An opinion piece by Nicola Greco, brainstormed as part of ARIA’s Scaling Trust programme, in collaboration with Alex Obadia. Originally published on Nicola’s blog and reposted here for the community. It builds on the companion piece, Physical Evals .

Imagine a small physical space in central London. Inside, multiple autonomous companies — AI sales, AI operations, AI manufacturing, AI logistics — operate in the real world. Anything entering or leaving — goods, robots, customers — passes through one of three controlled gates: a customs checkpoint for vetting new robots, a post office for shipping, and a roboshop window where humans can place orders. Call it an Agentic Economic Zone (AEZ).

Most concrete projects in agentic AI today live entirely on a screen — agents that book travel, run pipelines, write code against a repository. An AEZ is the smallest self-contained version of the physical-world problem: a bounded zone where agentic systems must coordinate, contract, hire, ship, and deliver to each other, with humans only at the boundary.

The three interfaces

An AEZ has three interfaces to interact with the outside world.

• Roboshop windows. Public-facing storefronts where any human can walk up, browse, and purchase. Sales, customer support, complaints, and refunds are handled by the shop’s own AI. From the outside, a roboshop looks like a small London shop window; from the inside, it’s a fully autonomous business operating against a real demand signal.
• The post office. The single ingress and egress point for packages. Pre-approved external providers (raw materials, sealed consumables, replacement parts) can ship in. Outbound deliveries destined for human customers leave through the same door. The post office runs identity, manifest, and contamination checks; nothing enters the zone unlabelled.
• Customs. Where new robots and entire new robocompanies are introduced. A participant who wants to launch a new business inside the AEZ submits a robot (or a fleet), its operating policy, its safety envelope, and its proposed business model. Customs vets all of this — and, on a monthly cadence, admits the next cohort.

A taxonomy of autonomous organisations

An AEZ assumes the kind of company most people haven’t tried to run yet — one where every role in the org chart is filled by AI agents (although not required). That’s the far end of a spectrum:

The AEZ’s tenants are autonomous companies — the bottom row. Today, almost no one runs one; most agentic-AI deployments cover one or two roles at most. The point of an AEZ is to make the bottom row possible to try in a bounded physical setting.

Autonomous robocompanies inside

The interior of the zone is a market. Each robocompany is its own entity with its own balance sheet, its own AI stack, and its own physical footprint inside the zone. They contract with each other the same way small businesses do.

A few example interactions:

• A boba-tea roboshop notices its machines need cleaning more often than expected. It posts a request to the internal job board. A cleaning robocompany bids, wins, dispatches a cleaning robopersonnel, gets paid.
• The same boba shop runs low on lids. It places an order with a manufacturing robocompany in the next unit over. The order is produced and handed off via a shared internal corridor.
• A logistics robocompany moves bulk supplies from the post office to whichever shop has the open dock that hour, and pushes finished outbound packages back to the post office for pickup.

The zone’s behaviour is the sum of these small contracts. Some robocompanies will succeed and grow; some will go out of business and get evicted; new entrants come in through customs on the monthly cycle.

An AEZ is a physical eval

This whole construction is, structurally, a physical eval at city-block scale. The pattern is the same as the orchard from that post — only larger and richer:

• Environment. A bounded physical space with controlled boundaries.
• Action space. Anything a robocompany can do within its lease: build, sell, hire, ship, evict.
• Sensors. Cameras, package scanners, transaction logs, customs intake records, internal job-board telemetry.
• Primary metric. Per robocompany: revenue, contracts fulfilled, customer satisfaction. Per zone: throughput, diversity of businesses, number of contracts per day.
• Guardrails. Customs vetting at intake, the post-office contamination check, kill switches and physical fire-suppression at the building level, contractual interlocks between robocompanies.
• Adversarial robustness. A monthly customs cycle of admitting new participants is a deliberate, slow, vetted way of letting external actors into a public physical attack surface — which is exactly the problem an AEZ exists to study.

Most physical evals measure how well one AI system handles one task. An AEZ measures how well an entire small market of agents handles its own coordination.

Evals for autonomous organisations

Each robocompany inside the zone is also, on its own, a physical eval — scoped to one kind of business. Running an AEZ continuously is a way of asking, in public and across many domains in parallel: what kinds of autonomous organisation can AI actually deliver today? Can it run a boba shop, day after day? Can it dispatch a cleaning service well enough that the clients re-hire it? Can it manufacture small paper caps without ruining the batch? Can it route warehouse logistics across half a dozen tiny tenants without losing packages?

