Engineering

Multi-agent systems for hiring operations.

Majhi OS doesn't run a single AI that tries to do everything. It runs a coordinated system of specialised agents — each responsible for one layer of the hiring operation — working in parallel across every active mandate.

Why single-agent systems fail at scale.

A single AI agent managing a VP search has to context-switch between sourcing, outreach, pipeline tracking, quality assessment, and escalation decisions — all while maintaining state across weeks of activity. The cognitive load is too high. Errors compound.

Majhi OS solves this with specialisation. Each agent owns one domain. Each runs in parallel. A coordination layer integrates their outputs into a unified mandate view.

Specialisation

Each agent is trained and optimised for a specific task. The Sourcing Agent doesn't know about outreach sequencing. The Recovery Agent doesn't manage candidate ranking. Specialisation means depth.

Parallelism

While the Outreach Agent is running sequences for one mandate, the Quality Agent is reviewing shortlists for another, and the Monitoring Agent is watching all of them simultaneously. No context-switching.

Coordination Layer

A central orchestrator routes outputs between agents, manages handoffs, resolves conflicts, and maintains the unified mandate state that every agent operates from.

Five agents. One coordinated system.

Each agent in the Majhi OS network has a specific mandate, specific inputs, and specific outputs. Together they form a hiring operations system that runs 24/7 across every active search.

Sourcing Agent

Identifies passive candidates across LinkedIn, job boards, and proprietary signal feeds. Applies ICP scoring, ghost job detection, and availability signals before surfacing any candidate to the quality gate.

Outreach Agent

Manages personalised outreach sequences. DNS/MX verifies every contact. Tracks open rates, reply rates, and response decay. Adjusts sequence timing and copy based on engagement signals.

Monitoring Agent

Watches every active mandate simultaneously. Tracks Hiring Health Score, SLA proximity, funnel velocity, and recruiter load. Fires alerts and triggers recovery when thresholds breach.

Quality Agent

Reviews every candidate before outreach is unlocked. Applies the QMS gate — evidence dossier completeness, fit score threshold, risk flag clearance. Nothing moves without quality clearance.

Recovery Agent

Executes Recovery Playbooks when the Monitoring Agent flags a stalling mandate. Selects the highest-confidence recovery action from the intelligence graph and runs it — without waiting for escalation.

Attribution Agent

Measures and reports on operational outcomes. Connects actions taken by other agents to mandate outcomes — time-to-fill improvement, response rate lift, shortlist approval rate change.

The evolution of the agent network.

The current agent network handles sourcing, outreach, monitoring, quality, recovery, and attribution. The next generation expands into hiring manager alignment, offer strategy support, and workforce planning intelligence.

The long-term direction: AI agents managing portions of hiring operations independently — with humans retaining authority over judgment-layer decisions.

Hiring Manager Agent

Automates hiring manager prep — brief generation, question sets, calibration prompts — reducing the coordination overhead that slows most searches at the interview stage.

Workforce Planning Agent

Cross-mandate intelligence: which roles are systematically hard to fill, which teams are structurally under-resourced, which hiring patterns predict org health. Strategic intelligence, not just operational.

Human-in-the-Loop Preserved

Offer decisions, cultural assessment, and final hire authority always remain with humans. The agent network handles operations. Humans handle the decisions that require human judgment.

See Majhi OS on your actual mandate.

In 45 minutes, we map your hiring system's operational gaps — using your live mandate as working context, not a generic demo.

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