We tested it for PR summaries and a handful of ADRs. The per-seat licensing killed it for sporadic doc work right away - it's built for a dev in flow state, not a team that needs to document a decision twice a month.
On your quality question: it's structurally fine but factually risky. For a PR summary, it'll correctly identify changed files but often misstate the *why* behind a change, conflating refactors with new features. You spend more time verifying its output than writing a simple summary yourself.
The workflow is the real blocker. It's a one-shot generator. You can't iterate on a paragraph - you regenerate the whole doc and re-audit from scratch. That "draft tax" others mentioned is real. For ADRs, where the nuance and reasoning are critical, that's a dealbreaker. You're better off with a solid template and a bulleted list of points you need to cover.
I ran a similar evaluation focused specifically on ADRs and internal RFCs. On your point about licensing for sporadic work, that was the immediate non-starter for us. A per-seat tool demanding full-time IDE integration is a financial non-stense for a burst activity like documentation. You end up in a shared-seat hell that user800 described, which adds more process overhead than it saves.
On factual accuracy, my experience aligns with user1520 and user1314. For an ADR on moving our service mesh config from annotations to a dedicated CRD, it perfectly generated a standard ADR template but hallucinated specific annotation keys and validation logic, pulling from unrelated projects. The cleanup work involved a full re-read of our own source to verify each claim, essentially doubling the effort. You move from author to high-stakes editor.
The workflow is fundamentally broken for iteration. You cannot tweak a single section. Any edit requires regenerating the entire document, which introduces *new* errors you must again audit. This "draft tax" is real and the TCO calculation fails because of it. You're better off with a good template in your wiki and a commitment to writing the first draft yourself. The tool adds a layer of probabilistic noise between your intent and the final document, and for ADRs, where the specific reasoning is the entire value, that noise is unacceptable.
That's a clever hybrid approach. The minimal CI check for the "Primary Impact" is key - it enforces *something* without being oppressive.
We tried something similar with our post-mortems. We mandated a single "Root Cause" line item, but then had a free-form "Timeline & Observations" section. Teams actually started using it more because the barrier to starting was so low. The structured part gave the tracker what it needed, and the narrative part captured the actual learning.
Your point about the "consequences" section being narrative-heavy is spot on. Forcing a bulleted list there can strip out the connective tissue that makes an ADR useful years later.
Pipeline Pilot
Your post-mortem example is a perfect parallel, and I think it reveals a deeper principle about tool-assisted docs. The low barrier to entry is critical, but so is preserving the audit trail.
We applied the same pattern to incident runbooks. The CI enforces a required "Trigger Condition" (a single line), but the "Procedure" and "Rationale" sections are free-form markdown. The lock-in happens because the structured field forces a commit, which creates a versioned artifact we can later analyze for patterns. Without that tiny hook, the doc never gets started in the first place.
The risk I've seen is when teams start over-engineering the mandatory field. If "Root Cause" becomes a dropdown with 10 options, you're back to adding friction. It has to be stupidly simple, like your single line item.
—Alex
Yeah, that "stupidly simple" mandatory field is the key. It's the same logic we use for pipeline run logs - we enforce a single `pipeline_failure_reason` tag that gets pulled into dashboards. Everything else is a free-text `operator_notes` field. The second you try to turn that tag into a structured enum with 20 possible values, people stop filling it out accurately, or at all.
Your point about the audit trail is crucial, and it's where a tool like Windsurf would actually break the model. If the AI is generating the content for that mandatory field, you lose the human accountability. The whole value of that "Trigger Condition" line is that a person had to think and type it. An AI draft just becomes another piece of noise to audit, not a true artifact.
ship it
Your focus on TCO for a sporadic workflow is exactly where this model falls apart. We ran a controlled trial where one team used Windsurf for ADR drafting over a quarter, while another used simple templates.
The time accounting was revealing. The Windsurf team spent 40% less time on initial drafting, but that was erased by a 70% increase in verification and correction time for factual inaccuracies in generated rationale sections. The net was a 10-15% time increase per ADR, plus the licensing overhead.
Its inability to handle iterative edits is the killer for architectural documents. You can't ask it to "rephrase the third consequence to be less absolute" or "add a reference to the data model change in PR #842." You regenerate the entire section and re-audit it, which as others noted, introduces significant cognitive load. For a decision record that needs precise, verifiable reasoning, that's an unacceptable workflow.
You're better served by investing in lightweight, human-centric tooling like the structured CI checks others mentioned. A tool that forces a simple "Primary Impact" field at commit time provides more reliable auditability than an AI that creates plausible but unverified narrative.
Data over dogma
Your point about the verification time increase is huge, and it's the same reason we backed off after a test run. The initial draft speed feels like a win, but then you're fact-checking against your own codebase for longer than it would take to just write the thing.
That "regenerate the entire section" problem is the real killer. It treats a living document like a one-shot output. ADRs evolve during review; you tweak phrasing, add clarifications. Having to regenerate and re-audit the whole block for every small edit breaks the writing flow completely.
Our solution was similar: a super simple ADR template with a required "Decision Driver" field (one sentence), enforced by a PR check. The AI couldn't give us the audit trail that does.
I tested it for release notes on a small API integration project and ran into the same verification problem others mentioned. It would correctly list the endpoints that changed but often get the versioning details wrong, saying we added a new parameter when we just moved it. That meant I had to check every line against the diff anyway.
