Enterprise-grade agent archetypes deployed across industries. Real architecture patterns, governance models, and execution capability — organized by what they do, not what they're called.
Processes unstructured clinical notes and pathology reports to extract, normalize, and validate biomarker data. Handles ambiguity, conflicting values, and incomplete records. Outputs structured biomarker profiles with confidence scores and source citations.
Clinical biomarker data trapped in unstructured pathology reports, requiring hours of manual review per patient. Oncology teams need normalized, validated biomarker profiles — but extraction is slow, inconsistent, and doesn't scale.
Multi-agent system on Azure: Document Ingestion Agent (Azure AI Document Intelligence) → Extraction Agent (Azure OpenAI GPT-4) → Validation Agent (rule-based + LLM cross-check) → Output Agent (structured JSON/FHIR). Orchestrated via custom router with fallback chains.
Human-in-the-loop for all low-confidence extractions. Audit trail for every field. PHI handling via Azure compliant infrastructure. No clinical decision-making — output is informational only.
85% reduction in manual review time. 94% extraction accuracy on benchmark dataset. Scalable to 500+ reports/day.
Framework: Custom orchestrator on Azure Functions · Models: Azure OpenAI GPT-4o · Infra: Azure AI Document Intelligence, Cosmos DB · Lifecycle: Stage 2 (Architect) → Stage 4 (Build) · Toolkit: Orchestration Blueprint, Identity & Trust Template
Evaluates patient or member eligibility against complex, nested business rules — plan coverage, prior authorization requirements, clinical criteria — and produces explainable approval/denial decisions with full rule trace.
Eligibility determinations involve dozens of interdependent rules that change with policy updates. Manual review is error-prone, slow, and creates inconsistent outcomes across reviewers.
Policy Parser Agent (rule extraction from documents) → Reasoning Agent (LLM-driven chain-of-thought against structured rules) → Explainability Agent (generates human-readable rule trace). Stateful with checkpoint recovery.
All denials require human review. Full decision audit trail. Rule versioning with change tracking. Bias monitoring on approval/denial distributions.
70% faster determination cycle. 99.2% rule-trace accuracy. Consistent outcomes across all reviewers.
Framework: Custom Chain-of-Thought orchestrator · Models: Azure OpenAI GPT-4o · Infra: Azure Functions, SQL Server · Lifecycle: Stage 2 (Architect) → Stage 3 (Govern) · Toolkit: Reasoning Techniques Playbook, Governance Policy Template
Analyzes incoming RFPs, maps requirements to vendor capabilities, and produces weighted comparison scorecards. Handles complex evaluation criteria across technical compliance, cost, past performance, and socioeconomic factors for state and local procurement.
State and local agencies process hundreds of RFPs annually. Evaluators manually cross-reference vendor responses against scoring rubrics — a process that takes weeks per solicitation and produces inconsistent scoring across review panels.
LangGraph stateful workflow: Document Parser Agent (PDF/DOCX ingestion) → Requirements Extraction Agent (criteria mapping) → Vendor Scoring Agent (weighted multi-criteria analysis) → Comparison Report Agent (ranked output with rationale). RAG over past procurement decisions for precedent-aware scoring.
All scoring decisions include full rationale traces. Human review required before final vendor selection. Audit log meets state procurement compliance requirements. No vendor data shared across agencies.
70% reduction in initial scoring time. Consistent application of evaluation criteria across review panels. Traceable scoring rationale for protest defense.
Framework: LangGraph on Railway · Models: Claude Sonnet, GPT-4o · Infra: Supabase (storage), Cloudflare R2 (documents) · Lifecycle: Stage 1 (Justify) → Stage 4 (Build) · Toolkit: Use Case Prioritization, Framework Comparison Playbook
Analyzes historical enrollment patterns, demographic shifts, program demand signals, and financial aid data to predict enrollment trends and recommend intervention strategies for community colleges and state university systems.
Community college systems manage enrollment across 16+ campuses with declining enrollment trends. Manual forecasting relies on lagging indicators. Intervention decisions (marketing spend, program adjustments, aid allocation) are reactive, not predictive.
CrewAI multi-agent crew: Data Aggregation Agent (ERP + SIS integration) → Trend Analysis Agent (time-series forecasting) → Intervention Recommendation Agent (strategy generation with cost-benefit) → Dashboard Agent (executive summary with drill-down). Deployed on Railway with Supabase backend.
