The Autonomous Agent Workflow

From Talent Sourcing to Day One in Four Intelligent Steps

Discover how LinQOrbit replaces weeks of manual screening with algorithmic precision, verifiable code audits, and autonomous multi-agent coordination.

Step-by-Step Trajectory

How Multi-Agent Sourcing Operates

LinQOrbit deploys dedicated autonomous micro-agents tailored to each phase of technical talent evaluation.

01

Requirement Synthesis

Dynamic Job & Skill Trajectory Definition

Define target stack, systems architecture constraints, and non-negotiable soft skills using plain natural language or simply ingest raw engineering job briefs.

Prompt Parser Active
“Senior Rust Systems Engineer with experience in Raft consensus, zero-copy buffers, and high-frequency messaging.”
Rust ASTDistributed ConsensusTrajectory: Staff+
02

Autonomous Discovery

Autonomous Deep Sourcing (Agent Orion)

Agent Orion scans global code repositories, open-source pull requests, tech RFC publications, and niche registries to detect high-fit engineers who never post on job boards.

Orion Scanner GraphScanning 14.8k PRs/sec
github.com/tokio-rs/...+42 commits verified
arXiv: Distributed Log CompactionLead Author
03

Technical Vetting

Technical Audit & Predictive Scoring (Agent Vega)

Agent Vega constructs an AST graph of actual public commits, measures cyclomatic complexity tolerances, and assigns an unyielding, objective 0-100% Match Index score.

Candidate Dossier Index97% AI Match
Concurrency & Memory Safety99%
Architecture Scalability94%
04

High-Conversion Outreach

Seamless Outreach & Executive Introductions (Agent Hermes)

Agent Hermes synthesizes custom outreach referencing genuine technical milestones, syncs team calendar availability, and routes confirmed conversations into your Kanban pipeline.

Status: Conversational HandshakeCalendar Synced
AH
Introduction sent to Alex Morgan
30m Technical Sync scheduled for Thursday 2:00 PM
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