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Scalefresh AI

Single-agent systems
break when workflows
get complex.
Multi-agent systems don't.

Multi-agent architectures divide complex workflows among specialized AI agents, each optimized for specific tasks, working together under intelligent orchestration.

Tell me more about this

Tell me more about this

Tell me more about this

What Are Multi-Agent Workflows?

Multi-agent workflows decompose complex tasks into specialized sub-agents, each focused on a specific capability, coordinated by a supervisor agent that plans the overall sequence.

Single-Agent System

One AI tries to handle everything: chart review, policy lookup, documentation generation, submission, tracking. It's a generalist attempting specialist work.

One AI tries to handle everything: chart review, policy lookup, documentation generation, submission, tracking. It's a generalist attempting specialist work.

One AI tries to handle everything: chart review, policy lookup, documentation generation, submission, tracking. It's a generalist attempting specialist work.

Multi-Agent System

  • Supervisor Agent → Plans workflow, coordinates specialists, handles exceptions

  • Patient Data Agent → Retrieves and synthesizes clinical information

  • Policy Agent → Looks up payer-specific requirements

  • Documentation Agent → Generates medical necessity letters

  • Submission Agent → Interfaces with payer portals

  • Monitoring Agent → Tracks status and responses

    Each specialist focuses on what it does well. Supervisor orchestrates handoffs.

  • Supervisor Agent → Plans workflow, coordinates specialists, handles exceptions

  • Patient Data Agent → Retrieves and synthesizes clinical information

  • Policy Agent → Looks up payer-specific requirements

  • Documentation Agent → Generates medical necessity letters

  • Submission Agent → Interfaces with payer portals

  • Monitoring Agent → Tracks status and responses

    Each specialist focuses on what it does well. Supervisor orchestrates handoffs.

  • Supervisor Agent → Plans workflow, coordinates specialists, handles exceptions

  • Patient Data Agent → Retrieves and synthesizes clinical information

  • Policy Agent → Looks up payer-specific requirements

  • Documentation Agent → Generates medical necessity letters

  • Submission Agent → Interfaces with payer portals

  • Monitoring Agent → Tracks status and responses

    Each specialist focuses on what it does well. Supervisor orchestrates handoffs.

The difference?

Reliability through specialization.

Why Healthcare Workflows Need
Multi-Agent Architecture

Why Healthcare Workflows Need
Multi-Agent Architecture

Healthcare workflows fail the "simple automation" test

on four counts:

Healthcare workflows fail the "simple automation" test on four counts:

1. They Require Multiple Specialized Capabilities

Prior authorization needs clinical reasoning (synthesizing patient history), policy interpretation (understanding payer requirements), medical writing (generating documentation), API integration (submitting to portals), and monitoring (tracking responses).


No single AI model excels at all five. Clinical reasoning requires medical knowledge. Policy interpretation requires legal/contractual understanding. API integration requires technical precision.

Solution: Build specialized agents. Train each on its specific domain. Coordinate them.

2. Different Steps Have Different Failure Modes

Chart review fails when data is incomplete. Policy lookup fails when payer criteria are ambiguous. Submission fails when portal APIs are down. Monitoring fails when payer responses are delayed.

When one massive agent fails, you can't diagnose which capability broke. When a specialized agent fails, you know exactly where: the Policy Agent couldn't find criteria, or the Submission Agent got an API error.

Solution: Isolated failure domains. When the Policy Agent fails, other agents continue. Supervisor routes exception to human review.

3. Requirements Change at Different Rates

Payer policies change quarterly. Documentation templates change annually. EHR APIs change when you upgrade systems. Portal authentication changes when payers update security.


With a single-agent system, any change requires retraining the entire model. With multi-agent architecture, you update the affected specialist.

Solution: Modularity. Update the Policy Agent when criteria change. Leave other agents untouched.

4. Different Steps Require Different Human Oversight

Clinical synthesis requires physician review. Documentation formatting can be staff-reviewed. Portal submission is automated after approval. Status monitoring is fully automated.


You can't apply one human-oversight pattern across all steps. You need granular control.

Solution: HITL checkpoints at the agent level. Documentation Agent output goes to human review. Submission Agent executes after approval. Monitoring Agent runs autonomously with exception alerts.

How Multi-Agent Orchestration Works

Architecture: Supervisor + Specialized Sub-Agents

The Supervisor Agent is the planner and coordinator. Sub-agents are the specialists.

