AI agents are digital workers. They need capacity planning, skill management, knowledge systems, and change control — just like human teams. MeetLoyd is the operational backbone for your AI workforce.
You have dashboards for every human team. What do you have for your AI agents?
Which AI agents are overloaded? Which are idle? Without utilization metrics, you're flying blind on your largest new workforce.
AI agents learn — but do they share? Without knowledge management, every agent starts from zero. Institutional knowledge stays trapped in individual conversations.
Every agent configuration change is a production deployment. Without CAB workflows and rollback capability, one bad prompt update takes down an entire team.
"Our AI agents are managed by the engineering team."
Today. When you have 200 agents across 15 departments, engineering becomes a bottleneck. MeetLoyd gives operations teams the workforce dashboard they need — capacity, productivity, SLA compliance — without requiring engineering involvement.
Workforce cockpit. No code required."How do you measure AI agent productivity?"
The workforce cockpit tracks: tasks per agent, tokens per task, completion rate, handoff latency, SLA compliance, and team-level benchmarks. You see top and bottom performers, utilization peaks, and capacity headroom. Real metrics, not vibes.
GET /compliance-cockpit/workforce/cockpit"We use Confluence for knowledge management."
Confluence stores human knowledge. MeetLoyd's knowledge system is agent-native: 80+ skills with progressive disclosure, quality scoring (adoption × freshness), knowledge gap detection, learning paths per team type, and retention metrics. Agents don't read Confluence — they query skills at runtime.
80+ skills. Quality scoring. Gap detection."How do you handle change management?"
Full ITIL change management: risk classification (low/medium/high/emergency), CAB summary with pending approvals and velocity metrics, blackout windows with violation tracking, change failure rate, and four-eyes enforcement on all configuration changes. Every prompt version, model change, and config update goes through the change pipeline.
ITIL-grade. CAB workflow. Four-eyes.Workforce management, knowledge systems, and change control — built for AI agents, designed for operations leaders.
Capacity planning: current load, peak/average utilization, headroom percentage, scaling recommendations. Skill gap analysis across teams. Productivity benchmarks: top/bottom teams, median response time, best success rate. SLA tracking with compliance percentages.
AI workforce capacity planning with skill gap analysis and productivity benchmarksQuality scoring per skill (0-100 from adoption and freshness). Knowledge graph showing category interconnections, orphaned skills, redundancies. Learning path recommendations based on team role vs current skills. Memory retention metrics: entries per agent, growth rate, stale detection at 30+ days.
Knowledge quality scoring with learning path recommendations and retention analyticsChange risk auto-classification. CAB summary dashboard: pending approvals, average approval time, 7-day and 30-day velocity. Blackout windows with CRUD management and violation counting. Change failure tracking: rollback detection, watchdog correlation, success rate. Unified change log across prompt, model, and config changes.
ITIL-grade change management with CAB workflow, blackout windows, and failure trackingCapacity planning. Knowledge systems. Change control. Operations at scale.