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Scalefresh AI

Scalefresh AI

Build Workflows Your Evaluation System Monitors

We engineer multi-agent systems for healthcare's highest-volume administrative and clinical workflows, with evaluation built in from the architecture level so every agent decision is traced, every tool call is scored, and every human override feeds back into the system.

Talk to us about your workflows

Talk to us about your workflows

Why Evaluation Changes How Agents Are Built

Agents without evaluation are automation you cannot diagnose

Most agentic AI systems in healthcare are built to automate a workflow and then monitored at the output level: did the prior auth get approved, did the document get generated, did the referral get routed. That level of visibility tells you whether the workflow completed. It does not tell you whether the agent made the right decisions along the way, or why it made the wrong ones when it failed.

We build agents differently. Every agent decision is captured as a structured trace with full context: what the agent received, what it decided, what tools it called, what each tool returned, and how the final output was assembled. That trace feeds directly into the evaluation system, where it is scored against ground truth using the same failure taxonomy, rubrics, and evaluators that measure every other AI system in your environment.


The result is agents you can actually diagnose and improve, not just deploy and hope.

Most agentic AI systems in healthcare are built to automate a workflow and then monitored at the output level: did the prior auth get approved, did the document get generated, did the referral get routed. That level of visibility tells you whether the workflow completed. It does not tell you whether the agent made the right decisions along the way, or why it made the wrong ones when it failed.


We build agents differently. Every agent decision is captured as a structured trace with full context: what the agent received, what it decided, what tools it called, what each tool returned, and how the final output was assembled. That trace feeds directly into the evaluation system, where it is scored against ground truth using the same failure taxonomy, rubrics, and evaluators that measure every other AI system in your environment.


The result is agents you can actually diagnose and improve, not just deploy and hope.

What We Build

Multi-agent systems engineered for traceability

It's not the algorithms.

It's the data.

Capability 1: Orchestration

Multi-agent architecture with specialized roles


Each workflow is decomposed into discrete steps handled by specialized agents. A prior authorization workflow might involve a clinical evidence extraction agent, a payer requirement matching agent, a form completion agent, and a submission tracking agent, each operating on its own context with defined handoff protocols between them. This specialization makes each agent's scope narrow enough to evaluate rigorously and its failures specific enough to diagnose precisely.

Capability 2: Traceability

Agent-level decision tracing


Every agent decision is captured as a structured trace showing which agent acted, at which step, with what context, what tools it called, what each tool returned, and how it arrived at its output. When a workflow produces a wrong result, the trace shows whether the failure originated within a specific agent's reasoning, in the data it received from a sibling agent, or in the tool call that returned unexpected results. That diagnostic specificity is what makes agent failures fixable rather than just detectable.

Capability 3: Tool integration

Tool-calling agents that work within your existing systems


Agents connect to your existing infrastructure rather than requiring system replacement. EHR platforms through FHIR APIs, payer systems for real-time eligibility verification, fax protocols for provider communications, documentation templates, coding engines, and scheduling systems. Every tool call is traced and scored, so when a tool returns unexpected results or fails silently, the evaluation system catches it rather than letting the error propagate through the rest of the workflow.

Capability 4: Human oversight

Human-in-the-loop design that improves the system over time


Agents handle the structured, repeatable portion of each workflow autonomously and escalate to human reviewers when confidence drops below defined thresholds or when clinical judgment is genuinely required. The critical difference from conventional HITL design is what happens with the human's decision. Every override is captured, categorized, and fed back into the golden dataset as a new labeled example, creating a feedback loop where the clinical staff who use the system are continuously improving the evaluation infrastructure that measures it.

Capability 5: Scoring

Tool call evaluation and agent decision scoring


Agent outputs are not just checked at the workflow level. Individual tool calls are scored for correctness, agent reasoning steps are evaluated against domain-specific rubrics, and the orchestration logic routing work between agents is traced and auditable. When parallel agents operate on overlapping context, the trace tree shows how each agent's output influenced the others and whether the final result was consistent with what each agent individually concluded.

