Fragmented supervision
Sessions, servers and tools are managed in separate places, with manual handoffs between them.
ANDIP
Distributed AI execution · architecture & validation stage
Infrastructure for the Next Generation of AI Agents
ANDIP is building a unified control plane to coordinate AI agents across distributed servers, manage resources, monitor execution, and improve operational reliability.
One Control Plane. Multiple Servers. Hundreds of AI Agents. Unified Execution.
In Development — Community Beta Target: Q1 2027
The operational gap
More agents create a systems problem before they create useful throughput.
Running agents across multiple servers adds coordination work that local agent tools do not solve on their own.
Resource contention
Agents compete for CPU, memory, browser slots, storage and model-provider quota.
Limited visibility
Queue state, ownership, progress, usage and cost can be difficult to see together.
Cost control
Usage limits and resource budgets need to shape placement—not arrive as an afterthought.
Fragile recovery
Lost workers can leave partial work, uncertain side effects and orphaned environments.
A proposed control layer
One operating model for agent work and the servers that run it.
ANDIP aims to connect existing VPS capacity to a centralized workflow and policy layer. It is designed to plan work, select a compatible worker, reserve resources, supervise an isolated attempt and collect verifiable artifacts.
Existing agent tools can remain focused on local execution. The distributed layer coordinates placement, durable state, permissions, budgets and recovery across machines.
Capabilities shown on this site are planned or under engineering validation unless a source badge explicitly says otherwise.
Platform capabilities
Infrastructure primitives, developed in measured phases.
ANDIP combines reusable foundations with new distributed components. The capabilities below are planned or awaiting validation.
Server orchestration
PLANNEDRegister workers, evaluate available resources, and coordinate fleet state.
Agent lifecycle management
PLANNEDTrack agent definitions, runs, attempts and terminal outcomes.
Distributed scheduling
PLANNEDPlace work according to capacity, compatibility, policy and budget.
Workload isolation
PLANNEDPrepare a separate workspace, process boundary and browser profile per attempt.
Monitoring & audit
PLANNEDCollect heartbeat, execution events, usage and reviewable operational history.
Resource controls
PLANNEDReserve capacity, apply quotas and prevent new work from exceeding policy.
Durable task processing
PLANNEDPersist task state and leases so recovery can reconcile interrupted attempts.
Failure recovery
PLANNEDUse fencing, checkpoints, idempotency and human review where outcomes are uncertain.
How ANDIP works
A traceable path from request to verified output.
Each stage is intended to create an explicit state transition, policy decision or artifact.
1
Request
validate scope
2
Plan
organize tasks
3
Schedule
reserve capacity
4
Execute
isolated attempt
5
Monitor
events & usage
6
Verify
check outcome
7
Complete
store & clean
Conceptual workflow only. The public website is not the production ANDIP control plane.
Workload categories
One infrastructure layer. Different execution adapters.
ANDIP is intended to support mixed agent workloads without requiring every task to use the same model, browser, or framework.
Coding agentsResearch agentsBrowser agentsData processingCustom automation
Browser and data collection remain subject to authorization, website terms, privacy, and access controls.
Built on foundations
Reuse what works. Engineer the distributed layer.
Agent Orchestrator provides local agent and session patterns. Jev Ultrafast is an optional browser adapter. The distributed registry, scheduler, durable queue, worker management, controls and recovery need additional engineering.
The upstream repositories are not owned by Technology and Media Services, LLC. ANDIP itself is not being represented as open source.
Read the attribution and integration notesA measured roadmap
From source verification to distributed validation.
Engineering proceeds through gates: a single-server vertical slice, multi-agent execution, multi-VPS placement, secure recovery, mixed workloads, then a reproducible concurrency test.
100 Concurrent Agents — Engineering Validation Target. This target remains subject to reproducible workload and failure testing; it is not a verified production capability.
Target
Community Beta Planned for Q1 2027
Development milestones and beta availability are targets and may change as engineering validation progresses. No fixed launch day is announced.
Early community interest
Join the Beta Waitlist Help shape the test plan—not a promise of beta access.
Developers, infrastructure engineers, researchers and automation teams can register their interest.