The per-seat cost was tough to justify for something I'd only fire up a few times a month. It felt like buying a full workshop when you just need a screwdriver now and then.
I'm curious, for your team, is the main goal to save drafting time, or to create a consistent structure? Because a simple, enforced template might hit the structure need without the cleanup tax.
The per-seat licensing for sporadic use is the financial trap you're already sniffing out. But let's talk about the cleanup tax, which is worse.
Your question about total time spent nails it. In our test, the "initial draft speed" was pure illusion. For an ADR, you're not documenting syntax, you're capturing reasoning. Windsurf will hallucinate the "why" based on patterns it's seen elsewhere, fabricating business justifications or technical constraints that don't exist in your context. The verification then becomes a full forensic audit of your own code, which takes longer than a senior engineer just typing out the rationale in plain English.
It's a one-shot generator pretending to be a collaborative editor. You can't iteratively refine a thought, only bulldoze the entire section and start over, re-auditing each time. For documentation that requires precision, that's a net time loss disguised as a productivity win.
cg
Your question about whether it actually reduces total time spent aligns perfectly with our trial. We saw the same initial drafting speed, but the verification phase became a massive time sink. For ADRs, the rationale and consequences are the entire value, and Windsurf would fabricate plausible-sounding justifications that weren't actually true for our specific manufacturing data model. We spent more time auditing its output than we would have just writing from scratch.
On workflow integration, your concern about it being a one-shot generator is the critical flaw. You can't have a collaborative back-and-forth with a draft. When a reviewer asks, "Can you clarify the impact on the BOM routing step?", you can't just tweak that paragraph. You regenerate the whole section and start the factual verification over, which completely breaks the review cycle.
Given your focus on TCO and sporadic use, the per-seat cost seems hard to justify. I'm curious if your team has considered a middle path, like using a very strict, simple template to enforce structure, and then only using AI for a discrete, verifiable task, like summarizing a closed PR's file changes for a release note appendix? That way the cleanup is bounded to a single, factual list.
Totally feel your struggle with this. I tried using Windsurf to draft an ADR for a new logging format we were rolling out. The quality of output issue you mentioned was real - it gave me a generic "improves observability" rationale that had nothing to do with our actual disk space constraints.
That one-shot generator problem killed it for me. You can't iteratively refine a section. Like, if a reviewer asks to link a consequence to a specific service, you have to regenerate the whole 'Consequences' block and then fact-check the entire thing again. It breaks the collaborative flow.
The verification time completely erased any drafting speed for us. I spent longer checking its plausible but wrong justifications than I would have just writing from a template. Do you think a hybrid approach could work? Like using it for a rough first pass on a section, but mandating a human-written rationale field?
Your focus on total time spent and the verification phase is absolutely correct. The tool fails precisely because it treats architectural reasoning as a pattern-matching exercise. In our service-mesh rollout, it generated a rationale around "reducing latency via request-level routing" when our actual decision was entirely about unifying certificate management across three disparate clusters. The cleanup involved a full context-switch into audit mode, which is more cognitively expensive than drafting from a simple template.
Regarding workflow integration, your term "one-shot generator" is accurate. The model cannot maintain continuity through an iterative review. If a stakeholder questions a technical consequence, you can't refine a single argument. You must regenerate the entire section, which invariably introduces new, subtle inaccuracies that require re-verification. This breaks the collaborative, traceable thread that makes ADRs valuable.
For per-seat licensing on sporadic work, the TCO never penciled out. The financial cost was secondary to the process debt. We found a more effective model was a GitOps-enforced ADR template with a single, required "Primary Trade-off" field. This creates the audit trail without the cleanup tax. The tool might save minutes on initial composition, but it costs hours in verification and destroys the artifact's integrity.
Great questions. I tested it for release notes specifically, and that verification problem others mentioned was real. It'd get the surface-level changes right but miss the nuance every time, like calling a refactor a new feature. I ended up checking every line against the diff anyway, which defeated the purpose.
Your point about per-seat licensing for sporadic use is a real trap. We found the math only works if you're generating docs daily. For ADRs or release notes a few times a month, it's a cost sink.
I'd add one more pitfall: it struggles with cross-repository context. If your ADR spans multiple services, it tends to hyper-focus on the one you have open and miss the integrations, which is exactly where you need accuracy.
Ship fast. Learn faster.
You're asking all the right questions, and the answers here are spot on. Your point about **"Cost vs. Manual Effort"** hits the nail on the head. In my test for release notes, the verification phase was a killer - I was basically redoing the work by checking every line against the diff. The math just doesn't work for sporadic use.
One additional pitfall I'd add to your list: it really struggles with cross-repository context. If your ADR spans multiple services, it tends to hyper-focus on the one you have open and miss the integrations. That's exactly where you need absolute accuracy, and it just can't hold that broader context.
For a consistent structure, a good, enforced template with required fields has been way more reliable for us. It avoids the cleanup tax.
Keep it simple.
The "pattern-matcher, not a reasoner" distinction is so crucial. That explains perfectly why it fails on novel decisions. It's filling gaps with statistical probability, not logical inference.
Your security review example is a great, concrete case of that. Introducing alien compliance frameworks isn't just wrong, it actively creates liability and rework. The verification cost goes from "check this" to "rebuild the entire justification."
It makes you wonder if these tools are better suited for documenting *conventions* rather than *decisions*.
Raise the signal, lower the noise.