Capability 1: Orchestration

Multi-agent architecture with specialized roles


Each workflow is decomposed into discrete steps handled by specialized agents. A prior authorization workflow might involve a clinical evidence extraction agent, a payer requirement matching agent, a form completion agent, and a submission tracking agent, each operating on its own context with defined handoff protocols between them. This specialization makes each agent's scope narrow enough to evaluate rigorously and its failures specific enough to diagnose precisely.

Capability 2: Traceability

Agent-level decision tracing


Every agent decision is captured as a structured trace showing which agent acted, at which step, with what context, what tools it called, what each tool returned, and how it arrived at its output. When a workflow produces a wrong result, the trace shows whether the failure originated within a specific agent's reasoning, in the data it received from a sibling agent, or in the tool call that returned unexpected results. That diagnostic specificity is what makes agent failures fixable rather than just detectable.

Capability 3: Tool integration

Tool-calling agents that work within your existing systems


Agents connect to your existing infrastructure rather than requiring system replacement. EHR platforms through FHIR APIs, payer systems for real-time eligibility verification, fax protocols for provider communications, documentation templates, coding engines, and scheduling systems. Every tool call is traced and scored, so when a tool returns unexpected results or fails silently, the evaluation system catches it rather than letting the error propagate through the rest of the workflow.

Capability 4: Human oversight

Human-in-the-loop design that improves the system over time


Agents handle the structured, repeatable portion of each workflow autonomously and escalate to human reviewers when confidence drops below defined thresholds or when clinical judgment is genuinely required. The critical difference from conventional HITL design is what happens with the human's decision. Every override is captured, categorized, and fed back into the golden dataset as a new labeled example, creating a feedback loop where the clinical staff who use the system are continuously improving the evaluation infrastructure that measures it.

Capability 5: Scoring

Tool call evaluation and agent decision scoring


Agent outputs are not just checked at the workflow level. Individual tool calls are scored for correctness, agent reasoning steps are evaluated against domain-specific rubrics, and the orchestration logic routing work between agents is traced and auditable. When parallel agents operate on overlapping context, the trace tree shows how each agent's output influenced the others and whether the final result was consistent with what each agent individually concluded.

Where This Works Today

Where This Works Today

Where This Works Today

Healthcare workflows where agentic AI delivers measurable impact

01.

Prior Authorization End-to-End

Agent extracts relevant clinical evidence from the EHR, matches it against payer-specific requirements, completes authorization forms, submits electronically, and tracks approval status. Escalates to human review when clinical judgment is needed or when the case falls outside established patterns. Every step traced and scored, with denial patterns analyzed through the failure taxonomy to identify whether denials stem from insufficient clinical evidence, incorrect payer matching, or documentation gaps in the source data.



01.

Prior Authorization

Agent extracts relevant clinical evidence from the EHR, matches it against payer-specific requirements, completes authorization forms, submits electronically, and tracks approval status. Escalates to human review when clinical judgment is needed or when the case falls outside established patterns. Every step traced and scored, with denial patterns analyzed through the failure taxonomy to identify whether denials stem from insufficient clinical evidence, incorrect payer matching, or documentation gaps in the source data.



01.

Prior Authorization

Agent extracts relevant clinical evidence from the EHR, matches it against payer-specific requirements, completes authorization forms, submits electronically, and tracks approval status. Escalates to human review when clinical judgment is needed or when the case falls outside established patterns. Every step traced and scored, with denial patterns analyzed through the failure taxonomy to identify whether denials stem from insufficient clinical evidence, incorrect payer matching, or documentation gaps in the source data.



02.

Medical coding

Agent reviews clinical documentation, applies coding logic for OASIS, HCC, E/M, or other code sets, and generates coded outputs with evidence linkage showing which specific documentation supports each code. Domain-specific rubrics evaluate whether the coding logic was applied correctly at the item level, and the failure taxonomy routes coding errors to the right upstream fix, whether that is a retrieval issue, a documentation gap, or a reasoning error in the coding logic itself.



