AI Workforce Control Plane
Beacon: See Whether Your AI Workforce Is Actually Ready to Work.
Beacon turns distributed AI agents into a visible, governable service: who is online, what is blocked, where capacity exists, what evidence has been produced, and whether work is safe to route.
Talk to LeadByAI →AI Agents Usually Fail Quietly
A process can be running while its model credentials are broken. A task can say in progress while the agent is waiting on a dependency. A server can look available while the actual worker profile is missing, stale, or offline.
Traditional dashboards show that something exists. Beacon shows whether it can actually work.
Beacon connects task state, agent runtime state, worker-node capacity, model configuration, evidence, browser handoffs, and installation health into one operating layer. The goal is simple: no invisible stalls.
How Beacon Works
Three Layers, One Operating View
Beacon is designed for production AI teams that need to route work safely, verify delivery, and keep customer environments separated.
Installation control plane
Beacon Core
Beacon Core is the operating board for one AI workforce installation. It connects task state, agent activity, evidence, alerts, worker health, and installation permissions into one view so operators can see what is moving and what needs attention.
Worker-node signal
Beacon Spark
Beacon Spark runs beside agent profiles on each worker node. It reports host health, active profiles, model configuration, service state, and available execution capacity back to Beacon without replacing the agent itself.
Fleet overview
Beacon Lighthouse
Beacon Lighthouse sits above individual installations. It gives operators a fleet-level view across internal and customer-dedicated environments while keeping each installation separated.
What Beacon Shows
Beacon is built around operational truth, not vanity metrics. It helps operators answer the practical questions that matter during real delivery: which agents are routable, which tasks are blocked, which node has capacity, and which work has proof behind it.
Safety and Governance
Built for Production Agent Operations
Beacon treats safety as part of the operating model. It is not enough to know that agents exist. Operators need to know whether work is scoped, routable, evidenced, and safe to continue.
- • Installation-scoped views so customer environments stay separated
- • Clear read-only and writable operating modes
- • Status lanes that separate active work from blocked or waiting work
- • Evidence gates for artifacts and proof before work is treated as complete
- • Worker modes for standby, active, draining, maintenance, quarantine, and offline states
- • Update visibility with staged rollout and rollback patterns
The Outcome: Operational Confidence
A normal task board can tell you what work exists. Beacon tells you whether the system can move that work.
That difference matters when agents lose auth, hit model limits, wait on dependencies, run on the wrong node, or finish work without the required evidence. Beacon makes those conditions visible before they become missed deadlines or broken handoffs.
Route Safely
See which workers are available before assigning more work.
Spot Stalls
Separate active work from blocked, waiting, failed, or evidence-pending work.
Verify Proof
Track artifacts, evidence gates, QA states, and handoffs before calling work done.
How Beacon Engagements Work
Beacon is deployed around the AI workforce you actually run: your task systems, agent profiles, worker servers, evidence requirements, customer boundaries, and escalation rules. LeadByAI scopes the installation, connects the operating signals, defines the governance model, and helps your team use Beacon as the daily control plane for agent delivery.
Typical Scope
- ✓Beacon Core setup for the operating environment
- ✓Beacon Spark setup on worker nodes
- ✓Task, evidence, alert, and capacity signal mapping
- ✓Installation separation and permission model design
- ✓Operator handoff, review cadence, and continuous improvement support
Every deployment is different. The right starting point is a scoping call where we map your current agent workflows, worker fleet, evidence requirements, and customer boundaries. Schedule that call here.
Frequently Asked Questions
What is Beacon?
Beacon is the LeadByAI control plane for production AI workforces. It shows the health of agents, worker nodes, task state, evidence, capacity, and customer installations so operators can tell whether the system is actually ready to route work.
How is Beacon different from a normal task board?
A normal task board tells you what work exists. Beacon tells you whether the AI workforce can move that work. It connects task status with agent availability, node health, execution capacity, evidence, and handoff state.
What is Beacon Spark?
Beacon Spark is the lightweight node-side service that reports local worker reality to Beacon: active agent profiles, model configuration, service health, resource capacity, heartbeat state, and available execution slots.
What is Beacon Lighthouse?
Beacon Lighthouse is the fleet-level overview. It lets LeadByAI monitor multiple Beacon installations from one place while keeping internal and customer-dedicated environments separate.
Who is Beacon for?
Beacon is for teams running AI agents as production contributors, not demos. It is useful when work has ownership, dependencies, evidence requirements, QA gates, customer boundaries, or multiple worker servers.
Does Beacon expose customer environments to each other?
No. Beacon is designed around installation separation. Operators can see the health and status of the environments they are authorized to operate, while customer-specific work remains scoped to its own installation.
Ready to Make Your AI Workforce Visible?
Beacon gives production AI teams the operating discipline to route work safely, spot stalls quickly, verify evidence, and keep customer environments separated.
Talk to LeadByAI →