AI RECRUITMENT
Let AI Run the Recruiting Work. Keep People in Control of the Decision.
Deploy specialised AI agents across sourcing, research, engagement, scheduling, assessment preparation, interview support, reporting and workflow management.
How can AI execute and coordinate recruiting work — without turning hiring into an opaque automated decision?
The AI Recruitment Control Room
One recruiter supervising multiple AI agents — active tasks, candidate communications, approvals, exceptions and completed actions, all visible in one place.
Sourcing Agent
WorkingSearch adjacent pools — AI Product Manager
Candidate Engagement Agent
WorkingAnswering 2 routine process questions
Scheduling Agent
Waiting approvalInterview slate for Thursday
Talent Research Agent
ExceptionConflicting employment dates — paused
Briefing Agent
Done3 candidate briefs prepared
Assessment Prep Agent
DoneWork sample finalised
Approvals awaiting a human
Scheduling Agent — interview slate for Thursday, 6 candidates.
Exceptions — automation paused
Talent Research Agent — conflicting employment dates, waiting for a person.
The Recruiting Agent Workforce
Role Architect
Builds and maintains role intelligence models.
Talent Research
Investigates candidates, pools and markets.
Sourcing
Finds and refines relevant talent sources.
Candidate Engagement
Communicates with candidates — and identifies itself.
Scheduling
Coordinates interviews and availability.
Assessment Preparation
Prepares work samples and structured assessments.
Interview Preparation
Drafts plans, questions and evidence prompts.
Candidate Briefing
Assembles evidence into candidate briefs.
Pipeline Intelligence
Monitors pipeline health and stalled roles.
Compliance & Audit
Watches permissions, records and policy boundaries.
Onboarding Handoff
Transitions selected candidates into onboarding.
Every Agent Has a Contract
Purpose, permitted actions, required inputs, output format, confidence state, escalation conditions and human-approval boundaries.
Sample agent contract
No agent should have undefined authority.
Three Visible Classes of Action
AI may perform automatically
AI prepares — a human approves
AI must never execute without explicit human approval
The Automated Recruiting Workflow
Workflows are configurable — stages, thresholds and approval gates adapt to each organization.
The AI Research Workspace
Ask AI to research a candidate, talent pool, role requirement or hiring-market question — with sources, assumptions and uncertainty exposed.
Research output always shows its sources, assumptions and uncertainty.
The Candidate Engagement Agent
Candidate-facing automation must identify itself appropriately and offer escalation to a human.
The AI Sourcing Agent
It proposes — it must not silently redefine selection criteria.
AI Interview Preparation
AI draft
Interviewers edit and approve before use.
The Exception Engine
Automation pauses when something does not hold together — and waits for a person.
Automation paused — human attention required
Uncertainty
The agent's confidence drops below its threshold.
Missing evidence
A required input never arrived.
Conflicting information
Two sources disagree.
Sensitive context
The matter needs human judgement.
Outside the permission boundary
The action exceeds the agent's contract.
Ask DISHA
The AI Activity Ledger
Every meaningful AI action records timestamp, agent, context, action, output, approval and the resulting system change.
| Time | Agent | Action | Approval | Result |
|---|---|---|---|---|
| 09:14 | Scheduling | Proposed Thursday interview slate | Human approved | 6 interviews booked |
| 09:41 | Engagement | Answered process question | Auto (in class) | Candidate notified |
| 10:02 | Talent Research | Flagged conflicting employment dates | Escalated | Automation paused |
| 10:30 | Briefing | Prepared 3 candidate briefs | Human approved | Briefs shared with panel |
What the Recruiter Gets Back
Recovered from administration
- Scheduling
- Status updates
- Routine answers
- Report assembly
Awaiting human approval
- Interview slate
- Outreach drafts
- Candidate briefs
Unresolved exceptions
- Conflicting dates
- Missing evidence
Human-judgement work
- Decisions
- Candidate relationships
- Criteria stewardship
Real enterprise outcomes are measured after deployment — generic productivity gains are not claimed.
AI Quality Monitoring
Quality signals are tracked and reviewed — the system is expected to earn trust, not assume it.
One Recruiter, Ten Agents
Illustrative demo — fictional role, synthetic data
Create a role, activate the agents, watch the workflow, intervene at the gate, challenge an output, inspect the ledger.
Governance
Least privilege
Agents hold the minimum access their contract allows.
Approval gates
Irreversible actions wait for a person.
Auditability
Every action is recorded and reviewable.
Data controls
Data use follows policy and consent.
Model monitoring
Quality and behaviour are tracked over time.
Prompt & version governance
Changes are managed, not silent.
Human oversight
A named human owns every workflow.
Candidate transparency
Candidates know when automation is involved.
SEE THE SYSTEM
The AI Recruiting Team
2 minutes · reserved film slot
Agents do the operational work. The recruiter remains accountable for decisions.
Connected, Never Duplicated
Automation You Can Supervise.
AI agents take on the operational weight of recruiting — researching, preparing, coordinating and learning — while humans retain decision authority at every gate that matters.