Ship fast. Without losing control of what your agents know.
Your engineers adopted Claude Code, Cursor, and internal copilots at speed. The productivity gains are real. So is the governance gap. Invoked closes it — without slowing engineering down.
The gap between your standards and your agents
A senior backend engineer uses Claude to refactor the auth service. Clean code, solid tests. The PR goes up. Security finds it: wrong wrapper, missing audit logging, skipped the “any auth change requires security review” gate. Eleven hours of delay. Another CTO reminder about the engineering handbook.
The handbook is 47 pages. It lives in Notion. The AI cannot read Notion.
This is the gap. Every standard your team built — code review checklists, deployment policies, approval gates, framework conventions, security guardrails — was designed for people to follow. The entity now writing the code isn't a person.
If you train the human but not the agent, you've trained the interface. The AI still defaults to public patterns. Your standards might as well not exist.
Agent skills proliferating without governance
Your engineers have personal skills folders on their machines. They're building agent skills, sharing them over Slack, copying them from GitHub, adapting from Claude's documentation. No inventory. No review. No standard.
Each skill is a piece of context that shapes how the agent behaves. Some are useful. Some encode outdated patterns. Some contradict each other. When 40 engineers have 30 skills each, you have 1,200 fragments of context running across your engineering org — and no way to see what they are or recall the bad ones.
The CTO who doesn't solve this isn't shipping slower AI adoption. They're shipping faster entropy.
Your engineering standards, deployed as agent skills
Agent skills are the structural fix. A skill is a named, versioned unit of context — instructions, examples, conditions — that an AI loads when relevant. The difference between a 47-page Notion doc and a skill isn't format. It's that the skill loads into every agent in the fleet, automatically, before the engineer touches the keyboard.
Authoring
Your senior engineers encode their judgment directly. Framework conventions, review checklists, deployment gates, security requirements. Structured as deployable agent skills at the moment of capture.
Governance
Every skill passes three enforcement layers before it reaches your engineers' machines: structural validity, automated evaluation, and organizational sign-off. Your security team reviews the skills that touch auth and billing. Domain leads review the ones that encode process standards.
Fleet deployment
When your platform team publishes a skill to the Enterprise layer, it sits on every engineer's machine. Can't be turned off. Updates centrally. The code review checklist isn't a memo anymore — it's a contract that runs before every PR.
Role plugins: senior engineer judgment at fleet scale
A Backend Engineer plugin contains your preferred framework patterns, logging standard, database conventions, the trigger for “auth changes require security review,” the code review checklist as an active gate. A Data Engineer plugin contains your warehouse conventions, PII handling rules, the “no production table without a staging migration” rule. A new hire joins Monday. The role plugin is already loaded. The AI knows how your company works before the engineer does.
Start with what your agents are already running
Before you can govern it, you need to see it. Invoked reads the skills paths your agents discover from — read-only, no source code, no repo permissions. You get a map of every skill running across your engineering org: who wrote it, whether it was reviewed, what it instructs the agent to do. Most engineering leaders are surprised by what they find.
What comes after
If what we find together is meaningful, we run a 90-day paid pilot with one team. Authoring studio. Governance layer. Fleet deployment. Full invocation audit trail. Your edge cases become our roadmap. Your engineering standards become the defaults for every engineer's AI.