Productizer vs Entire

The two products agree on the problem and answer it at different layers. Dohmke’s own framing is remarkably close to Productizer’s: the bottleneck has moved to “capturing intent, reviewing the intent, defining the evals, and proving that the loop did what it was asked to do”, and he asks “how do we make the intent survive the loop?” (WeAreDevelopers keynote, 20 July 2026) Entire answers that per change: a trail is written before the diff exists, and gates score the resulting change on every push. Productizer answers it across changes: a standing spec with permanent ids and a constitution that predate the change and outlive it. That is a difference in kind, not in quality — and everywhere else the two compose more than they compete, because one records what happened while the other decides what should.

Trails, checkpoints and gates — what Entire actually launched

A trail is written before the diff exists: the user records their intent, hands the trail to the agent, and the trail becomes “the thread connecting everything, including my idea, the plan, the agent’s work, the generated code, and ultimately the merge” (keynote). Every agent commit saves a checkpoint of the work at that moment, and on entire.io you can see “the prompts, tool calls, model responses, and files changed”. Gates are review signals that “indicate confidence, risk, drift, security”, and behind each gate is a runner — “a configuration file that lives in your repo. Think of it like a CI/CD workflow, but this tells the agent what to check and how to score it. Runners rerun and reevaluate on every push.” Those scores are usable as merge criteria, and merging still needs findings resolved, checks passed and a teammate’s approval — so there is a real human gate. The pitch is “shipping confidently without reviewing code meticulously line by line” and “a world where ‘looks good to me’ actually means something”. Also announced: a decentralized Git backend with nodes in the US, EU and Australia, repos pinnable or multi-region, GitHub mirroring, and a claimed 2.1 million pushes per hour on a single repo — their figure, unaudited, reported here as their claim.

Gates are Entire’s strongest answer to the same problem Productizer solves, and the fair comparison says so. Both put a scored, declarative, in-repo configuration between the agent and the merge. The honest distinction is what is being checked. Entire’s runners score a change — risk, security, drift and confidence on a diff, recomputed on every push. Productizer’s declared checks and its classifier test a change against a spec and constitution that predate it and that other changes were also tested against. Neither is the better mechanism in the abstract; they are answers at different layers, and only one of them survives across a year of unrelated changes.

Where Entire genuinely leads

Provenance depth is their clear win, and it is not close. entire blame <file> and entire why <file>:<line> take a live line of code and map it back to the prompt that produced it. Productizer has nothing at that granularity. v4.2.0 narrowed the gap rather than closing it: a Productizer-Req: trailer on the commit makes “which commits built R14” answerable with git log --grep on a machine that has never heard of this tooling, and a COV_ id quoting the requirement id verbatim makes an orphan detectable in both directions. That is provenance at the commit, and it is a claim, not evidence — nothing checks that a commit citing R14 has anything to do with R14, and the coverage scan is textual, so a document that merely mentions a coverage id registers as coverage. Entire maps a line to the prompt that emitted it, which is a different order of thing. Redaction is their other clear win: roughly 2,700 lines of tested Go across nine layers, entropy scoring, two pluggable scanner engines and optional PII detection. Productizer has no redaction layer at all, and no equivalent is planned or claimed. Add to that eight supported coding agents to Productizer’s one, scored merge gates that rerun on every push, a cross-agent skills pack, a local tree-sitter code-graph plugin, an experimental multi-agent entire review, and a multi-region Git backend.

Their fail-closed governance is good engineering and deserves the credit. Scanner selection is honoured only from the committed .entire/settings.json; a settings.local.json override is ignored with a logged warning — in their own words, “because the choice affects everyone who reads the repo’s checkpoints, not just the developer who set it” (entire docs). Disabling every scanner is a settings load error, and if the sole enabled engine fails at runtime the transcript write fails closed rather than persisting under-scanned data. That is the same instinct behind Productizer’s hollow-check rule, applied to a different problem, and applied well — and v4.2.0 borrowed it outright. Team-level settings in checks.yaml — anything deciding what is examined or whether the run blocks — are now honoured only from the committed file; a local override is ignored with a warning naming it, on their reasoning exactly, that the choice affects everyone who later reads the result; and a configuration switching every check off is a load error rather than the largest hollow pass available. The idea is theirs. It is here because it was right.

