Lifecycle stage — Build
Almost nobody has shipped a multi-agent system at production scale. The distance between an agent prototype that works in a notebook and a system that runs continuous operations over a robotics fleet, an AGV yard, an energy grid, or an industrial control network is where every other team stalls — and it stalls for reasons that are not obvious until you have built one. For cyber-physical stacks, the challenge is compounded: the agents do not just coordinate software tasks, they orchestrate interactions with physical systems — reading from sensors, dispatching actuator commands, managing fleet-level state, interfacing with SCADA and MES. This is the Build and Ship stages of the Hyperion Lifecycle, compressed into a 12-week embedded engagement for teams that already have an agent prototype with real users and real physical-system interactions, and need to industrialise it. I architected Auralink — 1.7M lines of production code, approximately 20 autonomous agents resolving 78% of incidents without human intervention, arXiv 2603.08736. The work I will do with your team is the same work I did with mine, adapted to your codebase, your agents, and your physical-operational constraints.
Every agent demo works in a notebook and falls apart the first time it interfaces with a real physical system at production concurrency. The tutorial uses synchronous calls, mocked sensor data, and a single happy-path trajectory. Production means dozens of agent sessions in parallel, each making real tool calls against live sensor feeds, SCADA endpoints, MES APIs, or fleet management systems — with real failure modes: sensor dropout, actuator command timeout, OT network partition, MES transaction conflict. The naive orchestration pattern that looked clean in the demo becomes a thundering herd of retries, deadlocks, and half-committed physical-system state.
Evaluation methodology from single-turn LLM work does not extend to multi-step agent trajectories that interact with physical systems. You can evaluate a prompt. You cannot yet evaluate a 14-step autonomous inspection trajectory where the fifth step chose the wrong sensor to read, the ninth step dispatched an actuator command based on stale state, and the final report was still technically coherent. Failure modes in agent trajectories that touch physical systems compound across steps and the consequences of a wrong tool call can be physical, not just logical.
Cost-per-task explodes unpredictably because each agent step multiplies both token burn and physical-system API calls. A single fleet-management request triggers a plan, which triggers sensor queries, which trigger sub-agents, which trigger MES lookups, which trigger actuator commands. Your per-session cost is now 40x a regular LLM call plus the latency budget consumed by physical-system round-trips. You have no instrumentation to answer the CFO's question or the safety engineer's question about why one anomalous session triggered seventeen actuator commands.
When an agent does something wrong that affects a physical system, you have no observability stack that tells you which step caused it. The operations team reports that an AGV was dispatched to the wrong bay, or a maintenance alert was suppressed incorrectly, or a substation relay was queried in a sequence that violated the protection scheme. Your logs show the final output and nothing else. You cannot reproduce the trajectory, cannot tell whether the bug is in the planner, the tool router, the sensor-data interpretation, or a SCADA interface timeout. Every incident becomes a multi-day forensics exercise.
The engagement runs in four three-week phases. I work embedded with your engineering team — your engineers build, I bring the topology decisions, the eval methodology for physical-system interactions, and the observability patterns from Auralink. By the end of week twelve your team operates the system without me.
I go deep on your current prototype — the agent graph, the tool inventory including physical-system interfaces (SCADA, MES, sensor APIs, fleet management, actuator command paths), the state management strategy, and the failure modes you have already hit. I produce a written topology design: which agents, which responsibilities, which communication patterns, which state boundaries, which failure-isolation zones, and which physical-system interactions require safety-interlock design or human-in-the-loop escalation. The design is specific to your domain and your physical-operational constraints.
Your engineers implement the topology. I work alongside them on the harder calls — the orchestration primitives for long-running physical-system tasks, the state machine for fleet-level coordination, the retry and compensation logic for actuator command failures and sensor dropout, the human-in-the-loop escalation paths where safety interlocks require operator confirmation. We ship incrementally against real physical-system traffic from week five onwards, not a big-bang cutover in week seven.
Trajectory-level evaluation built for cyber-physical agent systems — per-step evaluation of sensor-read accuracy, actuator command correctness, fleet-state consistency, and SCADA interaction safety. Ground-truth trajectories for regression testing. LLM-as-judge with calibrated prompts for the linguistic components; deterministic assertion-based evaluation for the physical-system interaction components. Per-step token accounting plus physical-system API call accounting so the full cost-per-task is visible and explainable.
The observability stack your on-call engineer and operations team will use when the pager goes off — trajectory traces linked to physical-system events, per-step sensor reads and actuator commands, tool-call inputs and outputs, fleet-state diffs, SCADA interaction logs, token accounting, latency breakdowns. Runbooks for the top-10 incident types, including incidents that involve incorrect physical-system interactions. Working sessions with your SRE and operations teams so they own the alerting thresholds, the dashboards, and the incident response playbooks.
Manufacturers deploying fleet-intelligence agents over robotics cells or AGV yards. Energy utilities building autonomous grid-monitoring or substation-inspection agents adjacent to SCADA. Logistics operators deploying warehouse-vision and route-optimisation agent systems over AMR fleets. Any operator where an agent prototype already interacts with physical systems — sensors, actuators, fleet management, SCADA, MES — and the engineering team has already hit the wall between 'agent demo works in the lab' and 'agent system operates reliably in the production environment.' This is not for teams without LLM production experience or without a physical-system codebase to integrate with; those organisations need the Strategy Sprint or the Physical AI Deployment service first.
Not much. The framework is a vehicle — the decisions that matter are the topology, the state management for physical-system interactions, the eval methodology for trajectories that touch physical systems, and the observability. In week one I assess whether your current framework is the right vehicle for a cyber-physical production workload; sometimes it is and we build on it, sometimes a specific bottleneck — typically long-running physical-system tasks or fleet-level state management — argues for a migration. I make that call with evidence.
Safety interlocks for physical-system interactions are designed into the topology in week one, not added as an afterthought. The topology design explicitly identifies which agent tool calls require human-in-the-loop confirmation (actuator commands above a threshold, SCADA write operations, fleet rerouting decisions that affect safety zones) and builds the confirmation path into the state machine. The goal is not to block the system — it is to ensure the right actions have the right confirmation requirements built in from the start.
A senior AI engineer available in 2026 has almost certainly not shipped a production multi-agent system that interfaces with physical systems at scale. I have done it at 1.7M lines of code and 78% autonomous resolution, in a system where agents operate under partition-tolerance requirements that directly map to industrial and energy environments. The pattern recognition for cyber-physical agent topology, eval methodology, and observability is not available on the contractor market yet.
No. Agent topology, eval harness for physical-system trajectories, and observability are each three-week problems done well. For cyber-physical systems, compressing the topology phase produces a system that handles the happy path and fails under the first real physical-system fault. If twelve weeks is not available, the right engagement is Pilot-to-Production Hardening, which covers the production-readiness work without the full topology redesign.
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