AI AgentRecruitment Agent
Recruitment Agent that automates hr workflows using your data and tools.
The challenge
Recruiters read hundreds of CVs per role and most applicants never hear back at all. Screening depends on who is reading and how late in the day it is, scheduling a panel takes days of email, and there is rarely a defensible record of why one candidate advanced over another.
The outcome
Azure AI Document Intelligence parses CVs and certificates into structured skills, a Microsoft Foundry agent screens against the role criteria with protected characteristics excluded from scoring, and Azure Logic Apps coordinates the panels. Every applicant gets an update, and every score links to its evidence.
At a glance
- Type
- ai agents
- Category
- specialized
01 — Architecture
End-to-end architecture
Applications arrive from the careers site and job boards, are parsed by Document Intelligence into structured skills and experience, and matched by the Foundry agent against the role criteria. Protected characteristics are excluded from scoring, access to candidate data is governed, and a recruiter makes every advance and reject decision.
- Candidate: Candidate / Manager
- Channels: Azure Static Web Apps, Communication Services
- Application: Azure API Management, AI Document Intelligence
- AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
- Data & governance: Azure SQL, Azure Cosmos DB, Access governance
- Decision & insight: Recruiter decision, Interview scheduling, Power BI / Fabric
02 — Workflow
Process & decision workflow
How an application is attracted, parsed, screened, engaged and scheduled — then branched. A clear match advances to interview with the panel briefed, while a borderline profile or a requested adjustment goes to a recruiter with the evidence and gaps shown side by side.
- Attract: Role published to the careers site and job boards
- Parse: CVs, certificates and portfolios turned into data
- Screen: Skills, experience and eligibility matched to the role
- Engage: Structured questions asked and answers summarised
- Schedule: Panels, availability and reminders coordinated
- Report: Funnel, time to hire and drop-off tracked
- Path 1 · clear match — Advance to interview: Candidate scheduled and the panel briefed
- Path 2 · borderline or adjustment — Recruiter decision: Evidence, gaps and comparison shown side by side
03 — Components
Key Microsoft components
Hiring decisions have to be fast and defensible — parsing, screening, scheduling and governance all stay on the Microsoft stack.
Azure Static Web AppsCareers site, application and candidate self-service.
Azure Communication ServicesCandidate email and SMS updates at every stage.
Azure API ManagementSecure gateway for applicant tracking and HRIS APIs.
Azure AI Document IntelligenceParses CVs, certificates and portfolios into structured data.
Microsoft Foundry Agent ServiceScreening, question generation and scheduling orchestration.
Azure OpenAI modelsCandidate summaries, structured questions and answer scoring.
Azure AI SearchRetrieval across the talent pool, role library and past hires.
Azure AI Content SafetyBias, tone and inclusive-language guardrails.
Microsoft Entra permissionsAccess governance over candidate and personal data.
Azure Logic AppsPanel scheduling, reminders and offer workflows.
Azure SQLApplications, stages, scores and decision records.
Azure Cosmos DBInterview state, availability and conversation history.
Azure Blob StorageCVs, portfolios and supporting candidate evidence.
Power BI / Microsoft FabricFunnel, time to hire and adverse-impact dashboards.
04 — AI
What the agent consumes
The capabilities the agent applies to every application, and the line it does not cross.
AI capabilities embedded in the agent
- CV and certificate parsing
- Skill extraction
- Role matching
- Structured screening
- Retrieval-augmented generation
- Interview question generation
- Answer summarisation
- Bias and language guardrails
- Availability matching
- Candidate messaging
- Funnel analytics
- Workflow orchestration
AI responsibility boundaries
The agent screens, summarises and schedules; a named recruiter or hiring manager makes every advance, reject and offer decision. Protected characteristics are excluded from scoring, every score shows the evidence behind it, candidates are told that AI assisted the process, and any adverse decision is reviewable by a person.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the role library, the hiring template, the criteria and the value model — the page structure stays identical.
Role & market profile
Define the job families, seniority, locations, languages and right-to-work constraints in scope. The personas are the candidate, the recruiter, the hiring manager and HR compliance.
Hiring template
One consistent flow for every recruitment agent: attract, parse, screen, engage, schedule, interview, decide, offer and hand over to onboarding.
Criteria & fairness rules
Declare the must-have and nice-to-have criteria, the attributes excluded from scoring, the rubric, the adjustment process and the escalation path. Every score links to its evidence.
Value model
Capture baseline metrics first, then map the expected benefits: time to hire, screening hours per role, candidate drop-off, offer acceptance and adverse-impact monitoring.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- Time to shortlistHoursApplications are parsed and screened as they arrive.
- Screening effort−65%Recruiters review evidence-backed shortlists, not inboxes.
- Candidate responseSame dayEvery applicant gets an update rather than silence.
- Decision evidence100%Each score links to the evidence in the application.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Time to shortlist: manual baseline 100, AI-assisted target 25
- Screening hours: manual baseline 100, AI-assisted target 35
- Candidate drop-off: manual baseline 100, AI-assisted target 60
Related & recommended
Derived automatically from our solution knowledge graph.
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Related quick wins
Ready-made Azure AI to start fast.
Technologies
What powers this solution.
TechnologyAzure AI
Managed AI services for vision, speech, language and document.
TechnologyAzure AI Foundry
Platform to design, evaluate and operate AI apps and agents.
TechnologyAzure OpenAI
Enterprise access to GPT models with governance.
TechnologyMicrosoft Copilot Studio
Low-code platform to build custom copilots and agents.
Related managed services
Keep it running and optimised.
ManagedAI Managed Services
AI Managed Services from our UAE-based 24/7 Cloud Operations Center.
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Cloud Managed Services from our UAE-based 24/7 Cloud Operations Center.
ManagedDevOps Managed Services
DevOps Managed Services from our UAE-based 24/7 Cloud Operations Center.
ManagedFinOps / Cloud Cost Management (OpsNow)
FinOps / Cloud Cost Management (OpsNow) from our UAE-based 24/7 Cloud Operations Center.
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