FERPA-compliant data handling. All student-level data anonymized in agent processing. Recommendations are advisory — enrollment officers make final decisions. Monthly model drift monitoring.
15% improvement in enrollment forecast accuracy. Early identification of at-risk programs 60 days before enrollment deadlines. Targeted intervention recommendations with estimated ROI.
Framework: CrewAI on Railway · Models: Claude Sonnet · Infra: Supabase, Netlify, Cloudflare · Lifecycle: Stage 1 (Justify) → Stage 6 (Operate) · Toolkit: ROI Calculator, AgentOps Playbook
Ingests requirements documents and existing test suites. Generates new test scenarios covering edge cases. Identifies coverage gaps. Executes regression suites and surfaces failures with root cause hypotheses.
Insurance platform releases require extensive regression testing across complex business rules. Manual test creation is slow, coverage is inconsistent, and QA cycles bottleneck delivery timelines.
Requirement Parser Agent (Azure OpenAI) → Test Generator Agent (structured prompting with business rule context) → Coverage Analyzer (deterministic + LLM hybrid) → Execution Agent (Selenium/API integration). Stateful workflow with checkpoint recovery.
Generated tests require human approval before execution against production-like environments. All generated artifacts versioned. No direct production access.
10× throughput improvement in QA cycles. 35% more edge cases identified vs. manual approach. Average 2-day reduction in release cycle time.
Framework: Custom stateful workflow · Models: Azure OpenAI GPT-4o · Infra: Azure DevOps, Selenium Grid · Tools: Jira API, Azure Test Plans · Lifecycle: Stage 4 (Build) → Stage 5 (Gate) · Toolkit: Design Principles Checklist, Anti-Patterns Workbook
Generates validation queries from schema mappings. Executes parallel source/target comparisons. Identifies discrepancies, classifies severity, and generates exception reports with remediation suggestions.
Large-scale data migrations from legacy PeopleSoft systems to Oracle Analytics Cloud require exhaustive validation — row counts, aggregations, business rule integrity — across hundreds of tables and millions of records.
Schema Mapping Agent → Query Generator Agent → Parallel Executor → Discrepancy Classifier → Report Generator. Built on Azure with Oracle Autonomous Database connectivity.
90% reduction in manual validation effort. Catches schema drift that manual QA misses. Full audit trail of every comparison.
Framework: Custom parallel executor · Models: Azure OpenAI GPT-4o (discrepancy classification) · Infra: Oracle Autonomous Database, Azure SQL, Azure Functions · Lifecycle: Stage 4 (Build) → Stage 5 (Gate) · Toolkit: Framework Comparison Playbook, Design Principles Checklist
Automates property assessment workflows by ingesting deed documents, parcel data, and valuation records. Applies assessment rules, generates valuation proposals, and routes exceptions for human review with full audit documentation.
County assessor offices process thousands of property assessments annually, each involving document review, rule application, and valuation calculations. Backlogs create taxpayer delays and compliance risk.
Document Ingestion Agent (OCR + structured extraction) → Rule Engine Agent (jurisdiction-specific assessment rules) → Valuation Agent (comparable analysis + rule-based calculation) → Routing Agent (exception classification + human queue). Oracle + Azure hybrid.
60% reduction in assessment processing time. 100% audit-compliant documentation. Exception routing accuracy at 97%.
Framework: Custom orchestrator on Azure Functions · Models: Azure OpenAI GPT-4o · Infra: Oracle DB, Azure AI Document Intelligence · Lifecycle: Stage 3 (Govern) → Stage 4 (Build) · Toolkit: Governance Policy Template, Incident Response Playbook
Guides new enterprise SaaS customers through onboarding by auto-provisioning environments, configuring integrations, scheduling kickoff calls, generating personalized training plans, and monitoring activation milestones — reducing time-to-value from weeks to days.
Enterprise SaaS onboarding involves 20+ manual steps across CSM, engineering, and product teams. Average time-to-value is 6 weeks. Customers churn in the first 90 days because onboarding stalls at handoff points between teams.
LangGraph stateful workflow: Welcome Agent (CRM data enrichment + plan analysis) → Provisioning Agent (environment setup via API) → Integration Agent (connector configuration + testing) → Training Agent (personalized content generation) → Activation Monitor Agent (milestone tracking + nudges). MCP for tool connectivity to CRM, ticketing, and calendar systems.