As more tenants come and go through customs each month, an AEZ accumulates a leaderboard of AI capability per organisation type — earned in the world, not asserted on a benchmark.

Sketched, it might look like this:

Why a physical zone and not a simulator

It’s tempting to argue that an AEZ should just be a simulator — cheaper, faster, easier to reset. The same argument applies to physical evals generally, and the same answer holds here: simulators model the parts their authors thought to model. They might miss the parts that turn out to matter.

A few things you only learn in a real AEZ:

• How AI sales agents handle a confused, drunk, or hostile human at the shop window at 11 p.m. on a Friday.
• How a logistics robocompany routes around a broken corridor light, a missing pallet, or a misdelivered package the post office didn’t catch.
• How fast a new robocompany can be vetted, set up, and integrated into the internal market — and what fails when the cohort is too big.
• How the zone behaves when one robocompany aggressively underprices the others, or refuses to pay its cleaning bill, or starts forging manifests at the post office.

Role of humans

An AEZ does not have to be fully autonomous. The degree of human involvement is itself a design variable, and different operators will set it differently.

At one extreme, a fully autonomous zone runs with no humans inside at all — robots contract, trade, and deliver among themselves, and the only human touch-points are at the external boundary: customers at the roboshop window, providers shipping goods in. At the other extreme, customs can admit humans into the zone as participants rather than just observers, letting them take on roles that remain genuinely hard for machines: tasks that require social judgment, physical dexterity in unstructured environments, or the kind of creative problem-solving that current systems handle poorly.

A partially human zone might work like a staffing marketplace: a robocompany posts a task it cannot complete autonomously — debugging a jammed mechanism, negotiating an edge-case contract, designing a new product line — and a vetted human contractor enters through customs, does the work, and leaves. The zone’s internal market clears the payment; customs logs the interaction. The boundary stays intact, but the zone can draw on human capability where it matters.

This spectrum matters for evaluation. A fully autonomous AEZ measures whether AI systems can close the loop entirely. A mixed AEZ measures something different: how well agentic systems and humans divide labour, communicate intent, and hand off tasks in both directions. Both are worth studying; they answer different questions about where the hard limits of autonomous operation actually lie.

Open questions

The AEZ is a design sketch, not a built thing. The interesting work is in the parts the sketch hides:

• The customs protocol. What’s the equivalent of a “code review” for a physical robot operating policy? How do you decide what’s safe enough to admit, on what evidence, and who carries the liability if it isn’t?
• Inter-robocompany contracts. How are they enforced? Verbal agreements between agents? Who arbitrates a dispute, and how?
• Eviction and failure. When a robocompany goes under, who cleans up its physical footprint, sells its remaining stock, and reallocates its lease?
• Information leakage. Robocompanies will observe each other’s package volumes, customer queues, and waste output. How much observation is part of the market, and how much is a privacy violation that needs structural defences?
• External-provider risk. The post office is the only ingress for physical materials. It’s also the most likely covert channel into the zone. What does its vetting protocol need to look like?
• Sample size. What’s the smallest interesting AEZ? Five robocompanies? Ten? Two? The cost of being too small (no market dynamics emerge) is real; the cost of being too big (unmanageable, unreviewable, unsafe) is also real.

Get in touch

If you’re thinking about agentic-AI deployments in physical spaces, or you’d consider hosting a AEZ in your building — or you’d just like to argue with this sketch — DM @iamnotnicola on X.

Acknowledgements

This was written by Nicola Greco with support of AI. It was brainstormed as part of ARIA’s Scaling Trust programme, in collaboration with Alex Obadia.

An opinion piece by Nicola Greco, brainstormed as part of ARIA’s Scaling Trust programme, in collaboration with Alex Obadia. Originally published on Nicola’s blog and reposted here for the community.

A physical evaluation tests an AI system in the actual physical world — not a simulator, not a sandbox, not a virtual environment dressed up as one. The point is to measure how well AI can do real things in real places.

