The policy execution layer for state HHS
Your entire program knowledge in one Policy Intelligence Hub
Federal rules, state policy, operational guidance, training, QA, and frontline support—connected in one AI-native policy execution platform
Understand what changed, what it impacts, and what workers need to do next
Human services work
Policy moves through people, systems, and daily decisions
Policy Intelligence Graph
Built for complex state HHS policy operations where guidance, training, quality review, and executive oversight have to move together.
From documents to policy intelligence
Connect every layer of policy execution
When one policy node changes, AgentRamp shows the downstream operational impact
One connected graph
Connect rules, state interpretation, operational guidance, training, and QA
Every downstream impact
See which policy sections, worker answers, and materials need attention
Human-reviewed execution
Keep AI suggestions cited, traceable, and subject to agency approval
The connected workflow
Policy does not stop at publication
AgentRamp carries every approved change through guidance, training, frontline support, and QA
Core capabilities
From policy intelligence to frontline readiness
01
Build the source of truth for HHS policy
Policy Studio maps CFR and state policy, detects state options, compares versions, preserves citations, and routes proposed changes through approval
Explore Policy Studio →Live mappingMap 7 CFR 273.2 to SNAP application processing|
Federal citation linked · State mapping workflow02
Know what changed before it becomes an operational problem
Policy Radar monitors CFR updates, FNS memos, waivers, and state sources, then sends material impacts to an analyst review queue
Explore Policy Radar →POLICY RADAR
New source update Previous requirement
Potential impact Analyst review required
↗ Impact alert03
Make every policy change teachable, practiceable, and measurable
Approved policy becomes focused learning, realistic simulations, policy-linked feedback, and targeted coaching—while QA evidence shows where readiness is breaking down
Explore Training & Simulation →POLICY-TO-PRACTICE
Frontline guidanceState-specific answer · cited
Training & SimulationScenario and knowledge check
QA & AnalyticsFeedback loop · QA signal
Training & readiness
Train for the decisions that policy requires
AgentRamp turns approved policy into role-specific learning, realistic simulations, and targeted coaching—then uses performance evidence to show where individuals and teams need support
Explore training and simulation →Policy work is not complete when a rule is published—or when a course is assigned — it is complete when workers can recognize the right policy, follow the right decision path, and explain the right next step in a real case
- 01Build from approved policy
Turn current guidance into focused modules, knowledge checks, and realistic scenarios
- 02Practice real decisions
Let workers rehearse interviews, eligibility reasoning, verification, and next steps without creating case risk
- 03Coach the individual
Show strengths, missed decision steps, policy-linked feedback, and the next recommended module
- 04Improve the system
Aggregate skill gaps and QA findings so leaders can target training and expose recurring policy or guidance problems
One graph, different responsibilities
Give every team the context to act
Connect policy analysis, program operations, frontline readiness, and oversight without creating another silo
Fits the enterprise
Connect the systems that carry policy into practice
AgentRamp can connect policy sources, guidance, training, workforce systems, and analytics while keeping integration availability explicit and verifiable
Discuss your environment →
SharePoint
Teams
Entra ID
Okta
Google Drive
Box
Slack
Zoom
DocuSign
Acrobat
Dropbox
ConfluenceSharePointTeamsEntra IDOktaGoogle DriveBoxSlackZoomDocuSignAcrobatDropboxConfluence
Salesforce
ServiceNow
Workday
SuccessFactors
Cornerstone
Canvas LMS
Docebo
Power BI
Tableau
Snowflake
MuleSoft
BoomiSalesforceServiceNowWorkdaySuccessFactorsCornerstoneCanvas LMSDoceboPower BITableauSnowflakeMuleSoftBoomiSecurity & AI governance
Built for the questions state agencies ask first
AgentRamp keeps AI suggestions separate from approved policy, preserves citations and audit history, supports role-based access, and is designed for deployment on Microsoft cloud services with FedRAMP-authorized options
Security, compliance, and integration availability are deployment-specific and reviewed with each agency. AgentRamp does not claim product-level FedRAMP, NIST, SOC 2, or HIPAA certification on this site
Azure
FedRAMP
NIST
800-53
800-53
AI RMF
Human approvalAI remains draft
TraceabilityCitations and history
RBACSegmented access
Program use cases
Apply policy intelligence where execution is hardest
Start with SNAP and extend the same governed policy-to-practice model across state HHS operations
Why AgentRamp
Not a chatbot, not a document repository — a policy execution layer
WorkStatus quoWith AgentRamp
Understand a rule
Find where the rule is written
Map what it affects and what must happen next
Review change
Compare disconnected documents and trackers manually
Use connected change intelligence with sources attached
Prepare workers
Measure whether assigned training was completed
Use policy-linked practice to show whether workers can apply the right decision path
Improve quality
React to QA findings after errors appear
Turn skill gaps, worker questions, and QA trends into targeted coaching and learning priorities
From source to operating baseline
Build a governed path from policy to practice
Connect the agency’s policy ecosystem, establish human review, and carry approved intelligence into frontline execution
Plan your rollout- 01Connect
Map authoritative sources and state manuals, then bring existing guidance, learning materials, and QA inputs into view
- 02Govern
Configure review queues, permissions, citations, approval workflows, and policy-linked evaluation rubrics
- 03Operationalize
Deliver guidance and simulations, identify coaching priorities, and connect readiness and QA signals back to the graph
Frequently asked questions
Understand the policy execution layer
Clear answers about the graph, governance, frontline support, and program scope
Move from policy change to operational readiness
See how AgentRamp turns policy intelligence into frontline action
See how AgentRamp connects policy intelligence, frontline learning, simulation, coaching, and QA in one governed system
Demo requestTell us where policy execution breaks today
Share your details and we’ll route the request to the AgentRamp team