Your AI-assisted dev team is shipping. Do you know what?
Spec Kitty is a delivery control plane for engineering teams building with AI coding agents. One governed record — from spec to merge — without disrupting how your engineers work.
"It helped catch nuances early that would have caused problems later."
Engineer, Financial Services pilot
"The mature engineer in 2027 will vibe-code prototypes and spec-drive everything that ships. Speed for exploration. Governance for production."
Robert Douglass, founder, Spec Kitty
"AI coding tools create speed. They also create gaps no one is watching."
When agents run in terminals, tickets, and chat threads, the original intent fragments before it reaches code review.
Executives inherit the risk without the visibility. These are the four gaps that open up as soon as AI coding tools land in your engineering org.
Visibility
The work happens in terminals you can't see.
Your team ships with Claude Code, Cursor, Copilot, and Gemini CLI. By the time it hits a pull request, the context is gone.
Governance
Specs live in Slack threads and someone's head.
AI agents pick up work without anchored intent. What gets built isn't always what was decided.
Accountability
The board asks about AI ROI. You have a commit history.
Compliance teams and investors are starting to ask about AI-generated code in production systems. You need an audit trail that exists.
Reporting
Status meetings exist to fill the visibility gap.
Engineers resent them. The data is two days stale and filtered through whoever talked last.
27.8% of AI coding tool dependency suggestions point to non-existent, deprecated, or unsafe versions.
— Sonatype 2026
4.4× throughput increase when governance is embedded before agents touch the codebase.
— Red Hat 2026
Texas Responsible AI Governance Act in effect January 1, 2026. Affirmative defense available to organizations with documented governance structures.
Organizations that build governance before their first AI-assisted incident get the affirmative defense. Organizations that build it after are reconstructing evidence under legal scrutiny.
A governed delivery record that builds itself as work happens.
Spec Kitty sits between your intent and your agents. It doesn't replace your tools. It coordinates them — and keeps a record that auditors, boards, and compliance teams can actually use.
Lock the spec before agents touch the work
Product and engineering align in natural language. Acceptance criteria, constraints, and rationale are committed before any agent picks up a task. What gets built is decided by people. How it gets built is left to the agents.
spec-first workflowCoordinate across Claude Code, Cursor, Copilot, and Gemini CLI
Work packages flow to your existing tools. Engineers don't change their stack. Spec Kitty tracks what each agent is doing, what's under review, and what's blocked — from a single governed record.
multi-agent coordinationGive executives a live view without adding meetings
Status, blockers, and delivery health surface in real time. No standup tax. No manual reporting. The data comes from where work actually happens — not from what people remember to log.
live engineering statusBuild the audit trail as you go, not after the fact
Specs, work packages, reviews, acceptance decisions, and launch readiness accumulate automatically. By the time a compliance team or board asks questions, the record already exists.
compliance-ready by defaultBuilt for the executive who owns the risk.
Spec Kitty addresses the visibility and governance challenges that matter to technical leaders at the executive level.
CTO — Series B/C SaaS
You're scaling AI-assisted development. The board wants proof you know what's shipping. Your engineers love the new coding agents. Your job is making sure agent-generated code ships with the same rigor as hand-crafted code did. You need proof that intent made it to production.
The question:
How do I give the board engineering visibility without adding overhead my team will route around?
CIO — Enterprise
AI Act, SOC 2, internal risk committees — they're all starting to ask about AI-generated code in production systems. You need an audit trail that exists without engineering changing their behavior. Governance frameworks are catching up to AI deployments. You're accountable before the policy is written.
The question:
What evidence do I have that our AI coding process has human decision points before merge?
VP Engineering
Your engineers are 30% faster with AI tools. You still can't tell your CTO what they built last week. DORA metrics tell you throughput. They don't tell you whether original product intent survived contact with an AI agent. The gap between what was decided and what was delivered is where future incidents live.
The question:
How do I maintain spec fidelity across a team using four different AI coding tools?
Specifics. For people who don't trust vague claims.
