Platform teams sit on the tools everyone else depends on: CI/CD, infrastructure, observability, ticketing, and internal APIs. Cuttlefish lets AI do real work across those systems through a governed map of exactly what it can reach and do — no scattered credentials, no raw tool sprawl, and a clear record behind every action.
Most AI tools are happy to suggest a fix, but they can’t safely touch your pipelines, clusters, dashboards, or internal services. The only way to let them in is to hand over secrets and a wall of unscoped tools — and that’s exactly what your team can’t do.
Giving an AI assistant access usually means dropping API keys, tokens, and service credentials somewhere it can read them. That spreads your most sensitive secrets across yet another surface, with no clean way to scope, rotate, or take them back. Cuttlefish keeps those secrets in secure storage and never surfaces them into a conversation or hands them to a model.
Wiring every internal system in as an undifferentiated pile of tools lets the model do anything any of them allows — including the destructive things. There’s no boundary between “read the rollout status” and “delete the namespace.” Cuttlefish turns that pile into a scoped map where each action carries its own limits.
When AI does touch a real system, you’re left without a clean answer to “what did it change, and who approved it?” Reviews and incident write-ups turn into log archaeology. Cuttlefish keeps a tamper-proof record of every action, so the answer is already there when someone asks.
Cuttlefish takes the systems your platform already runs and turns them into a clear, scoped set of things AI is allowed to do — then keeps that boundary on for every action.
Point Cuttlefish at the internal systems your team runs — code, pipelines, clusters, cloud accounts, observability, ticketing, and your own services — through guided setup, with credentials kept in secure storage.
From each system’s specs and manifests, Cuttlefish builds a clear map of the specific things it can do — read this status, open that change, run this check — instead of exposing the whole tool to the model.
Every action gets a clear boundary before it can be used: look-only, prepare-only, or ask-first. The model only ever sees what you’ve allowed, so reads stay quiet and anything consequential waits for a person.
Approved work runs through the same boundary every time and leaves a tamper-proof record of what it touched, what changed, who said yes, and where the result went — ready for a review or an audit.
The difference between “AI can reach our systems” and “AI has a clear, limited set of things it can do in our systems” is the difference between a risk you can’t take and one you can sign off on.
Each capability is a specific, named thing — check a deployment, summarize a build, open a change request — with its own limits on what it can reach. The model never gets a generic “do anything in this system” door, which is the whole point of a map over a tool dump.
A newly connected system starts able to look but not change. Your team can ask questions, pull status, and gather reports right away, while anything that writes, deploys, or deletes stays closed until you deliberately allow it. You widen access in one place, on your own timeline.
Every connection shows whether it’s healthy and exactly what it’s allowed to do, and any of it can be turned off in a single move. If something ever looks wrong, one stop control halts connected activity at once and returns everything to watch-only.
Platform teams don’t want one more console with its own hidden state. The way Cuttlefish is set up fits the review-and-version workflow you already trust.
What Cuttlefish can reach and what it’s allowed to do is plain configuration, not a black box buried in a UI. You can read it, version it, and reason about it the same way you do the rest of your platform — so a change to AI access goes through the same eyes a change to anything else does.
Widening access, adding a system, or tightening a boundary is a reviewable change, not a quiet toggle someone flips in the moment. It moves through the same approval and rollback discipline your team already uses for infrastructure, so AI access never drifts out from under you.
A connection can be one person’s own or one your organization provides for the whole team, managed centrally so the right people share access without each setting it up. Everyone can see which connections the company manages, so there’s no confusion about who controls what.
Cuttlefish gives platform teams an explicit operating boundary for runtime identity, environment access, provider assignment, connector authority, change control, retained state, and recovery. Existing systems stay in place; manifests and governed capabilities define how agents may interact with them.
Each runtime is bound to a user, organization, agent, active policy, model route, and set of approved capability targets rather than inheriting broad ambient access.
Teams approve available model routes and compile connector operations into classified capabilities with explicit read, prepare, approval-required, or blocked behavior.
Consequential operations pause with target and effect context. Approval and execution grants remain narrow, attributable, revocable, and connected to the resulting record.
Runs, signed governance receipts, source and outcome evidence, artifacts, and proof-completeness state support review without replacing logs, traces, or existing observability.
Runtime state, event history, pending work, and workflow records are retained so interruption is detected and recovery follows an explicit replay, repair, pause, or resume path.
Repositories, CI/CD, cloud control planes, clusters, observability, ticketing, and internal services remain systems of record behind the authority boundaries your platform team sets.
Platform work lives and dies on accountability. Cuttlefish treats approvals and proof as the default, not an add-on you bolt on later.
Routine, read-only work runs smoothly. Anything that deploys, changes infrastructure, or touches a running system pauses and shows a plain card — what it’s about to do, what it affects, whether it can be undone — with allow, deny, modify, or run-as-a-test. If you’re away from your desk, you can approve or deny from your phone.
Every consequential action keeps a record that can’t be quietly altered: the system it touched, the change it made, the person who approved it, and the result. You review it as plain, readable proof — useful for a change review, an incident write-up, or a compliance ask, with no extra work.
When a check or a routine keeps coming back — a release readiness report, a rollout triage view, a config comparison — you turn it into an installed tool your whole team opens like any other, with a version history and a way to roll back. The boundaries and proof come along with it.
Tell us the platform environment you’d connect first and what you’d want AI to do in it, and we’ll scope a pilot with the boundaries, approvals, and proof your team needs to say yes.
