agent did what it did, you can pull the exact rule and the record, and prove it. The decision is defensible.
The stack
What does a healthcare AI stack need to be complete?
A complete regulated AI stack has four layers, and CogniSwitch is the trust layer between the agents and the clinical data.
Evals & Observability
Watch the agents, score their output, catch problems
Arize, Braintrust, Langfuse
Agents & AI Applications
The agents themselves: prior-auth, clinical notes, intake
LangChain, CrewAI, OpenAI, Anthropic
Trust Layer
Check each decision against policy at runtime, and give an auditor a reason they can re-derive.
CogniSwitch
Clinical Data
The source of truth the agent has to match
EHR, payer policies, clinical SOPs
Arize answers
"Are the agents behaving well?"
CogniSwitch answers
"Can we prove this decision was allowed?"
CogniSwitch sits above your evaluation platform and completes the stack.
What does Arize do on its own?
Arize is a strong, mature observability and evaluation platform. It traces what your agents do, scores how good the output is, and watches for trouble over time. The engineering is solid and the signal is real.
It ships OpenTelemetry-based tracing, a full evaluation toolkit, and online evals that score live production traffic. Its open-source companion, Phoenix, is available under the Elastic License 2.0, so teams that need to self-host can. It surfaces drift and performance monitoring, so you can see when a model starts to slip. For seeing what happened and scoring how good it was, Arize does its job well.
What evaluation and observability can't do on their own
Evaluation and observability platforms, Arize included, score output using an LLM-as-a-judge: one language model grading another model's work. That is a useful instrument, and it is not what the approach is built for: proving that one specific decision was correct and allowed. Four things follow from putting a model in the scoring path.
A model grades a model
When one model scores another, they can share the same blind spots. The grader is no more independent than the agent it is grading.
The score can move
Run the same output twice and the score can change. A verdict that moves is not one an auditor can re-derive and trust.
Too costly to check everything
Each judge call costs real money, so teams check a sample of traffic, not every decision. The one that matters may go unchecked.
Too slow to stop a bad call
A judge call takes seconds, so it runs after the fact. It cannot block a non-compliant decision before it reaches a patient or payer.
So with only Arize you can see and score. What you cannot do is prove that one specific decision was correct and policy-compliant, with a reason you can repeat. That takes a verification step. Better tracing alone does not get you there.
In practice // A health system in production
How do Arize and CogniSwitch work together in healthcare?
Consider a health system running two AI agents in production: a prior-authorization agent and a clinical documentation agent. For each one, Arize and CogniSwitch do different jobs in the same flow. Arize watches the behavior across thousands of cases. CogniSwitch proves each individual decision. Here is how that plays out.
Prior-authorization agent
The scenario
The agent reviews a request and decides whether a procedure is approved or denied under the patient's plan.
Arize observes the behavior
Arize traces every request the agent handles, scores its accuracy, watches for drift, and alerts the team when quality starts to slip across the population of cases.
Answers
"Is the prior-auth agent behaving well overall?"
CogniSwitch proves the decision
CogniSwitch checks each individual denial against the exact payer-policy version that applied at that moment, produces a yes-or-no verdict that names the rule that fired, and keeps the record. The same input gives the same verdict every time.
Answers
"Can we prove this specific denial followed the policy?"
Together, in one flow
Arize tells you the agent is denying at a healthy rate. CogniSwitch lets you pull up one disputed denial and show the regulator the precise rule it applied and why.
Clinical documentation agent (discharge summaries)
The scenario
The agent drafts a discharge summary from the encounter: medications, problems, and the care plan.
Arize observes the behavior
Arize monitors summary quality and eval scores such as completeness and hallucination across all summaries, and tracks how those scores move over time.
Answers
"Are the summaries generally good?"
CogniSwitch proves the decision
CogniSwitch checks each summary against the EHR itself: every medication, problem, and plan item is matched to the structured record, and any item that does not match is flagged before the summary is finalized.
Answers
"Can we prove this summary matches the patient's record?"
Together, in one flow
Arize tells you summary quality is holding steady. CogniSwitch catches the one summary that lists a medication the patient was never on, before it reaches the chart.
Both layers, together
Arize sees what the agent did across the whole population. CogniSwitch proves the one decision under question. Together, the agent is safe to run in production and defensible to an auditor.
What changes when you add CogniSwitch to Arize?
What changes when you add CogniSwitch to the observability you already run. The rows build from seeing the agents to being able to deploy them in a regulated setting with confidence.
Yes = the stack can do this
The first three rows are Arize doing its job well. The rest is what the verification layer adds.
| What you can do | With only Arize | With Arize + CogniSwitch |
|---|---|---|
| See what the agents do, request by request | Yes | Yes |
| Score output quality and catch hallucinations | Yes | Yes |
| Catch quality drift across the population over time | Yes | Yes |
| Get the same verdict on a decision every time you check | No | Yes |
| Name the exact policy rule that drove a decision | No | Yes |
| Reconstruct and prove one specific decision after the fact | No | Yes |
| Block a non-compliant decision before it ships | No | Yes |
| Deploy AI agents in a regulated setting with confidence | No | Yes |
| Defend a decision to an auditor or regulator | No | Yes |
FAQ
Common questions from teams that already run an observability stack and are deciding where the trust layer fits.
Q1Is CogniSwitch an alternative to Arize?
No. They operate at different layers of the AI stack. Arize is evaluation and observability: it scores and monitors what your agents produce. CogniSwitch is the trust layer: it verifies and enforces decisions deterministically. Most regulated teams run both, not one instead of the other.
Q2What does Arize do that CogniSwitch does not?
Arize gives you production observability and evaluation: OpenTelemetry-based tracing, eval scores, and drift and performance monitoring across your agents. CogniSwitch does not replace that signal. It adds a verification layer on top of it.
Q3What does CogniSwitch add to an Arize stack?
Deterministic verification. Arize tells you whether an agent is performing well on average. CogniSwitch proves whether a specific decision was correct and policy-compliant, with a reproducible, rule-named verdict and an audit trail you can hand an auditor.
Q4Why isn't Arize's evaluation enough for a regulated decision?
Arize evaluation uses an LLM-as-a-judge, which is probabilistic: the same output can score differently on re-run, and cost pushes teams to sample rather than check everything. That is the right tool for monitoring quality trends. A regulator needs a reproducible verdict on the specific decision, which is what deterministic verification provides.
Q5Do Arize and CogniSwitch run together?
Yes, as complementary layers in the same stack, each doing its job. Arize observes and evaluates; CogniSwitch verifies and enforces. You keep your observability and add the ability to prove and enforce regulated decisions.
Q6We already use Arize. What changes if we add CogniSwitch?
Your observability stays exactly as it is. What you gain is proof and enforcement: every regulated decision is checked against your SOPs and source data at runtime, deterministically, producing an audit trail. Arize keeps answering 'is the agent performing well?'; CogniSwitch answers 'can we prove this decision was compliant?'
Get your agents into production.
Keep your observability. Add the layer that proves a decision followed the rules and blocks the one that does not, before it reaches a patient or payer. It runs on a context graph, not another model in the scoring path.
Author
Joshua Thomas
Co-Founder & CTO, CogniSwitch
Reading Time
~9 min read
References
- 1.Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena — Zheng et al., NeurIPS 2023
- 2.Large Language Models are not Fair Evaluators — Wang et al., ACL 2024
- 3.A Survey on LLM-as-a-Judge — Gu, Jiang et al., 2024-2025
- 4.Evaluating large language models for drafting emergency department encounter summaries — PLOS Digital Health, 2025
- 5.A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation — npj Digital Medicine, 2025