03.

Clinical Documentation

Agent generates progress notes, discharge summaries, or visit documentation from clinical conversations and source records, with every claim in the generated document traceable to the evidence that supports it. Evaluation checks factual consistency against source records, completeness of required elements, and hallucination rate, ensuring that generated documentation meets the standard a clinician would produce and flagging anything that does not for human review.



04.

Claims denial management

Agent reviews denied claims, identifies denial reason codes, matches against clinical documentation to assess appeal viability, drafts appeal letters with supporting evidence, and tracks appeal outcomes. Denial patterns are analyzed across the portfolio to identify systematic issues, whether that is a specific payer consistently denying a particular procedure code, a documentation pattern that triggers denials, or a coding error that the evaluation system's failure taxonomy can route to the right fix.



How We Build This

woman signing on white printer paper beside woman about to touch the documents
woman signing on white printer paper beside woman about to touch the documents

01

Discovery and architecture (Weeks 1-3)

We map your current workflow before designing the agents that will handle it.

What happens:

  • Workflow analysis of current processes and decision points

  • Identification of agent boundaries and specialization areas

  • Multi-agent system architecture with handoff protocols

  • Escalation criteria defining when agents defer to human reviewers

  • Evaluation instrumentation design and success metrics defined before building anything

What you get:

Clear architecture showing which agents handle which steps, where humans stay in the loop, and how every agent decision will be traced and scored.

Development and integration (Weeks 4-14)

Agents built iteratively with working systems delivered every 2-3 weeks.

What happens:

  • Agent development with EHR integration and payer API connections

  • Tool-calling infrastructure and clinical validation rules

  • Human review interfaces with override capture

  • Evaluation instrumentation wired in from the first agent deployed

  • Continuous testing against golden dataset throughout development

Delivered incrementally:

Working agents integrated with your systems, handling real workflow logic, with full trace-level observability and evaluation scoring from day one.

02

person using macbook pro on table
person using macbook pro on table
a white vase filled with lots of wrenches
a white vase filled with lots of wrenches

03

Deployment and eval-gated release (Weeks 15-18)

Agents move to production only when evaluation results confirm they are ready.

What happens:

  • Pilot deployment in a controlled environment with full evaluation scoring

  • Agent performance measured at the decision level, not just workflow output

  • Failure taxonomy analysis across all agent decisions during pilot

  • HITL override patterns captured and analyzed

  • Expansion to full production based on eval thresholds being met

What you get:

Agents handling real workload in production, with evaluation gating every expansion to new case types and continuous governance running from that point forward.

Technical Stack

Built on the orchestration and integration platforms healthcare requires

Built for the platforms

healthcare runs on

Orchestration:

LangGraph for complex multi-agent workflows with branching logic and parallel execution

Integration:

Custom MCP servers for payer systems, fax protocols, and legacy platforms

Cloud:

Azure AI Foundry, Microsoft healthcare AI tooling

EHR connectivity:

Epic, Cerner, athenahealth through FHIR APIs

Eval and observability:

Langfuse, Braintrust, LangSmith for trace capture and experiment management

Orchestration:

LangGraph for complex multi-agent workflows with branching logic and parallel execution

Integration:

Custom MCP servers for payer systems, fax protocols, and legacy platforms

Cloud:

Azure AI Foundry, Microsoft healthcare AI tooling

EHR connectivity:

Epic, Cerner, athenahealth through FHIR APIs

Eval and observability:

Langfuse, Braintrust, LangSmith for trace capture and experiment management

Common Questions

Common
Questions

How is this different from RPA or workflow automation?

What percentage of cases do agents handle autonomously?

Do agents replace clinical or administrative staff?

How long before agents are handling real workload?

Can we start with one workflow and expand?