Prior authorization is the approval course of well being plans require earlier than protecting sure medical providers or drugs. It stays one of the vital handbook processes in healthcare, not as a result of the medical reasoning for requiring approval is flawed, however as a result of the insurance policies that govern it are trapped in static, unstructured codecs that resist automation. This content material is on the core of day-to-day medical operations impacting a whole bunch of tens of millions of sufferers annually. Nevertheless, the coverage content material varies by medical space, geography, line of enterprise, and well being plan, and evolves as medication and know-how advances. Traditionally, well being plans didn’t have a scientific technique to handle, analyze, and optimize them. Digitizing these medical insurance policies into structured, machine-readable information utilizing commonplace terminologies scale back a essential operational bottleneck by supporting extra constant, computable workflows and serving to well being plans modernize prior authorization operations at scale whereas sustaining acceptable medical oversight.
Cohere Well being(R), a medical intelligence firm that powers well being plan operations, constructed Cohere Coverage Studio(TM) utilizing Amazon Bedrock AgentCore, which gives the multi-tenant isolation required for his or her well being plan prospects and a managed agent runtime that accelerates deployment with out rebuilding infrastructure. The applying makes use of a versatile, multi-tenant agentic structure to speed up coverage digitization with in depth workflow administration and automated model monitoring.
On this put up, you find out how Cohere Well being constructed a multi-tenant agentic structure on AgentCore utilizing AgentCore Runtime’s safe MicroVM isolation, unified software entry by way of AgentCore Gateway, AgentCore Reminiscence, and the Agent Abilities open commonplace to quickly scale coverage digitization capabilities, whereas preserving transparency, model management, and human oversight.
Problem: The coverage digitization bottleneck
Realizing the worth of AI-assisted workflows in prior authorization is determined by a foundational problem: remodeling the principles trapped in static paperwork and PDFs into structured, machine-readable information that AI techniques can use extra constantly, whereas medical skilled stay liable for medical evaluation the place medical judgment is required. Well being plans face a posh problem of managing medical insurance policies to help quickly altering necessities. Automating coverage digitization helps well being plans adapt to those modifications.
Cohere Well being recognized three challenges in constructing an AI answer for this workflow:
Authorities rules – Per Facilities for Medicare & Medicaid Providers (CMS) rules, well being plans are required to help API-based digital prior authorization by January 2027.
America’s Well being Insurance coverage Plans (AHIP) – The AHIP commitments require well being plans to attain 80 p.c real-time approvals for digital prior authorization submissions. Every line of enterprise has distinctive necessities, growing the necessity to shortly handle, audit, and deploy medical insurance policies.
Technical structure calls for – The answer wanted to ingest a number of enter codecs and produce completely different representations of every coverage for various downstream shoppers, every with its personal suggestions loop.
AgentCore addresses these challenges with managed runtime infrastructure, session isolation, and unified software entry.
Answer overview
The next diagram reveals how Cohere Coverage Studio connects AgentCore Runtime, Gateway, and Reminiscence right into a unified agentic system for coverage digitization.
The Coverage Studio utility is constructed on AgentCore utilizing the Agent Abilities open commonplace. To scale out representations in Cohere Coverage Studio, Cohere Well being added new abilities to an current AgentCore Runtime that was already decomposing insurance policies. This runtime had entry to the coverage abilities, coverage APIs as Mannequin Context Protocol (MCP) instruments by way of AgentCore Gateway, and session reminiscence for coverage analysts’ suggestions loops, serving to groups refine outputs inside a ruled, human-in-the-loop course of.
The workforce accomplished three duties:
Deployed AgentCore Runtime with AgentCore Gateway and AgentCore Reminiscence for a full agentic system utilizing LangChain.
Configured AgentCore Gateway to fetch instruments and abilities.
Wrote abilities with medical coverage specialists and evaluated them utilizing Cohere Well being’s standardized observability course of based mostly on Arize AI.
You possibly can apply these identical patterns to construct your individual multi-tenant agentic system.
Deploying AI brokers with reusable Amazon Elastic Container Registry (Amazon ECR) base pictures
Cohere Well being serves a number of well being plans that require strict information isolation between tenants. AgentCore Runtime’s safe microVM isolation enforces this with devoted compute, reminiscence, and filesystem assets per session.