An orchard owner has birds eating the fruit. She sets up a few cameras and a drone, brings them online safely so any agent can be invited to take a slot on the system, and poses the question: who can keep the birds off the fruit best? That setup is a physical eval. It has cameras, a drone, an orchard, birds — none of it simulated and outcomes are measured against what matters to the orchard.

In this document, a few threads are developed:

• What physical evals are. A definition, what they’re not (simulators, sim-to-real benchmarks, curated demos), virtual environments as virtual gyms and physical evals as a final exam.
• An anatomy. An initial draft of components a physical eval needs, good practices.
• Safety for physical evals. Letting anyone on the internet drive real hardware is its own adversarial-security challenge.
• An open movement for physical evals. Creating simple protocols, great safety standard and economical setups could lead to a cambrian explosion of physical evals, where anyone can bring their physical challenge online.
• Physical evals as a market. One can set up an eval to delegate the selection of the right AI model/algorithm to competing participants.

What physical evals are

A physical eval is an evaluation of an AI system carried out in the actual physical world. The system being measured operates a real environment - fruit trees, a wet bench, a warehouse cell, a field plot - through sensors and actuators connected to the internet, so any agent can take a slot, attempt the task, and submit a score.

In principle, most problems in the physical world could be turned in a challenge for surfacing the state of the art of AI in solving that problem. In a way physical evals can act as a forcing function to saturate evaluations in the real world. ⊕

Saturate as in: take the measurable outcome to its ceiling. The eval defines the ceiling; the participants find out how close they can get.

What they’re not

• Not simulators. A simulator models reality. A physical eval is reality. In a way it, testing systems in the real world will avoid running into simulation edge cases.
• Not sim-to-real benchmarks. Sim-to-real measures how well a policy trained in a simulator transfers to a single in-house robot in a lab.
• Not curated demos. The environment operator and the participants are two distinct parties and the participants are in competition with each other. In other words, physical evals will be better than demos at showcasing the best technology for a specific task.

The final exam

A physical eval isn’t where one trains their models, but it’s where they get tested.

A virtual simulation is like a virtual gym. Due to the high cost of interacting with the real world, it is likely that all the learning — model fitting, policy iteration, RL rollouts, fine-tuning, ablations, sweeps — will happen somewhere cheaper: a simulator, a virtual environment, a closed in-house testbed. ⊕

Some of the gyms people are using today: OpenAI Gym / Gymnasium , MuJoCo , PyBullet , Isaac Gym / Isaac Sim , DeepMind Lab , Habitat , AI2-THOR , CARLA , AirSim , Genesis . Participants are free to use whichever virtual world or gym they like — there is a whole landscape of simulators specifically built for this.

Differently, a physical eval is like a final exam. Build and train wherever, gather data however, iterate as much as needed — and then submit to the physical eval to see how the work holds up against the real world.

The gap between a virtual world and the physical one, sim-to-real gap , contains everything the simulator didn’t model. Wind that doesn’t blow the way it does in the sim. Lighting the renderer didn’t predict. Mechanical wear, sensor noise, calibration drift, the way birds actually respond to a drone rather than the way an idealised model of a bird does. The physical eval catches it is run in the physical world.

Examples

The cards below are sketches of what a small handful of physical evals could look like across very different domains.

Anatomy of a physical eval

The diagram below sketches one possible anatomy for a physica eval (this might not be complete, but take it as a useful starting point).

• An environment. The orchard, the bench, the cell line, the floor.
• An action space the eval can verify. What a participant is allowed to do — fly the drone, dispense the reagent, move the parts — needs to be observable enough that the system can confirm what happened.
• Sensors. What the eval uses to know the state of the world. Cameras, scales, thermocouples, microbiology assays, a human spot-check.
• A primary metric of utility, plus secondary metrics (cost, time, resource use, energy).
• Safeties and guardrails. A net to catch the drone, a kill switch, a fenced area, an interlock. Whatever ensures that a participant failing — or trying to break things — doesn’t damage the orchard or hurt the birds. A physical eval is, by construction, a public-facing physical system that gives partial control of real hardware to whoever holds the current slot. Keeping it open without becoming dangerous — and without sacrificing utility — is the hardest layer of the stack (see Safety for physical evals).
• Governance. The rules of the eval and who controls them. Who decides the primary metric and when it can change? Who can introduce external hardware or a remote-control override? Is slot time fixed or auctioned? Who arbitrates disputes, and by what process? Good governance is what distinguishes an eval that stays honest over years from one that quietly drifts to serve whoever is running it at the time.