Time-to-value reduced from 6 weeks to 8 days. CSM capacity increased 3x. 40% reduction in onboarding-related support tickets.
Framework: LangGraph on Railway · Models: Claude Sonnet · Infra: Supabase, Netlify, Cloudflare R2 · Tools: MCP (Salesforce, Jira, Google Calendar) · Lifecycle: Stage 2 (Architect) → Stage 6 (Operate) · Toolkit: Orchestration Blueprint, AgentOps Playbook
Ingests IT support tickets, classifies severity and category, diagnoses root cause using knowledge base and system logs, and auto-resolves L1/L2 issues (password resets, access provisioning, software installations). Routes complex issues to human specialists with full diagnostic context.
IT help desks handle 2,000+ tickets/month. 60% are repetitive L1 issues (password resets, VPN access, software requests) that consume specialist time. Mean time to resolution for simple requests: 4 hours. SLA breaches increasing quarterly.
CrewAI crew: Triage Agent (classification + priority) → Diagnostic Agent (KB search + log analysis) → Resolution Agent (auto-fix via API calls to AD, ServiceNow, Okta) → Escalation Agent (context packaging for human specialists). RAG over internal KB with 5,000+ resolution articles.
55% of L1 tickets auto-resolved without human intervention. MTTR for L1 reduced from 4 hours to 8 minutes. L2 specialist time freed by 30%.
Framework: CrewAI · Models: GPT-4o, Claude Haiku (triage) · Infra: Azure, ServiceNow API · Tools: Okta, Active Directory, JIRA · Lifecycle: Stage 4 (Build) → Stage 6 (Operate) · Toolkit: Design Principles Checklist, Anti-Patterns Workbook
Monitors agent actions against policy constraints in real-time. Flags policy violations, drift from approved behavior boundaries, and anomalous decision patterns. Generates compliance reports with evidence chains for audit readiness.
As organizations deploy more AI agents, ensuring each operates within policy boundaries becomes exponentially harder. Manual compliance reviews can't keep pace with autonomous agent actions.
Event Stream Listener → Policy Rule Engine (constraint checking) → Anomaly Detection Agent (behavioral drift analysis) → Alert & Reporting Agent (severity-classified notifications + audit reports). Deployed as a sidecar to monitored agent systems.
Real-time policy violation detection (< 30s latency). 100% action coverage for monitored agents. Audit report generation reduced from days to minutes.
Framework: Custom event-driven on Azure Functions · Models: GPT-4o (anomaly classification) · Infra: Azure Event Hub, Cosmos DB, Azure Monitor · Lifecycle: Stage 3 (Govern) → Stage 6 (Operate) · Toolkit: Governance Policy Template, AgentOps Playbook
Ingests vendor questionnaires, SOC 2 reports, financial filings, and public breach databases to produce risk-scored vendor assessments. Continuously monitors vendor risk posture and alerts on material changes — critical for financial services third-party risk management.
Financial institutions manage 500+ vendor relationships with annual risk assessments that take 3–5 days each. Questionnaire reviews are manual, SOC 2 gap analysis is inconsistent across reviewers, and continuous monitoring is practically nonexistent.
LangGraph workflow: Document Intake Agent (SOC 2, questionnaires, financial filings) → Gap Analysis Agent (control mapping against firm's risk framework) → Scoring Agent (weighted risk model with historical trend) → Monitoring Agent (public breach database + news surveillance). RAG over internal risk policies and prior assessment decisions.
Assessment time reduced from 5 days to 4 hours per vendor. Continuous monitoring covers 100% of critical vendors (vs. annual snapshots). 3 material risk events caught within 24 hours of public disclosure.
Framework: LangGraph on Railway · Models: Claude Sonnet, GPT-4o · Infra: Supabase, Cloudflare R2 (document store) · Tools: MCP (Salesforce, internal risk DB) · Lifecycle: Stage 3 (Govern) → Stage 5 (Gate) · Toolkit: Identity & Trust Template, Incident Response Playbook
A coordinated multi-agent system for end-to-end clinical trial data operations — from biomarker extraction through eligibility reasoning to compliance validation and reporting. Demonstrates real agent-to-agent communication, shared state, and orchestrated governance.