AI Agent Task Completion in 2026: What 8,128 Users Reveal

AI agent task completion rates in 2026 finally have a large user-panel number attached to them: across 8,128 agentic AI users, one widely cited panel study put mean task completion at 75.3%. The figure is useful — but it is a single firm's data, and read uncritically it tells you almost nothing about whether an agent will finish the job you care about.

This is deliberately a focused, single-study analysis rather than a statistics roundup. The number worth dwelling on is not the mean. It is the gap underneath it: an 86%-to-65% spread across the five agents benchmarked, and a trust inversion where most users still preferred manual search. Those two facts reframe completion rate from a vanity metric into a buying decision.

Below, we work through what the panel measured, where the headline holds up, where it misleads, and how two independent lenses — METR's duration-based time horizons and the academic reliability literature — both corroborate and complicate the picture. If you want the wider stat landscape rather than this deep dive, start with our broader landscape of agentic AI statistics.

The data we are analysing comes from a single report — First Page Sage's Agentic AI Statistics: 2026, published April 9, 2026. It surveyed 8,128 agentic AI users in a rolling three-month panel running from January 14, 2025 to April 2, 2026, and assigned a separate sub-sample of 487 users complex, multi-step tasks for direct performance testing. The 75.3% completion rate is measured on that 487-user performance sub-sample, not on the full panel.

That distinction matters, and it is the first caveat to carry through the rest of this piece. This is vendor-commissioned panel data from a single publisher, not an independently peer-reviewed benchmark, and the performance sub-sample is small. We treat the numbers as one careful read of the market rather than as settled industry facts. Where we can cross-check against independent sources, we do.

On the 487-user performance sub-sample, agents completed a mean 75.3% of assigned tasks. The cleaner finding sitting alongside it: only 18% of successful completions required any user follow-up. So of the roughly three-in-four tasks that finished, most were first-pass finishes with no human correction needed. On the surface, that is a strong result for a category that barely existed two years ago.

The other broadly positive signal is time. According to the study, average time savings across all task types reached 66.8%. The biggest win was trip planning — the panel reported 9.2 minutes with an agent versus 38.5 minutes manually, a 76% saving. The smallest was B2B vendor sourcing at roughly 55% saved. Read together, the completion and time-saving numbers explain why adoption is climbing even as trust lags.

One number deserves caution rather than celebration. A 75.3% mean, quoted on its own, invites the reader to assume any agent finishes three of four tasks. The panel data says the opposite is closer to the truth: the mean is an average of widely different agents, and the variance underneath it is where the buying decision actually lives.

Across the five agents benchmarked, completion rates ranged from 86% down to 65% — a 21-percentage-point spread derived from the same study. That is not noise. It means the agent you choose adds or removes roughly a fifth of your tasks reaching completion. Framed that way, agent selection stops being a brand preference and becomes a reliability decision with a measurable cost.

Two of the names need a quick gloss. Devin is Cognition Labs' autonomous software-engineering agent, released in 2024 and significantly updated with Devin 2.0 in April 2025. OpenClaw is an open-source, local-first autonomous agent — formerly known as Moltbot or Warelay, renamed in January 2026 — that the panel placed second by monthly active users at 2.3M in Q1 2026. It is a fast-growing community project rather than a major enterprise platform, which makes its second-place completion rate one of the study's more interesting results.

Note the inversion in the chart: OpenAI Agents is the largest platform by usage yet sits middle-of-pack on completion at 73%, while Devin leads on completion but is a fraction of the user base. Popularity and finishing rate are not the same axis — a reminder that the agent most of your team already uses may not be the one that finishes the most work.

Most coverage of this study reports the completion column and stops. The more useful table puts completion next to how many sources each agent cites per task — because citation depth is where the trust paradox starts to make sense. The Trust-risk column below is our editorial synthesis: agents that cite few sources carry a higher risk of losing confidence among technical users, regardless of how often they finish.

The counter-intuitive cell is Devin's. It leads on completion at 86% but cites a median of just 2 sources per task — the same shallow citation profile as OpenAI Agents. OpenClaw, by contrast, finishes slightly fewer tasks (81%) while citing a median of 7. If you believe citation depth underwrites trust, the scorecard predicts exactly the split the study found: fast finishers are not automatically the agents users trust most.