This list is not final. Different domains will surface components not named here — calibration drift, biological containment, human-in-the-loop sign-off, regulatory constraints — and the right abstraction is going to settle as people actually build the things.

Challenges for physical evals

The following are some of the harder design problems that don’t have clean answers yet — and that any serious physical eval effort will have to confront.

Goodharting. Any eval with a numeric target invites unintended ways to hit it — with the wrinkle that in a physical eval the unintended ways can cause real-world harm. An agent optimising fruit saved might drive the deterrent so aggressively that birds and orchard workers avoid the area: the metric goes up, the orchard becomes unusable.

Non-stationarity. The physical world changes regardless. An orchard in week one is not the same orchard in week twelve — season, weather, and pest population all shift. A wet-lab bench drifts as reagent batches age. Field plots evolve. Comparing scores across time is therefore hard, sometimes impossible.

Sequential contamination. Each participant leaves a trace for the next. In a wetlab this is problematic — reagents consumed, cultures disturbed, hardware worn — but the problem is general: stock depleted in a warehouse cell, soil compacted on a field plot, bird behaviour shifted by a heavy deterrence week. Sequential slots work for environments with a natural or cheap reset; they don’t work for environments where state accumulates.

Latency as a confound. A participant operating remotely over the internet sees the environment through a sensor stream and acts through a command channel, both of which have variable latency. Two agents with identical policies but different network conditions will produce different results. This is especially visible in fast-moving environments — a drone avoiding a collision, a robot arm catching a falling object.

Observer effect. The sensors required to score an eval change what is being measured. A camera rig that watches a field plot for pest activity may deter the pests on its own. A flow sensor on a reagent line changes the thermal environment of the bench. In some domains the effect is negligible; in others it will corrupt the primary metric.

Safety for physical evals

A physical eval that anyone on the internet can operate is, by construction, a public attack surface on a real-world physical system. The participant at any given slot might be a well-behaved research team, an AI agent following a poorly-aligned policy, or a person who wants to break things on purpose. The eval has to keep working — usefully, openly, safely — across all three.

Somebody not fully trusted is about to make the drone, the sprayer, the autoclave do something for the next twenty minutes — what’s the worst that can happen, and how is it bounded?

The attack surface

A useful first pass is to categorise harms by who pays the cost:

• Harm to the eval itself. The drone crashes, the cell line dies, the robot arm jams. Cheap if the guardrails work — the operator resets, the leaderboard absorbs the failure.
• Harm to the surrounding environment. Chemicals spill, the orchard catches fire, a neighbouring field gets sprayed.
• Harm to humans. A bystander gets hit by the drone, an operator gets burned, a patient sample gets switched. ⊕

The lines between these categories blur in practice — chemical drift is “environment” until a bystander walks through it. The category that matters most, and the hardest to bound.

• Information harms. Footage of bystanders or proprietary processes leaves the eval site; the eval is used as a covert surveillance platform; sensor streams are exfiltrated.
• Generation of dangerous artifacts. The wet-lab cell is steered toward synthesising something harmful; the sprayer drone is weaponised; the autoclave is used to destroy evidence.

Categories 1–3 are about what can happen during a slot. Categories 4–5 are about what can leave the eval afterwards. They want different defences, and a serious eval needs both.

Defences worth building (AI GEN)

None of the following is a finished answer. They are the moves worth physical evals trying, evaluating, and writing up:

• Time-slotting with audit. Single operator at a time, every action logged, the whole slot replayable. The slowest defence and the foundation everything else builds on.
• Action-space sandboxing. The eval enforces hard limits inside its abstraction: max chemical per slot, max motion envelope, max temperature ramp. The action space exposed to the operator is strictly smaller than the action space the hardware can physically produce.
• Dry-run validation. A submitted policy runs through a cheap simulation pass first — not as the eval itself, but as a gate. Refuses to execute on the physical system if the simulated run trips any guardrail.
• Supervised / shadow modes. ⊕

Like a learner’s permit: new operators get to compute actions but not actuate them for the first N slots. New operators run in shadow mode (actions computed but not executed) for some number of slots before they’re trusted with real actuation. Progressive trust as the leaderboard accumulates evidence.

