Healthcare AI // For Compliance and Clinical Leaders
Galileo and CogniSwitch:
see what your AI did,
and prove it was allowed
CogniSwitch and Galileo are complementary, not competing. Galileo sees what your agents do across the whole population and scores it. CogniSwitch proves the one decision under question was allowed.
No, CogniSwitch is not a Galileo alternative. They operate at different layers of the stack. Galileo evaluates how your agents behave in production and constrains them on model scores. 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 Galileo 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
Galileo, 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
Galileo 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 Galileo do on its own?
Galileo is a strong, mature AI observability and eval engineering platform. It evaluates, monitors, and protects GenAI applications and agents at scale. 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 tracing and logging across Python, TypeScript, and REST, 20+ out-of-box evals for RAG, agents, safety, and security, and Luna evaluation models that distill expensive LLM-judge evaluators into compact models that can monitor live traffic at low cost. An insights engine clusters agent failures to surface root-cause patterns, and its runtime layer can block, redact, override, or call a webhook when an output crosses a configured ruleset. For seeing what happened, scoring how good it was, and constraining behavior on those scores, Galileo does its job well.
What evaluation and observability can't do on their own
Evaluation and observability platforms, Galileo included, score output using model-based metrics: Galileo's own docs note that out-of-box and LLM-as-a-judge metrics use an LLM to evaluate inputs and outputs, and its runtime guardrails are gated on Luna evaluation-model scores. 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
Galileo engineers hard against this with Luna, but a model in the scoring path still carries cost, so thresholds and sampling, not a check of every regulated decision, are the norm.
Scored, not rule-named
The runtime layer can block or redact when a score crosses a threshold, but it does not name the specific rule that drove a regulated decision in a verdict an auditor can re-derive.
So with only Galileo you can see, score, and constrain behavior on those scores. What you cannot do is prove that one specific decision was correct and policy-compliant, with a reason you can repeat and a named rule an auditor can re-derive. That takes a deterministic verification step. Better evaluation alone does not get you there.
In practice // A health system in production
How do Galileo 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, Galileo and CogniSwitch do different jobs in the same flow. Galileo 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.
Galileo observes the behavior
Galileo 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
Galileo 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.
Galileo observes the behavior
Galileo 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
Galileo 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
Galileo 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 Galileo?
What changes when you add CogniSwitch to the evaluation and 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 four rows are Galileo doing its job well. The rest is what the verification layer adds.
| What you can do | With only Galileo | With Galileo + 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 |
| Block or redact an output when its eval score crosses a threshold | 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 |
| Defend a decision to an auditor or regulator | No | Yes |
| Deploy AI agents in a regulated setting with confidence | 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 Galileo?
No. They operate at different layers of the AI stack. Galileo is evaluation and observability: it scores and monitors what your agents produce, and powers model-based guardrails. CogniSwitch is the trust layer: it verifies and enforces decisions deterministically. Most regulated teams run both, not one instead of the other.
Q2What does Galileo do that CogniSwitch does not?
Galileo gives you production observability and evaluation: tracing and logging across SDKs, 20+ out-of-box evals for RAG, agents, safety, and security, Luna evaluation models that monitor live traffic, and an insights engine that clusters failure patterns. CogniSwitch does not replace that signal. It adds a verification layer on top of it.
Q3What does CogniSwitch add to a Galileo stack?
Deterministic verification. Galileo tells you whether an agent is performing well on average and can block or redact outputs whose eval scores cross a configured threshold. 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 Galileo's evaluation enough for a regulated decision?
Galileo's metrics are model-based: its own docs note that out-of-box and LLM-as-a-judge metrics use an LLM to evaluate inputs and outputs, and its runtime guardrails are gated on Luna evaluation-model scores. That is the right tool for monitoring quality trends and constraining behavior. A regulator needs a reproducible verdict on the specific decision, named to the rule that applied, which is what deterministic verification provides.
Q5Do Galileo and CogniSwitch run together?
Yes, as complementary layers in the same stack, each doing its job. Galileo observes, evaluates, and constrains behavior on model scores; CogniSwitch verifies and enforces a regulated decision deterministically. You keep your observability and add the ability to prove and defend each decision.
Q6We already use Galileo. What changes if we add CogniSwitch?
Your observability and eval stack stays exactly as it is. What you gain is proof and rule-named enforcement: every regulated decision is checked against your SOPs and source data at runtime, deterministically, producing an audit trail. Galileo keeps answering 'is the agent performing well?'; CogniSwitch answers 'can we prove this decision was allowed?'
Get your agents into production.
Keep your evaluation and 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.
Keep reading
CogniSwitch also completes Arize + CogniSwitch, Braintrust + CogniSwitch, and Cekura + CogniSwitch.
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