When deploying a number of AI agent cases throughout groups, sustaining consistency whereas permitting customization is necessary. Every workforce wants its personal agent configuration, however rebuilding the complete runtime setting for each deployment creates pointless overhead and drift. You should utilize the next base picture sample to deploy new brokers to AgentCore Runtime microVMs with a minimal Dockerfile.
Base picture and shopper sample
Cohere Well being developed a two-tier deployment structure that separates the secure runtime setting from team-specific configurations:
The FROM line pulls the shared base picture containing the LangChain agent framework and customary dependencies. The COPY line provides the team-specific agent_config.yaml, which controls the next choices:
Reminiscence modes – Select between stateless (NO_MEMORY) or persistent (AGENTCORE) dialog historical past.
Storage methods – full_trace for correction workflows or conversation_only for clear historical past.
Session context caching – Robotically caches talent definitions and paperwork to keep away from redundant Amazon Easy Storage Service (Amazon S3) fetches.
Immediate caching – Can assist scale back prices and latency by caching system prompts and regularly used content material.
Versatile software configuration – Allow/disable instruments per deployment.
Mannequin configuration – Base mannequin on Amazon Bedrock with configurable token limits, temperature, and different inference parameters.
LiteLLM configuration – Configure LiteLLM because the reverse proxy between the mannequin and the agent.
With the runtime deployed, the following step was connecting it to instruments and abilities.
Unified software and talent entry with AgentCore Gateway
Cohere Well being’s brokers entry a number of software sorts, together with AWS Lambda capabilities for fetching abilities and paperwork, and inside APIs, maintained throughout completely different groups. AgentCore Gateway consolidates these behind a single authenticated endpoint, so groups add new instruments with out redeploying the agent.
AgentCore Gateway structure
Cohere Well being carried out this utilizing AgentCore Gateway with separate targets for shared instruments and project-specific instruments.
Software Lambda perform construction
AgentCore Gateway invokes an AWS Lambda perform for every software request. The perform routes to the right handler based mostly on the software identify handed within the gateway context.
Software implementation
Every software handler fetches information from a particular supply. The next instance retrieves a talent definition from Amazon S3.
Agent configuration
The agent configuration defines which gateway targets the agent can entry and the way it authenticates.
With the runtime and instruments in place, Cohere Well being turned to constructing the area experience layer.
Abilities growth and analysis
AI brokers want domain-specific data to carry out specialised duties successfully. Generic prompts produce inconsistent outcomes, require in depth token utilization, and lack the nuanced understanding that area specialists deliver. Every new use case historically required rebuilding agent infrastructure from scratch, creating bottlenecks in deployment velocity. A modular abilities framework addresses this by decoupling area experience from infrastructure. For Cohere Well being, this implies medical coverage specialists can creator and refine new abilities instantly, serving to make sure the system helps coverage workflows in ways in which stay grounded in knowledgeable evaluation and governance.
Modular abilities framework
Groups deploy new capabilities by way of modular, versioned talent definitions with out rebuilding the agent.
Improvement workflow
Cohere Well being follows a structured workflow to develop and validate every talent earlier than it reaches manufacturing.
Analysis course of
Evaluating abilities requires collaboration between machine studying engineering and information science. The method begins with reference datasets that comprise floor fact outputs for every talent. The workforce defines success metrics (accuracy, completeness, and consistency) and runs an analysis suite in opposition to these check circumstances. When a talent fails, the workforce analyzes the failure mode and iterates on the talent definition earlier than retesting.
After a talent passes the analysis suite, information science critiques the outcomes in opposition to acceptance standards and approves the talent for manufacturing deployment.
After deployment, Arize AI tracks effectiveness metrics in manufacturing. Scientific coverage analysts annotate pattern outputs to catch errors the automated metrics miss. The workforce screens for talent degradation over time and makes use of these information factors to prioritize optimization work.
Ability versioning and deployment
Abilities transfer to manufacturing by way of a layered versioning scheme and a staged deployment pipeline.
Twin-layer versioning
Abilities use dual-layer versioning: semantic versioning for functionality monitoring and Amazon S3 object versioning for deployment historical past. The primary layer tracks functionality modifications in SKILL.md, with every model tagged in git (for instance, talent/policy_ingestion/v1.2.3). Amazon S3 object versioning gives the second layer, sustaining immutable historical past for each add with rollback functionality and separate non-prod/prod buckets.
