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Building a healthcare agent from intake to submission

An imaging referral shows how to build an agent with a clear task, the right tools, and human review before submission.

By Deon Metelski

4 min readSeptember 21, 2026

A team building an imaging-referral agent needs a shared definition of a completed case. The packet should carry the correct case reference, supporting documents, applicable coverage information, and a summary that links its claims to the source records. It should reach the responsible reviewer with its intended destination visible.

Use an illustrative referral that arrives with clinical records and a missing physician’s order. The first release has a specific responsibility: assemble a review-ready packet, follow up on the missing order, and route the proposed submission for human review. That case becomes the thread connecting the plan in Agent Studio to the tests used before release.

Write the case’s possible continuations

Walking through representative referrals exposes the branches the agent will encounter. A complete packet can proceed to preparation. A missing order needs a request and a later continuation. Conflicting identifiers need evidence and review. For each branch, the team names the expected next state and who owns the next decision.

The referral’s acceptance conditions can be written directly into that working brief:

  • The packet and its attachments belong to the selected case.
  • The summary identifies the required documents and points to their source records.
  • A missing order produces a follow-up that the responsible person can review.
  • The proposed submission includes its destination and the evidence behind the action.

Frequency helps the team choose the recurring work for its first release. The range of exceptions determines which branches to include, and the consequence and reversibility of each action help place the review points.

Run the missing-order branch in Agent Studio

The team describes the agent’s role and writes its ordered tasks. Plan Mode presents those tasks as a checklist during execution: identify the case, inspect attachments, retrieve coverage information, prepare the packet, and request review. The team connects the tools for those steps and configures Human Approval where a person’s decision is required.

Agent StudioBuild Agent / Referral intake
Configure
PlanKnowledgeBuilt-in ToolsChecksApprovalMetrics
Agent Plan and Execution
Background / Plan Mode
1
Read the referral packetFind the matching physician's order
2
Check eligibility and plan rulesRetrieve the applicable policy
3
Prepare the authorization requestAttach evidence and source references
4
Submit to the payer portalPause for human approval
Order
Review packet
Reviewer
Submit authorization?Destination and packet ready for review
Review
Illustrative Agent Studio configuration using the current builder's Plan, Knowledge, Tools, Checks, and Approval panels. The workflow produces a packet for review before submission.

Workspace holds procedures, source files, and output artifacts. Knowledge scopes make the appropriate policies available. When the agent finds the missing order, it records the gap and prepares a request from the case details. The reviewer receives that proposed action with the packet, destination, policy consulted, and checks already completed.

A correction remains part of the case. If the order carries a different service date, the revised artifact records that change before the next step uses it. When the requested document arrives, the agent can incorporate it into the packet and continue from the recorded state.

Prepared, approved, submitted, and acknowledged each describe a separate event in this procedure. A submission receipt provides evidence that the packet was submitted. The later acknowledgment records the next transition and tells the care team where the referral stands.

Use the same case brief to evaluate the agent

Before release, the team turns the connected procedure into repeatable tasks in Benchmarks. The complete-packet task exercises the standard path. The missing-order task exercises the request and resume steps. The conflicting-record task checks how the agent gathers evidence and asks for review.

BenchmarksExperiments / Referral readiness
CasesComparisonAttempts
Referral readiness
Inspect the same cases across agent versions
Task cases
Complete packetStandard path
Missing orderRequest and resume
Conflicting recordEvidence and review
BaselineCandidate
Candidate attempt / Missing order
Order requestedFollow-up artifact saved
Record updatedSigned order attached
Review completedPacket accepted by reviewer
referral-order.pdfMatched to the selected caseAttached
Final-state checker
Required documents present
Review decision recorded
Run events and artifacts retainedOpen comparison
Illustrative Benchmarks review: representative referral cases open into recorded attempts, artifact inspection, and final-state checks.

Each task checks the expected artifact and final state. Comparing the same cases across changes to instructions, tools, or models reveals which behavior changed. Opening an attempt connects its result to the particular step and records that produced it.

After deployment, operational measures follow those same case groups. Time to a review-ready packet, reviewer corrections, and completion rate each need a defined observation window and data source. Run history and submission events connect the measures to individual referrals.

The next improvement can then target a concrete part of the work: identifying the order, retrieving its source, or giving the reviewer a clearer packet. The case brief remains a common reference for the builders, reviewers, and care team responsible for the outcome.

Explore Agent Studio and Benchmarks.


Deon Metelski

Written by

Deon Metelski

GM, Healthcare

Product leader shaping agentic healthcare AI that feels human and interoperates across every clinical ecosystem. Previously a healthcare product leader at Zyter|TruCare, Deon has spent his career turning complex clinical and operational work into products clinicians trust. At ACTAVA he leads distributed teams to orchestrate safe, explainable AI agents across EHRs and connected ecosystems, translating clinical and operational needs into intuitive, dependable experiences. His work blends empathy with data, advancing healthcare AI through a product lens, and is driven by a commitment to access, interoperability, and measurable outcomes across the care continuum.

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