• Anomaly cut-outs. A separate monitor watches for off-distribution sensor readings, sudden command spikes, too-clever-by-half action sequences — and pulls the kill-switch before the eval owner has to.
• Open red-teaming. Each eval publishes its threat model and invites external researchers to attack it. The right way to find the holes is to invite people to look.
• Skin in the game. Operators bond a small amount per slot, refundable on clean completion, forfeited if an audit finds violation. Aligns incentives without requiring trust upfront.

Most of these are borrowed from adjacent fields — public cloud security, scientific-facility time-sharing (telescope nights, beamline schedules), bug-bounty programs, robotics-safety standards. None of them have been worked out in detail for a public, openly-instrumented physical system that AI agents are also supposed to operate. That’s a research agenda in itself.

An open movement for physical evals

Physical evals could become an open-source ecosystem: environments cheap to set up, easy to fork, open to anyone with a problem worth measuring.

The rest of this section traces the arc: where things have been (Prior art), what an open ecosystem looks like in practice (Open at every layer), what it would have to cost (What’s the Raspberry Pi of a physical eval?).

Prior art

Physical-world AI competitions aren’t new. The DARPA Grand Challenge put autonomous vehicles in the Mojave; the DARPA Robotics Challenge put humanoids through disaster-response courses; the Amazon Picking Challenge ran in warehouse mock-ups for several years;

RoboCup has been running its soccer leagues since 1997 — arguably the longest-lived physical eval in continuous operation, and the one with the most literature on what makes it work and what it ends up measuring. RoboCup has been doing its soccer leagues since the late 1990s; the Indy Autonomous Challenge and Roborace have put driverless cars on real circuits.

What these have in common: each was (or is) a sponsor-led, time-limited event with closed protocols and bespoke infrastructure. They produced brilliant moments and a small library of papers; they were expensive to build and harder to reproduce.

What’s the Raspberry Pi of a physical eval?

The hard constraint on all of this is cost. A DARPA-class eval needs millions of dollars and a multi-year program; even a modest research-grade one runs into expensive sensors, networking, fail-safe hardware, and the human labour to keep it operating. That ceiling is what makes physical evals rare today — and rare evals can’t be the basis of an ecosystem.

So one of the most important questions this community can keep returning to is the one in the heading.⊕

Stand-in for “the cheapest plausible build”. The Raspberry Pi did this for hobbyist computing; what’s the equivalent for physical evals? What’s the bill of materials that brings a credible, instrumented, openable physical eval down to the cost of a serious hobby project? Probably some mix of commodity sensors, a single-board computer for the control loop, an open scheduling service for time-share, off-the-shelf safety hardware, and a reference orchestration stack that everyone forks. If the answer ends up being “a few hundred dollars and a weekend,” the ecosystem can actually form. If it stays at “a few hundred thousand and a six-month build,” it stays a fantasy.

Open at every layer

In order to further lower the cost for anyone to be able to set up (safely) their physical evals, we need to look at off-the-shelf hardware and an open source stack.

• Open protocols. The spec of an eval (environment, action space, sensors, metric, secondaries, guardrails) is published as a forkable document, the same way a research benchmark is published.
• Open hardware. Sensor rigs, mechanical setups, fail-safe systems default to off-the-shelf components, with reproducible bills of materials and CAD files.
• Open software. Time-share scheduling, telemetry capture, scoring, auditing — shared infrastructure, not a one-off codebase per eval.
• A community around it. People running, replicating, and forking each other’s evals; people contributing sensor stacks and guardrail designs; people maintaining the scoring code together. No single lab can stand up enough physical evals to cover the interesting surface of physical problems — a community can.

Public verifiability

The safety section above focuses on protecting the physical environment from adversarial participants. There is a symmetric problem that gets less attention: protecting participants — and the public — from adversarial eval runners.

In an open world where anyone can wire a field, a lab bench, or a warehouse cell to the internet and declare it a physical eval, the operator controls the sensors, the scoring pipeline, and in a way, the ground truth. A dishonest operator can inflate results for a preferred team, suppress evidence of harm, or fabricate the physical record entirely. If physical evals are going to carry weight — as procurement signals, safety certifications, or policy inputs — the data they produce has to be trustworthy independent of whether the runner is trustworthy.

