The AI Workforce OS.
One system for people and AI agents.
Today HRPulsar manages the human side of an AI-first company. V2 extends the same skills-first core to AI agents — one graph of who (and what) can do the work, one place to route it, one way to govern it.
This page is a roadmap — it describes where HRPulsar is going, not what ships today.
V2 grows from a platform that already ships.
The vision is not a blank slate. The skills-first core it builds on — the workforce model, the capability data, the AI layer — is live in production now.
Talent platform v1.15.4
Competencies, assessments, development, recruiting, grading, exams, talent market and analytics — in the product and the demo.
AGPLv3 core
Self-host on your infrastructure or use the cloud — the core platform is open and inspectable.
AI Fluency in the product
A methodology based on Reid Hoffman's three levels, wired into assessments and development plans.
From AI readiness to execution.
Most organizations can assess readiness. Running day-to-day work across people and AI agents is a different problem:
Unknown agent ownership
AI agents do real work, but nobody owns them the way managers own teams.
No shared skill model
Human skills and agent capabilities live in different worlds — so nobody sees the full capacity of a team.
Thin governance
Quality controls and policies that exist for people rarely extend to the agents working next to them.
Weak link to outcomes
Readiness scores are easy. Connecting capability to delivery speed, cost and quality is the hard part.
An operating layer for the hybrid workforce.
Six pillars. Where a pillar builds on something that already ships, the chip says so; the rest is planned work, in the open.
Workforce Graph
Employee profiles and competencies ship today. V2 adds agent profiles with owners, capabilities and scopes — one graph for the whole workforce.
Capability Mapping
One skills model already covers people — frameworks, assessments, readiness. V2 extends the same model to what agents can do.
Work Orchestration
Route tasks to the right executor — human or agent — by capability, policy and load.
Quality & Risk Controls
Review loops, policy checks and escalation paths designed into every hybrid workflow.
Development Loops
Development plans for people ship today. V2 closes the same loop for agents: evaluation, tuning, and re-deployment.
Outcome Analytics
Speed, quality and unit economics of hybrid teams — capability data connected to what actually got delivered.
Governance by design.
AI at scale needs controls built in from the start. V2 is designed so that every decision is traceable: who did what, with which model, under which policy.
Policy engine
Routing rules, escalation paths and approval gates defined per workflow.
Compliance
Data boundaries, access rules and regulatory requirements enforced at runtime.
Auditability
A full trace of human and agent actions — model, input, output, reviewer.
Access control
Role-based permissions for people, scoped capabilities for agents.
The numbers that will matter.
These are the dimensions the OS is designed to move. No invented benchmarks here — we will publish real numbers as pilot teams produce them.
Time-to-delivery
How much faster work moves when routing includes agents.
Cost-per-task
What execution costs when repeatable work is shared with agents.
Quality pass rate
Whether output holds up under review loops and policy checks.
AI-assisted coverage
How much of the repeatable work actually runs through agents.
See the platform v2 grows from.
The best way to judge a roadmap is to look at what already ships. Open the demo — or talk to the founder about where this is going.