Deployment circulation
Developer commits and opens a PR to develop.
Steady integration and steady supply (CI/CD) packages talent.tar.gz with metadata on merge.
The pipeline uploads to the Amazon S3 non-prod bucket and updates the manifest.
Consider in non-prod setting.
Open PR to predominant.
Deploy to prod with gradual rollout and monitoring.
Outcomes and affect
Via this implementation, Cohere Well being achieved measurable enhancements throughout coverage digitization velocity, deployment velocity, and protection.
Coverage digitization effectivity: Total time spent on coverage digitization lowered by 30 p.c, from 2 hours quarter-hour to 1 hour 35 minutes per coverage. Cohere Well being has digitized 1000’s of insurance policies up to now utilizing handbook and semi-automated workflows. The agent-based framework targets additional time discount per coverage because it scales throughout the present coverage library.
Deployment velocity: Full agent deployments within the product decreased from 3–4 months to 2–6 weeks. The reusable ECR base picture sample lets groups get up a brand new agent with a minimal Dockerfile, and the modular abilities framework means new capabilities ship with out rebuilding the agent runtime. The system abstracts DevOps issues, so conventional machine studying (ML) and information science engineers can deploy brokers with out in depth coding expertise. The coverage digitization product runs a single-agent, multi-skill structure with one agent, a main talent with a sub-skill, and three reference injections.
Coverage protection: Cohere Coverage Studio represents coverage content material with verbatim textual content and a regular codified proof layer, packaged collectively and obtainable throughout authentic coverage codecs and sources.
“Prior authorization coverage evaluation has all the time demanded a rare stage of medical consideration—each phrase in a coverage doc can carry downstream penalties for sufferers. However that spotlight has traditionally been break up between interpretation and verification: not simply understanding what a coverage means clinically, however confirming which model of it ruled a given choice, and whether or not that very same model is what the well being plan printed to suppliers. These aren’t administrative questions—they’re questions that bear instantly on medical integrity. Amazon Bedrock AgentCore gave us the structure to handle each concurrently—AI-powered agentic workflows that help with navigating the interpretive complexity of medical language, with built-in reminiscence and model monitoring that make provenance a first-class concern somewhat than an afterthought. Structured, versioned coverage outputs make the medical foundation of a choice traceable and reviewable by design, and AgentCore’s safe, multi-tenant runtime means we are able to ship that functionality throughout each well being plan we serve with out compromising isolation.”
— Brian Covino, M.D., FAAOS, Chief Medical Officer, Cohere Well being
Apply these patterns to attain related outcomes: reusable base pictures for constant deployments, unified software entry by way of a single gateway, and modular abilities that scale with out rebuilding infrastructure.
Future: Connecting insurance policies by way of a data graph
Constructing on Cohere Coverage Studio’s success with AgentCore, the following evolution introduces an clever data graph which is already underway. Working with the AWS Generative AI Innovation Middle, Cohere Well being prototyped the foundational semantic layer mapping medical insurance policies to standardized ontologies (UMLS, SNOMED) to help larger interoperability utilizing standardized healthcare phrases. Utilizing Amazon Neptune, this grounds coverage ideas in a construction that AI can traverse and hint. That graph connects medical insurance policies with decisioning merchandise throughout expanded indications.
Enhanced structure
The data graph layer sits between the coverage illustration engine and downstream decisioning techniques, making a semantic community that:
Maps relationships between insurance policies, medical tips, medical codes (ICD-10, CPT, HCPCS), drug formularies, and prior authorization standards throughout therapeutic areas.
Scales indication protection by figuring out patterns and similarities throughout medical domains, in order that new coverage sorts deploy quickly with out handbook configuration.
Connects coverage fragments to a number of decisioning contexts, so {that a} single coverage replace propagates accurately throughout affected authorization workflows.
