Fabrication
The AI confidently reports work that doesn't exist.
Flagship use case
Autonomous AI software engineering — run as a workflow.
We build WorkflowHub with WorkflowHub. The agents that write our code pull from our own queues and pass our own gates. Below is one overnight run, with what shipped, what it cost, and what stalled — including the parts that went wrong.
The problem every buyer already knows
Everyone has tried "let an AI write code." Everyone has hit the same four walls.
Point agents and copilots don't solve these, because these are process failures, not model failures. A workflow does.
Vagueness in, garbage out
Loose requirements become unbuildable tickets.
No trust
Nobody can say afterwards what the AI did, why, or what it cost.
No safety
An agent with repo access is one bad prompt from wrecking main.
The WorkflowHub answer
The SDLC workflow
Software delivery runs as a designed, gated pipeline — Requirements → Development (tech-routed lanes) → Code Review → Test → Verify → Deploy → Done — where AI agents pull work exactly like employees, and the workflow enforces what no prompt can:
- Evidence gates. An item cannot leave Development without a commit reference and a build status. That stops an item moving on nothing — but the gate checks that the field is filled, not that the commit is real. We found that out the hard way, auditing finished items whose commits were never on main, and now verify delivery with a separate pass over git history. Closing that gap is on the roadmap, not in the product.
- A real BA layer. The Requirements stage researches the codebase, pushes back on ambiguity (explicit interpretation + clarifying questions + declared defaults), sizes every ask against what one agent session can actually deliver, and splits programs into bite-size items — or escalates to a full project with a saved Design & Objectives document as its validation baseline.
- Narrow, routed context. A classification set at intake routes C# work to a C# lane and web work to a React lane — each lane delivers only the standards that task needs. Better code, fewer tokens.
- Safety by construction. Agents code in isolated worktrees, commit only to feature branches, and nothing reaches production without a validated merge — the human-approval model the rest of the platform sells.
- Everything tracked. Every stage writes structured fields (decisions, files changed, commit, test evidence, tokens, cost, duration) with human/AI provenance. The audit trail is the management report.
The receipts
One overnight production run (July 15–16, 2026)
Unattended overnight, supervised by the workflow's own review gates.
| Shipped to production | Evidence at the gate |
|---|---|
Version endpoint (GET /api/version) | build green, 3/3 tests — now serving in prod |
| GitHub webhook integration (~450 lines) | 8/8 tests, HMAC trust boundary security-reviewed |
| Hybrid on-prem blob storage (AgentBlobStore) | 12 files, 11/11 tests, both data providers |
| In-app skill download (API + web) | 9/9 tests, cross-stack coherent in one commit |
| Marketing update (lean agent download) | reviewed, scoped exactly to the ask |
Five features, ticket → code → tests → review → merge → production, at single-digit dollars per feature — while the workflow's guards did their job in real time: a looping item was auto-quarantined after 3 attempts; agents facing impossible asks filed honest "needs human" outcomes instead of fabricating; one agent correctly declined an item marked do-not-build. The same run surfaced three process defects (orphaned commits, session-timeout sizing, duplicate re-work), each caught by the pipeline's verification layer and fixed — the process improved itself overnight.
Equally important is what the run proved about inputs: every one-deliverable, well-specified item shipped first-try; every architecture-posing-as-a-ticket stalled safely. The difference was never the AI — it was the workflow discipline around it. That discipline is the product.
The value proposition
In the buyer's terms
Gets more done
A agents of agents works your backlog 24/7, in parallel lanes, batching similar work to reuse context.
Saves money
Cheap models where they're sufficient, strong models where it matters, token/cost stamped on every item — quality-per-dollar is a managed metric, not a hope.
You stay in control
Humans gate what matters (requirements approval, release to production); the agent volunteers its telemetry; every action is attributable, auditable, reversible.
It compounds
Agents write what they learn into a shared, deduplicated knowledge base and the stage skills version themselves — the process gets smarter with every ticket.
We don't just claim this — WorkflowHub is built by this workflow. The version endpoint in our production API was written, tested, reviewed, and shipped by an agent working a ticket in our own product. Ask us to show you the work item.
Sources: SDLC workflow run ledger (July 15–16, 2026), supervisor merge records 5c506bd–9304076, per-item field evidence in the WorkflowHub project.
Put your own backlog on this pipeline.
The same evidence-gated pipeline that builds WorkflowHub is the one you'd run your own engineering through — bring a real workload and we'll set it up with you.