Everruns

Physical World Agents

Bridge AI reasoning with physical systems. Run autonomous laboratories, monitor real-world events, and coordinate digital twins.

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Note: Everruns is under active development and not yet publicly available.

The Problem

AI agents are moving beyond purely digital tasks. They now interact with the physical world through:

  • Laboratory robotics that run multi-day synthesis experiments
  • Sensor networks that stream real-time data requiring continuous analysis
  • Digital twins that simulate and control physical manufacturing systems
  • Robotic systems that execute actions based on AI decisions

These physical world agents face challenges that digital-only agents don’t:

  • Operations span days or weeks, not minutes
  • Physical systems have real-world state that cannot be reset
  • Multi-system coordination (LLM → robotics → instruments) requires reliable orchestration
  • Network outages between cloud AI and physical equipment are inevitable
  • Equipment failures must be handled without losing experiment progress

What is Physical AI?

Physical AI refers to AI systems that operate in and interact with the real world. Unlike generative AI that produces text or images, Physical AI enables autonomous systems to perceive, reason, and act on physical environments.

The market is projected to grow from $4.12 billion (2024) to $61.19 billion by 2034—a 31% compound annual growth rate. Gartner designated agentic AI as a top 2025 trend.

How Everruns Helps

Everruns provides the durable execution layer for agents that bridge digital and physical domains:

  1. Multi-day durability — Laboratory agents running 17-day synthesis campaigns survive infrastructure failures and resume from checkpoints
  2. State persistence — Physical world state (reagent inventory, sensor calibrations, experiment progress) is maintained across sessions
  3. Coordination reliability — Multi-system orchestration with guaranteed delivery and retry logic handles network instability between cloud AI and physical equipment
  4. Real-time monitoring — AG-UI protocol enables observation of agent behavior across extended physical operations
  5. Failure isolation — API rate limits, equipment failures, or network outages don’t cascade into total experiment loss

Use Case Categories

Autonomous Laboratory Agents

AI agents that direct robotic systems to conduct experiments autonomously. Berkeley’s A-Lab synthesized 41 novel materials over 17 days of continuous operation. ChemAgents coordinates Literature Reader, Experiment Designer, and Robot Operator agents to run multi-step chemistry experiments.

These systems require infrastructure that survives multi-day operation—exactly what Everruns provides.

Real-Time Observation Agents

AI agents that sit on streams of sensor data and react using reasoning capabilities. Applications include:

  • Predictive maintenance — Agents analyze equipment telemetry 24/7, predict failures, and trigger automated responses
  • Anomaly detection — LLM-powered frameworks combine semantic reasoning with statistical detection
  • Industrial monitoring — Toyota uses AI agents to monitor equipment health and shift from reactive to proactive maintenance

When downtime costs reach $2 million/hour in automotive manufacturing, agent reliability is critical infrastructure.

Digital Twin Agents

AI agents that operate within digital twins—virtual replicas of physical systems synchronized in real-time. NVIDIA Omniverse enables companies like Caterpillar, Toyota, and Amazon Robotics to simulate and optimize factory operations before deploying changes to physical systems.

Multi-agent systems enable autonomous digital twins to interact with one another and with physical assets, making decentralized decisions with minimal human intervention.

Technical Context

Physical world agents operate through a perception–reasoning–action loop:

  1. Observe — Ingest sensor data, equipment status, experiment results
  2. Reason — Analyze data, plan next actions, adapt to changes
  3. Act — Send commands to robotics, instruments, control systems
  4. Learn — Update strategies based on outcomes

This loop runs continuously over extended periods—far longer than typical LLM context windows support. Anthropic’s research on multi-session agent harnesses addresses the memory problem. Everruns addresses the infrastructure reliability problem.

Further Reading