These are the outcomes Spec Kitty is built to deliver. No methodology magic, no inflated ROI math.
Real-time delivery status
Live engineering status derived from where work actually happens — not from what people manually update in project trackers.
Spec-to-merge traceability
Every decision — from the original spec through acceptance — is attached to the same delivery record. Intent is traceable all the way to the commit.
Multi-agent coordination
Claude Code, Cursor, Copilot, Codex, Gemini CLI — Spec Kitty coordinates across your entire AI dev stack without replacing any of it.
Automatic audit evidence
The compliance record builds itself as work progresses. When a committee asks for documentation, you already have it.
Zero engineering behavior change
Developers keep their tools and their workflow. Governance doesn't require compliance theater from the people doing the work.
Blocker detection before escalation
Stuck work surfaces before it becomes a delivery miss — without requiring an engineer to send an email about it.
The pressure looks different depending on where you sit.
Regulatory environment, team structure, and board expectations vary. The governance gap is universal.
United States
Board pressure on AI ROI is the primary driver. US CTOs at Series B and beyond are fielding board questions about AI spend and measurable engineering output. The ask is evidence, not narrative.
- AI coding ROI documentation for investors
- Engineering velocity benchmarks post-AI adoption
- Audit-ready records ahead of SOC 2 and ISO reviews
European Union
AI Act and GDPR compliance are moving from advisory to operational. EU technical leaders face regulatory timelines requiring documented human oversight of AI-generated systems. Governance isn't optional — it's a liability question.
- Human decision points before AI-generated code merges
- Documented AI tool usage in production systems
- Traceability requirements under AI Act obligations
Australia
Smaller teams, closer to the business, faster decisions — and fewer people to absorb governance overhead. Australian CTOs typically sit closer to product and commercial operations. They need governance that doesn't require a dedicated compliance headcount.
- Lightweight governance that scales with a lean team
- Visibility across contractors and distributed engineers
- Business-readable status without engineering translation
Hard questions
What a CTO actually asks in the first conversation.
Does this require my engineers to change how they work?
No. Engineers keep their tools — Claude Code, Cursor, Copilot, Codex, whatever they prefer. Spec Kitty coordinates at the workflow layer without replacing any of them. The governance record builds from existing activity, not from new inputs engineers have to create.
How is this different from Jira, Linear, or what I'm already using?
Those tools track tasks that people manually update. Spec Kitty tracks delivery intent — the spec, the rationale, the constraints, the agent activity, and the acceptance decision — tied together automatically. It's not a ticket system. It's a governed delivery record with traceability from original intent to merged code.
What does "compliance-ready" actually mean?
It means the evidence exists before anyone asks for it. The record of who approved the spec, what constraints were set, which agents handled implementation, and what acceptance decisions were made is built as work progresses — not reconstructed when a compliance team or auditor asks.
Where does my data go?
Spec Kitty processes development workflow data — specs, work packages, status signals — within configurable privacy tiers. Local-first execution is available. Sharing is consent-based and explicit. Full security posture, data boundaries, subprocessors, and retention policies are covered in the demo.
What does onboarding actually take?
The demo runs through a real deployment scenario, not a slide deck. After that, integration with your existing AI dev tools is scoped based on your specific stack. We'll give you an honest timeline estimate in the first conversation.
Built by people who shipped at GitHub and Red Hat
Creator of Spec Kitty. Long open-source background; ex-Acquia / Platform.sh; 20+ years across CMS, e-commerce, and DevOps. Building the layer between product planning and agentic delivery.
VP of Engineering Operations and Chief of Staff to the CTO at GitHub. Chief Agilist at Red Hat ($2B Red Hat Enterprise Linux business). 30 years in tech, zero patience for unnecessary meetings.
See it against your actual stack.
The demo runs in your environment, with work from your actual backlog. You'll see what a governed delivery record looks like from spec to merge — using the tools your team already uses. 30 minutes. No slides.
Request a demoNo pitch deck. No obligation. Privacy policy →