Key capabilities
As new insurance policies are digitized by way of AgentCore, the data graph is designed to assist determine related connections, flag potential conflicts, and recommend reusable patterns to help reviewer and coverage workforce workflows. The graph learns from coverage buildings throughout medical areas, suggesting templates and accelerating time-to-deployment for brand spanking new indication sorts from days to hours. Decisioning engines question the data graph utilizing pure language or Quick Healthcare Interoperability Assets (FHIR) assets to retrieve probably related coverage fragments with full provenance and model historical past. The graph additionally maintains bidirectional hyperlinks between CMS necessities, AHIP commitments, and inside coverage representations, supporting regulatory alignment at scale.
These capabilities ship complete indication protection with out proportional engineering effort, real-time coverage updates throughout related decisioning merchandise, automated battle detection to assist forestall inconsistent authorization outcomes, and sub-second coverage retrieval for authorization requests.
This data graph basis helps Cohere Well being’s means to assist well being plans obtain 80 p.c of digital prior authorization approvals in actual time. The graph maintains the safety, multi-tenancy, and audit capabilities established within the present AgentCore structure.
Conclusion
On this put up, you discovered how Cohere Well being used AgentCore and three architectural selections to scale back AI agent deployment from months to weeks. Three patterns (reusable ECR base pictures, unified software entry by way of AgentCore Gateway, and modular abilities growth) helped Cohere Well being help extra scalable coverage digitization workflows throughout codecs whereas decreasing digitization time by 30%.
The ECR base picture sample alleviates redundant infrastructure work, so groups can deploy new brokers with a minimal Dockerfile. Cohere Well being can scale the AI system with out rebuilding the runtime. The AgentCore Gateway structure gives a single authenticated endpoint for the instruments, whether or not they’re utilities based mostly on AWS Lambda or OpenAPI providers. The abilities framework, constructed on the Agent Abilities open commonplace, separates area experience from agent mechanics, supporting fast iteration with steady analysis by way of Arize AI and medical coverage analysts.
The way forward for healthcare AI is determined by techniques that may adapt shortly to altering necessities whereas sustaining reliability and safety. With AgentCore and these architectural patterns, you’ll be able to construct that system in the present day.
To get began with these patterns in your individual setting, discover the next assets:
Study Cohere Well being’s different AgentCore deployment of a medical necessity evaluation agentic assistant on this re:Invent session.
In case you’re a startup constructing production-ready AI brokers, AWS Activate gives the credit, technical steerage, and structure help that can assist you transfer from prototype to manufacturing. Get began in the present day.
In case you have suggestions or questions on this put up, go away a remark within the feedback part.
Concerning the authors
Oleksiy Kononenko
Oleksiy is a Options Architect on the State and Native Authorities workforce at AWS, the place he companions with authorities businesses to make use of cloud applied sciences to enhance citizen providers. Together with his earlier Healthcare and Life Sciences Startups expertise at AWS, he brings a novel builder’s perspective to architecting options that remedy real-world issues. When not working with prospects, you’ll discover him exploring new tech or mountain biking.
Kenji Fujita
Kenji is a Workers AI Platform Engineer at Cohere Well being, the place he has labored for the previous six years. All through his tenure, he has developed lots of the capabilities throughout the assorted platforms that the brand new agent framework is scaling out to help. You could find Kenji struggling whereas watching the Mets and working in his free time.
Vikas Mehta
Vikas is a Machine Studying Engineer at Cohere Well being, the place he began as a co-op throughout his MSCS at UMass Amherst. He’s a contributor to the framework outlined on this put up. When he’s not working, Vikas enjoys swimming, board video games with buddies, and exploring parks and eating places across the metropolis.
Anna Wang
Anna is a Software program Engineer at Cohere Well being, the place she began as an intern throughout her undergraduate research at Tufts College. She is a contributor to the framework outlined on this put up. Exterior of labor, Anna’s present obsessions are sourdough and distance working.
Ebad Ahmadzadeh
Ebad is a Principal Machine Studying Engineer at Cohere Well being, the place he has labored for the previous 4 years. He led analysis and implementation for lots of the ML merchandise on the firm. Ebad enjoys studying about music concept, spends time along with his household, and goes on canine walks.
Adwait Patil
Adwait is a Machine Studying Engineer at Cohere Well being, the place he began as a co-op throughout his MSDS at Northeastern’s Khoury School of Pc Sciences. He has labored extensively on Cohere Coverage Studio. Adwait can normally be discovered mountaineering or taking part in badminton or basketball, usually utilizing it as the proper excuse to discover new meals spots.