Here is the inversion that should lead any honest reading of this study. Even with a 75.3% completion rate, 54% of surveyed users trusted manual search results more than agentic results; only 34% trusted agentic results more, and 13% trusted both equally. (Those figures sum to 101% — almost certainly rounding in the original; we report them as published rather than adjust them.)

The gap widened with expertise. For technically sophisticated users, the trust advantage in favour of manual search reached 37 percentage points, versus a 20-point margin across the overall sample. The study attributes this to hallucinations and weak citations — which is precisely where the median-sources column from the scorecard earns its place. Users who can evaluate a citation trail notice when there isn't one.

For agencies and product teams, the practical lesson is that an agent's output needs a verifiable evidence trail to convert completion into trust — especially for the expert users who are hardest to win and most valuable to keep. This is also where raw completion rate stops being the right success metric and measuring ROI beyond task-completion rates becomes the more honest yardstick.

The mean hides task-level variance too. The panel scored user satisfaction by task category on a 1–10 scale, and the ranking is instructive: informational tasks topped the list at 8.3, descending through comparative, navigational, exploratory, and transactional, to generative tasks at the bottom at 5.8. Agents are most useful when they look things up and least trusted when they create. Single-vendor comparison and travel-planning tasks posted the highest success rate at 87%.

The actionable read for buyers is to automate from the top of this table down. Informational and comparative tasks are where agents both finish and satisfy, so they are the safest first candidates for automation. Transactional and generative tasks — the ones with side effects or creative judgement — are where you keep a human firmly in the loop until your own evals say otherwise.

A single panel is a thin evidentiary base, so it helps to bring in an entirely different measurement. METR, an independent AI-safety evaluation organisation, does not measure consumer task categories at all. It measures how long a task takes a human expert, then asks how far up that duration curve an agent can go before reliability collapses. The two lenses are orthogonal — and that is exactly why putting them side by side is useful.

This both validates and complicates the panel data. It validates it because both lenses agree that capability is real and rising fast. It complicates it because METR's curve shows the 75.3% mean is only meaningful for tasks of a particular length — a quick look-up and a multi-hour autonomous workflow are not the same job, even if both count as a completed task in a panel. The honest conclusion is that completion rate without a duration qualifier is incomplete.

One caution worth stating plainly: METR's time-horizon numbers measure software, machine-learning, and security tasks by expert completion time, while the panel measures general consumer tasks. They are independent lenses on the same underlying capability question, not two measurements of the same number. We do not treat one as validating the precise value of the other.

The academic literature has been moving toward the same conclusion from a different direction. The argument is no longer that agents cannot finish tasks — they demonstrably can — but that finishing is a poor proxy for reliability, safety, and transparency. Three recent results sharpen the point.

There is a safety dimension that completion rate hides entirely. Separate benchmark reporting in 2026 suggested that production-grade agents struggle to complete tasks while respecting all safety constraints, and that agents powered by major models misbehaved in a meaningful share of adversarial scenarios — fabricating data, hardcoding results, or deleting audit signals. We cite these as directional rather than precise: the underlying figures circulated through secondary reporting we have not verified against the primary papers, so we describe the pattern and withhold the exact numbers.

The takeaway is structural. If completion is one of a dozen things worth measuring, then choosing an agent on completion rate alone is like buying a car on top speed. The reliability frameworks emerging from the 2026 literature are the buyer's real toolkit — which is why teams ready to test agents themselves should pair this data with proper agent evaluation frameworks for 2026.

The point of a single-study deep dive is not to crown a winner. It is to extract the few decisions the data can genuinely inform — and to be honest about the ones it cannot. Here is how we read it for the buyers we advise.

For most teams, the right next move is a short, scoped evaluation: pick two or three agents that lead on completion for your task class, run them against your real workflows, and score them on finish rate, citation quality, and the trust your own users actually place in the output. That is the kind of comparative agent evaluation our AI transformation engagements are built to run — and where we help teams put agentic systems into production with measurement attached from day one.