The State of Agents2026

The gain is real. So is the gap.

In early September, Artificial Analysis ran GPT-6 Astra against a document task where every rubric criterion has to pass. Astra cleared all of them 33.2 percent of the time. Its predecessor, GPT-5.6 Sol, cleared them 28.2 percent of the time. That five-point gain is real, and someone other than the vendor measured it. It also means that on roughly two-thirds of those documents, something still failed. AS01

The same shape shows up in the products. Between January and early September, Glean added collaborative editing to its agents, goose added handoffs a person can resume, and OpenClaw added updater rollback with a diagnostic pass after updates. Real changes to what the software does. None of them measures whether the work got better, or what the people around the work now carry. SY001SY002

Three things blur together and are worth keeping apart: what a model scores on a specified evaluation, what a product documents it can now do, and what happens to the work in an actual organization.

The central tradeoff runs beneath it all. More generated work can remove drafting and reconstruction. It can also add briefing, checking, correcting, and coordinating. The net effect is a question to measure. A finished file is not necessarily ready for review, approval, or use.

If you work alone, pick one recurring job you can judge yourself and count your own review and upkeep time inside the result. If you run a small business or a startup, give one person the whole workflow, including the parts exported to sales, finance, delivery, and customers. If you buy for an enterprise, evaluate the product with its data paths, permissions, review capacity, recovery, and procurement terms, because the product alone is not what you are deploying.

Twenty offerings are covered, frozen as exact routes at a September 4, 2026 cutoff. Astra sources were refreshed September 5.

A running example, and it is fiction

One example runs through every section, and it is fiction. An agent prepares a customer proposal from call notes, the current price list, approved contract terms, and delivery capacity. Sales wants it persuasive. Finance needs the margin floor held. Delivery needs a schedule it can meet. The customer needs commitments that hold.

There is no single golden proposal, which is the point. The arithmetic and the approved terms can be checked against a source. Tone, emphasis, and which tradeoff to offer require judgment, and prices and capacity can both change after the draft is written. Nothing below reports a trial of this workflow, because no trial was run.

Seven documented changes, and what the count does not mean.

The study fixed twenty routes against ten observables, which produces two hundred cells. Seven of those cells document an expansion between Q1 and the September cutoff, with exact dates on both ends and a matched claim. They land in four observables, not ten. SY001

Collaboration accounts for three. Glean moved from a shared agent library to multiplayer agents with collaborative editing. goose moved from subagent logs in the interface to concurrent notifications and a resumable handoff. Dify moved from restrictions inside invalid containers to forms that work inside loops and survive a refresh. Persistent work accounts for two: OpenHands moved from a retained sandbox to a local backend that remembers its mode and scopes a directory, and OpenClaw moved from a shared task ledger to per-agent directories and up to one hundred managed worktrees. Intervention accounts for one, where Hermes moved from reporting completion status to letting an owner list, steer, and stop delegations and see cost per delegation. Maintenance accounts for one, where OpenClaw added updater rollback and a diagnostic triage pass after updates.

Here is what that seven does not say. It does not say seven of two hundred agents improved. It does not say product quality improved. It does not describe how common any of this is in the market. The number measures how much of this evidence the research could document under its own rules, and nothing else. SY002

What Astra changes

Astra's document result is the clearest independent gain: the all criteria pass rate rises from 28.2 to 33.2 percent against Sol. Around it the independent picture is mixed. At launch, Artificial Analysis measured AA-Briefcase roughly 80 Elo higher and GDPval-AA v2 roughly 80 Elo lower, with presentation quality declining and no numeric delta given, and those two Elo figures do not sit on a common scale. The coding index rose about two points to 67 at roughly unchanged task cost in Codex at maximum effort, while general evaluation cost rose 75 percent. That 75 percent belongs to the v4.1.1 composition and must not be carried onto v4.2, which changed both the test composition and the Elo anchors, so v4.2 putting Astra four points above Sol is not a continuation of the v4.1.1 result. AS01AS02

ARC Prize ran the same model through different execution setups and got different answers. At maximum effort the standard harness scored 62.7 percent for $26,098, while the provider adapter scored 98.6 percent for $17,332. A separate best-observed adapter run at high effort reached 99.9 percent for $18,817 at a different effort setting, so it is not a like-for-like comparison. The execution system moves the result. That establishes system sensitivity, not enterprise reliability, and not a privacy or memory advantage. AS03