Where Productizer goes deeper

The agreement, before the work, as a durable cross-change structure. A trail captures intent per change, in free text, at the moment of the change. Productizer holds one spec per product with permanent requirement ids that are never reused and never renumbered, a constitution of P-numbered principles above them, supersession that keeps the superseded text, and a classification step that measures every new intent against the entire spec before anything is built. When the new intent contradicts an existing agreement, the work halts and a human rules. Around that: nine linear stages, declared checks in a committed checks.yaml that block when a check examined less than it declared, human gates on deploy and publish, a backlog with permanent B-ids where order is priority, and read-only generated views that never feed back into the files. A small discipline that says a lot about the rest: a value that could not be measured is never rendered as zero.

This is the part that is hard to reach from the change side. A gate can score the diff in front of it, and a trail can say what this change was for; neither can tell you that today’s request contradicts a decision taken four months ago, because there is no standing decision to compare against — no requirement id, no supersession, no constitution. Answering “how do we make the intent survive the loop?” per change is a real answer. Making it survive across changes is a different one.

Where Productizer is weak — plainly

Contradiction detection is the mechanism this page rests on, and its accuracy is only partly measured. The symbolic cross-check scores 1.00 precision and 0.70 recall on the 21 cases we first wrote, and 0.67 / 0.12 on a harder 26-case corpus; the end-to-end system’s recall is unmeasured. We state the sample size rather than the bare number, because a figure from a self-authored corpus of 21 is not the same claim as a benchmark. Productizer is Claude Code only, where Entire covers eight named agents. Its lifecycle has not yet been exercised end to end through CI, and its eval corpus has never been graded — 26 cases load, but claude plugin eval is early-access gated and no case has been scored by a model. It has no redaction layer at all, nothing comparable to Entire’s provenance depth, and no scored merge gate: the runner format ships and nothing in the repository executes one, so it is a contract without a runtime, and the scorer beside it runs on demand rather than on every push. If your problem is “which prompt wrote this line, does the record leak secrets, and can the diff be scored before it merges”, Productizer is the wrong tool and Entire is the right one.

One caveat Entire documents itself

Because transcripts land on a branch inside your repository, their own documentation warns that “If your repository is public, this data is visible to the entire internet” (entire docs), and shadow-branch code snapshots are written unredacted. That is reported here as their own documented caveat, not as an accusation — it is the honest trade of storing rich history where the code lives, and they say so in the open.

The verdict — they compose more than they compete. One records what happened and scores it at the merge; the other decides what should happen and holds it across every later change. Run Entire when you need line-level provenance across many agents, a serious redaction pipeline before any of it is written down, and in-repo gate runners scoring risk, drift and security on every push. Run Productizer when the thing you are missing is a standing agreement — permanent requirement ids, principles above them, and a classifier that stops the work when a new intent contradicts an old one — held locally in your own repo with no account, no control plane and no telemetry. Nothing about running one rules out the other, and pretending they are rivals on every row would be less credible than saying that plainly.

Entire capabilities are mapped from the public entireio repository, Entire’s own documentation, reporting on their February 2026 seed round, and Thomas Dohmke’s WeAreDevelopers World Congress keynote of 20 July 2026, as of 2026; quoted phrases are theirs. The 2.1 million pushes per hour figure is Entire’s own claim and is unaudited here. Funding and founder details are stated as facts, not as insinuation. “No publicly confirmable paid tier” means exactly that — not an assertion about their customers, which is unknown. Productizer marks reflect shipped capability in github.com/gitayg/productizer; the recall figures are our own, measured only on the symbolic cross-check: 0.70 against the 21 cases we first authored, 0.12 against a harder 26-case corpus; the end-to-end system is unmeasured. Entire is a trademark of its owner; this is an independent capability comparison, and this page is not affiliated with, endorsed by, or partnered with Entire. Both products evolve — verify specifics against current documentation.