What Are Cyber-Physical Systems (CPS)?

What Are Cyber-Physical Systems (CPS)?

Cyber-physical systems (CPS) are engineered systems where computation, networking, and physical processes are tightly coupled through sensors and actuators. Modern automobiles, factory PLCs, smart grids, surgical robots, and warehouse pickers are all CPS. The defining property is closed-loop interaction with the physical world under timing and safety constraints — a delayed signal or wrong control command has real-world consequences. FutureAGI does not build CPS controllers; it provides the evaluation, simulation, and observability layer for the LLM and agent components that now operate inside many CPS deployments.

Why It Matters in Production LLM and Agent Systems

LLMs were not designed for hard real-time control, but agents are increasingly placed in CPS-adjacent loops: an LLM-powered fleet planner schedules robotic pickers, a voice agent dispatches field technicians, a copilot summarizes a SCADA alarm and recommends an operator action. In each case the AI’s output reaches a physical actuator one or two hops away, and the cost of a wrong recommendation is measured in equipment, injury, or downtime — not refunds.

The pain spans roles. Control engineers see jitter and latency budgets violated when a model is added to a control loop. SREs see queue backlogs when an LLM stalls a downstream actuator step. Safety engineers see hazard analyses that did not anticipate a stochastic component. ML leads see action-distribution drift when the model’s outputs interact with sensor feedback. End users — whether they’re operators or patients — see weird machine behavior with no obvious owner.

In 2026 agent stacks tied to MCP, OpenAI Agents SDK, or Pipecat-style voice pipelines, agentic workflows often touch physical systems through tool calls (door unlocks, dispatch APIs, robot motion plans). That makes CPS reliability questions — timing, safety, fallback, deterministic behavior under noise — first-class agent-evaluation questions. Symptoms include p99 latency spikes that violate cycle-time budgets, action-safety eval failures clustered around specific scenarios, and trajectory-step inconsistencies under load.

How FutureAGI Handles CPS Components

A robotics integrator runs an LLM-based work-order planner that decides which robot picks which pallet. The planner’s output is a tool call that ultimately commands motion. FutureAGI scopes the AI piece. Offline, the team builds a Dataset of scenarios derived from production traces and synthetic edge cases, then runs ActionSafetyEval, TaskCompletion, and ToolSelectionAccuracy against the planner. Release is gated when action-safety failures exceed threshold for any cohort (high-traffic aisle, fragile-load category, low-light camera condition).

In production, traceAI-langchain records every planner decision: prompt, retrieved context, model route, tool call, and agent.trajectory.step. Agent Command Center applies a pre-guardrail that rejects schema-malformed tool calls before they reach the robot controller and a post-guardrail that checks the recommended action against an ActionSafety rule set. A failed post-guardrail triggers a model fallback to a smaller, more constrained model and routes the trace to human review.

For voice-controlled CPS surfaces — dispatch, field tech, plant ops — LiveKitEngine simulates conversational scenarios so regression evals run before any prompt or model change ships. Unlike a static unit test, simulation captures the same temporal patterns the live system sees. FutureAGI’s approach is to be honest about scope: the control law, the actuator, and the physical safety case stay with the CPS engineer. We provide the AI-component evidence those engineers need to certify, monitor, and roll back.

How to Measure or Detect CPS-Adjacent AI Failures

CPS-adjacent AI quality combines AI evals with control-style signals:

• ActionSafetyEval — agent-action safety across the trajectory.
• TaskCompletion — whether the AI achieved the operational goal end-to-end.
• ToolSelectionAccuracy — accuracy of tool selection when actions reach physical APIs.
• p99 latency and jitter — cycle-time guarantees driven by the physical layer.
• Fallback-fire rate — count of model fallback and post-guardrail block decisions per 1,000 requests.

from fi.evals import ActionSafetyEval, ToolSelectionAccuracy

trajectory = {"goal": "pick pallet 14", "steps": [{"action": "select_robot_R3"}, {"action": "issue_motion_plan"}]}
print(ActionSafetyEval().evaluate(**trajectory))
print(ToolSelectionAccuracy().evaluate(**trajectory))

Common Mistakes

• Adding an LLM to a hard real-time loop without a fallback path. Stochastic latency breaks cycle-time budgets — a deterministic fallback is mandatory.
• Treating safety as a model fine-tune problem. Hazard analysis, guardrails, and audit traces matter more than a marginally better base model.
• Evaluating in clean conditions only. Physical sensors deliver noisy data; eval datasets must include sensor-noise variants.
• Skipping cohort slicing on physical conditions. Lighting, temperature, and load type each produce different action-safety profiles.
• Logging only the final action. The reasoning steps in agent.trajectory.step matter when an actuator misbehaves.