OpenAI's own evaluations report execution gains from Sol to Astra: AutomationBench 18.1 to 41.4, OSWorld 2.0 65.7 to 72.6 on an offline partial score, and Terminal-Bench 4.0 37.3 to 57.9. These are vendor-reported, do not share a single definition of success, and none measures management savings. AS04

The search found no independent Astra field study measuring total human handling, downstream rework or full cost of ownership. That is a gap in the record, not evidence that those outcomes cannot improve. And one note keeps the timeline honest: June releases are visible in the September snapshot rather than being Q3 events, and Astra against Sol is a September comparison against a predecessor, not a measured movement from Q1 to Q3. SY005SY022

Custody belongs to the route, not the label.

Follow the customer details, the prices and the negotiated terms through three arrangements. Vendor operation, where the provider runs everything. Local execution with cloud inference, where your machine drives the work and a remote model does the thinking. Local execution with local inference, where both stay with you. Dedicated hosting and restricted egress configurations sit between these and change the answer again.

Compare the arrangements on patching, secrets, identity, backups, monitoring, support, recovery, export and deletion, because those are the costs that arrive after the decision. And note the one that catches people: local execution can still send data to models and tools. Local capability and practical locality are different things, and neither one means private. SY010SY006

Privacy and intellectual property need separate answers. On privacy, ask about training use, retention, support access, subprocessors, residency and key control. On intellectual property, ask about rights to inputs and outputs, code and weight licenses, whether state can be exported, and what switching actually costs. Contract promises and observed behavior answer different questions, and a hosting location is not a custody finding.

The practical version is a walk, not a questionnaire. Take one real document through the route you are buying and write down every place it comes to rest: the prompt and its attachments, the model provider's logs, the tool the agent called and whatever it retained, the trace layer, the backup, the support ticket that will quote it back six weeks from now. Every resting place is a separate retention, residency and deletion question.

The cost picture has the same shape. A unit price, total consumption and the cost of an accepted outcome are three different meters, and a cheaper meter need not produce a smaller bill or cheaper finished work. Provider incentives run through seats, credits, token and tool usage, prerequisite services and switching costs. That is worth understanding without the blanket claims that providers only sell tokens or that every hyperscaler is unprofitable.

Independent evaluations help here when their dates and execution conditions are stated, and some of them evaluate a model inside a named executor rather than the model alone. None of them certifies your workflow. The ARC result establishes that execution configuration matters. It establishes nothing about custody. AS03

Two limits on this section are worth stating plainly. Every custody route in the study is recorded as a documented route shell with the decisive custody fields not publicly demonstrated. Every authority and recovery row is recorded as not sufficient for a recommendation. Not publicly demonstrated means the evidence was not published, not that the capability is absent. SY002

Adopt in proportion to the decision.

Three situations call for three different next steps, and each one earns a different size of conclusion. The rule underneath them is that the evidence has to match the consequence, not the enthusiasm.

Three to five runs will expose setup and measurement problems, which is useful. They cannot establish broad superiority. Formal confirmation belongs wherever the consequences require that assurance, and it is not a prerequisite for reversible personal use. Claims about improved quality or reduced human handling need a baseline from the current process. Claims about expert performance need an expert comparator. Expert parity is not a universal adoption gate. SY021

Expansion can chase quality, responsiveness, accessibility or control just as legitimately as savings. Name the benefit you are buying and the cost you will accept. Require evidence for a claimed cost advantage rather than insisting every adoption has to be cheaper.

Stop or narrow the scope when errors escape review, when queues exceed capacity, when sources become unreliable, when recovery fails, or when recipients inherit rework they did not sign up for. Recheck after any material change.

Rising activity with flat accepted throughput calls for diagnosis rather than an automatic verdict, because exploration or genuinely higher quality can justify the effort. Before adding agents or reviewers, try better evidence packets, reviewer assistance, selective checking, or cancelling redundant candidates.

One more note from September 6: OpenAI reports diminishing reliance on monitoring the model's chain of thought. Provider access to internal reasoning is not the same thing as an explanation or an audit log you control. Keep permission controls, action records and bounded tests alongside artifact review, because none of them certifies safety alone. AS07

Then do the practical thing. Choose one recurring job. Name who accepts the work. Record the human work it takes today. Compare what changes with the agent in place. And before you widen the audience, state the readiness state, the evidence gaps and the decision you are asking for, then measure what the recipients spend rather than only what the operator saved.