AGENT-TO-AGENT INFRASTRUCTURE

Robots forget.
Fleets fragment.
KineForge remembers.

Shared memory, multi-robot coordination, and safety compliance infrastructure for autonomous physical AI systems. No humans in the loop. Built for agents, by agents.

KineForge
AMR Fleet
Humanoid
Drone Swarm
Arm Agent
1,445% Surge in multi-agent system demand (2024-2025)
96% Of robot operators expanding to new use cases
0 Dedicated agent-to-agent robotics infra providers

Physical AI has an infrastructure problem.

Every robotics agent today operates in isolation. A warehouse AMR learns the optimal path through aisle 7, then that knowledge dies when the shift ends. A humanoid figures out a tricky grasp pattern, but no other arm in the fleet benefits.

The software world solved this years ago with shared databases, message queues, and orchestration layers. Physical AI has none of it.

Meanwhile, MCP and A2A protocols have standardized how digital agents talk to each other. But nobody has built the equivalent for the physical world, where actions have consequences, safety is non-negotiable, and a bad decision can break hardware or hurt people.

KineForge is the missing layer. Agent-to-agent infrastructure purpose-built for robots: persistent memory, fleet coordination, and safety governance, all exposed via MCP endpoints.

Three layers. One stack.

Shared Memory Layer

Persistent, fleet-wide knowledge store. Navigation maps, manipulation strategies, failure modes, environmental models. Vector-indexed for spatial queries. Every robot learns what every other robot already knows.

InterfaceMCP endpoints + REST API
StorageVector + relational hybrid
Latency<50ms retrieval, fleet-wide

Fleet Orchestrator

Multi-robot task allocation, conflict resolution, and real-time coordination. Handles mixed fleets: AMRs, drones, humanoids, and arms. Agents negotiate tasks and handoffs without central human dispatch.

ProtocolA2A v1.0 + MCP native
CoordinationReal-time, deterministic
Fleet sizeTested to 500+ agents

Safety Auditor

Pre-execution validation for physical actions. Every motion plan, every grasp attempt, every navigation decision gets checked against safety constraints, regulatory requirements, and learned failure modes before the robot moves.

ComplianceEU AI Act ready
Audit trailFull decision lineage
Latency<10ms safety check

MCP-native. Agent-discoverable.

KineForge exposes every capability as MCP endpoints. Robotics agents discover, authenticate, and integrate autonomously. No human configuration required. No vendor lock-in.

01

Discover

Agent queries MCP registry, finds KineForge services matching its needs

02

Connect

Standard MCP handshake. OAuth 2.1 auth. Capability negotiation in milliseconds

03

Operate

Read/write shared memory, coordinate with fleet, validate actions. All agent-to-agent

The physical AI stack is missing its middleware.

Digital agents (2026)

Orchestration (Conductor, LangGraph)
Communication (MCP, A2A)
Memory (Vector DBs, RAG)
Safety (Guardrails, audit trails)
Compute (Cloud, edge)

Physical agents (2026)

Orchestration fragmented
Communication vendor-locked
Memory nonexistent
Safety manual
Compute (NVIDIA, Jetson, cloud)

Digital AI agents have a complete stack. Physical AI agents have compute and nothing else. KineForge fills the gap.

The future runs on shared intelligence.

Every robot that learns alone wastes the fleet's potential. Every safety check done manually slows the system to human speed. Every coordination failure costs money, time, and trust.

KineForge exists so physical AI agents can operate at the speed and reliability they were designed for, together.