Supervisor Responsibilities:

  • Receive workflow request

  • Plan execution sequence

  • Route tasks to appropriate sub-agents

  • Coordinate handoffs between specialists

  • Handle exceptions and edge cases

  • Escalate to humans when necessary

Sub-Agent Responsibilities:

  • Execute assigned task within defined scope

  • Return results or errors to supervisor

  • Operate independently without knowing full workflow

  • Maintain audit logs of actions taken

Supervisor Responsibilities:

  • Receive workflow request

  • Plan execution sequence

  • Route tasks to appropriate sub-agents

  • Coordinate handoffs between specialists

  • Handle exceptions and edge cases

  • Escalate to humans when necessary

Sub-Agent Responsibilities:

  • Execute assigned task within defined scope

  • Return results or errors to supervisor

  • Operate independently without knowing full workflow

  • Maintain audit logs of actions taken

Communication Pattern: Hub and Spoke

Supervisor sits at the hub. Sub-agents are spokes. Sub-agents don't talk directly to each other—they communicate through the supervisor.


Why? Control and visibility. Supervisor sees all handoffs. Audit trail is clear. Debugging is straightforward.

Example: Prior Authorization Workflow

A typical prior authorization process may include:

Intake Agent:

Receives request, validates completeness, routes by urgency

Clinical Synthesis Agent:

Retrieves and summarizes longitudinal patient data via RAG

Policy Agent:

Identifies applicable payer criteria and documentation requirements

Documentation Agent:

Generates medical necessity letter mapping clinical evidence to policy

Review Orchestrator:

Presents to clinical staff for HITL approval

Submission Agent:

Interfaces with payer portal API, tracks confirmation

Monitoring Agent:

Watches for payer response, routes denials to appeals sub-workflow

Intake Agent:

Receives request, validates completeness, routes by urgency

Clinical Synthesis Agent:

Retrieves and summarizes longitudinal patient data via RAG

Policy Agent:

Identifies applicable payer criteria and documentation requirements

Documentation Agent:

Generates medical necessity letter mapping clinical evidence to policy

Review Orchestrator:

Presents to clinical staff for HITL approval

Submission Agent:

Interfaces with payer portal API, tracks confirmation

Monitoring Agent:

Watches for payer response, routes denials to appeals sub-workflow

Each step is handled by a dedicated agent—coordinated, auditable, and clinically accountable.

Multi-Agent vs. Monolithic:
The Real Difference

Multi-Agent vs. Monolithic:
The Real Difference

Multi-Agent vs. Monolithic:
The Real Difference

When you're evaluating vendors, ask them: "What happens when a payer changes their prior authorization criteria format next quarter?" The answer reveals their architecture. Monolithic systems require weeks of retraining. Multi-agent systems update one component in days.

Here's what that difference looks like in practice:

Dimension

Vs.

Monolithic Single-Agent

Monolithic Single-Agent (MSA)

Multi-Agent System

Multi-Agent System (MA)

Architecture

One model handles all tasks

MSA - One model handles all tasks

Specialized models for specific tasks

MA - Specialized models for specific tasks

Failure Impact

Failure anywhere = failure everywhere

MSA - Failure anywhere = failure everywhere

Failure isolated to one agent

MA - Failure isolated to one agent

Updates

Retrain entire model

MSA - Retrain entire model

Update affected agent only

MA - Update affected agent only

Debugging

Hard to diagnose which capability failed

MSA - Hard to diagnose which capability failed

Clear diagnosis: which agent failed and why

MA - Clear diagnosis: which agent failed and why

Human Oversight

All-or-nothing

MSA - All-or-nothing

Granular oversight at agent level

MA - Granular oversight at agent level

Scalability

Scales poorly as complexity increases

MSA - Scales poorly as complexity increases

Scales through modularity

MA - Scales through modularity

FAQ: Implementation Considerations

How much does comprehensive evaluation cost compared to the AI system itself?

Can't our AI vendor handle evaluation?

When should we start evaluation?

What if we've already deployed AI without systematic evaluation?

How do we know if our evaluation is sufficient?

How much does comprehensive evaluation cost compared to the AI system itself?

Can't our AI vendor handle evaluation?

When should we start evaluation?

What if we've already deployed AI without systematic evaluation?

How do we know if our evaluation is sufficient?

How much does comprehensive evaluation cost compared to the AI system itself?

Can't our AI vendor handle evaluation?

When should we start evaluation?

What if we've already deployed AI without systematic evaluation?

How do we know if our evaluation is sufficient?