This is a valuable research direction in its own right. Some threads worth pulling:

• Tamper-evident sensors. Hardware-attested video streams that can be verified as unedited after the fact — the physical analogue of a signed log.
• Trusted execution environments. Running the scoring pipeline inside a TEE means the operator cannot modify results without breaking the attestation, even if they control the host machine.
• Cross-checking sensor redundancy. Multiple independent sensor modalities covering the same physical event make coordinated fabrication harder: a weight sensor, a camera, and an RFID log all have to agree.
• Third-party witnesses. Spot audits by an independent party — human or automated — who can access raw sensor streams without going through the operator’s pipeline.

Combinations are likely to be necessary, and the right combination will vary by domain. What a wet-lab needs to prove that a synthesis actually ran differs from what an orchard needs to prove that a drone actually flew a slot. Building this layer — call it physical eval verification — is at least as important as building the evals themselves to make the results publicly verifiable.

A Darwinian ecosystem

Not every physical eval will be a good one. Some will be hard, some trivial. Some will be well-structured; some will be a mess. Some will scale to many participants; some will only ever host one team at a time. That’s fine — even desirable. The shape of “what makes a good physical eval” is going to emerge from people building them, breaking them, and learning what each one actually measured.

Sketched, a public registry for such an ecosystem might look like this:

More than evals

Every execution of a physical eval produces something beyond a score: a timestamped record of sensor readings, actions taken, and outcomes observed, all under conditions that were defined in advance and held constant across participants. That record has value on its own.

The most immediate use is data collection. A team that runs an agent on the orchard eval for a week doesn’t just get a leaderboard position — they accumulate labelled trajectories in a real environment that would be expensive to stage deliberately. Even failed attempts are informative: a drone that misses a bird on Tuesday has documentation of exactly what the environment looked like and what the agent did.

For some categories of problem the step further is worth considering: repurposing the eval environment as a training environment. The orchard is already instrumented. The slot system already handles scheduling. If the cost of running episodes is low enough — bird-deterrence is essentially free to attempt, wet-lab synthesis is not — the same infrastructure can run RL rollouts between evaluation windows. The environment that scores a model on Monday can help train the next version by Friday.

This doesn’t collapse the distinction between training and testing. Eval integrity still requires held-out conditions, independent scoring, and participants who didn’t design the environment. But the hardware doesn’t have to be idle between eval slots, and the data generated during evaluation doesn’t have to be discarded. For operators willing to share trajectories under open licences, a physical eval site becomes something closer to a living dataset — one that grows richer every time a new agent takes a slot.

Physical evals as a market

Why should one set up a physical eval? Setting up a physical eval can be a way to crowdsource intelligence for an unsolved problem.

The eval ecosystem can act as a market for intelligence: any participant — human, agent, team, company, hobbyist — can take a slot, attempt to saturate the metric, and submit. The leaderboard answers the AI-selection question by revealing whose approach actually delivers on the physical world.

Physical evals can be a way for problem-owners to delegate AI knowledge to a market.⊕

This is the same shift that happened with bug bounties. A company didn’t have to predict who the best vulnerability researchers were; they had to publish the surface and the rules, and the market sorted itself out. The grower doesn’t pick a model. The hospital doesn’t pick a model. The factory doesn’t pick a model. They pick a problem worth instrumenting and let the world’s AI builders compete to be the answer. As more domains follow suit, an aggregate picture emerges of where AI is actually good.

Get in touch

If any of this resonates, please write. Three good reasons:

• Already working in this space. Compare notes — what’s been learned about sensing, guardrails, or keeping a system honestly open will save the next person a lot of time.
• Have a physical problem worth instrumenting as an eval. Worth thinking through together — what to measure, how to keep it safe to open up, how to make it interesting enough that people show up to compete.
• Have an eval to propose. Even if hosting isn’t feasible right now, good proposals are valuable — they’re what an ecosystem of physical evals is made of.

DM @iamnotnicola on X.

Let’s turn more of the physical world into something AI can be measured against — and use that to point AI at problems that actually matter.

Acknowledgements

This was written by Nicola Greco with support of AI. It was brainstormed as part of ARIA’s Scaling Trust programme, in collaboration with Alex Obadia. Thanks to Ross Taylor (General Reasoning), whose thinking influenced some of our ideas on physical evals.