Use case · Governed AI
You want agents that triage incidents, open pull requests, and request access for you. What you can't accept is software acting in production with no boundary, no sign-off, and no record. IntegraCI puts every AI action behind the same controls you already trust for your pipeline: a governed gateway, per-tool policy, human approval on anything that writes, and a trail of what happened. The agent moves fast. You keep the say.
Who this is for
The problem
The moment a model can call your tools directly, it can do anything those tokens allow, with no one checking and no way to reconstruct it later. That is fine in a demo and unacceptable in production.
It acts with no boundary
An agent wired straight to your tools inherits whatever those tokens can do. The blast radius is the sum of every credential it can reach.
No one signed off
A model that opens pull requests, grants access, or restarts a service without a human in the loop turns a confident guess into a production change.
You can't reconstruct it
When something goes wrong, you need to show what the agent did and why. Loose prompts and ad-hoc scripts leave you guessing after the fact.
Governed AI gateway
Instead of scattering provider keys across services, agents reach the model through a single gateway. You get one place to see usage, hold a budget, and decide which provider serves which tenant.
One governed path
Model calls go through a single gateway, not scattered SDK keys. You see what was asked, what came back, and what it cost, per tenant.
Usage you can account for
Every request is metered against a budget. When a tenant runs out, calls fall back rather than run up an unbounded bill.
Your model, your terms
Point the gateway at the provider you already use. Self-host the whole path, up to an air-gapped install, when the data can't leave.
● your model provider
one path, one budget, your provider
denied unless a policy says yes
Per-tool authorization
Before any tool runs, the call is checked against policy-as-code, the same engine that gates your deployments. A tool no policy permits is denied, and every agent stays inside one tenant's boundary, enforced by the database with row-level security.
Decided per tool
Before an agent calls a tool, the request is checked against policy-as-code. The same engine that gates your deploys gates what the AI is allowed to touch.
Closed by default
A tool that no policy permits is denied. Widening what an agent can do is an explicit change you author, not a default it inherits.
Scoped to the tenant
An agent runs inside one tenant's boundary, which the database enforces with row-level security. It cannot read or act across tenants.
Human approval
Reading is automatic. Acting is not. Any step that writes (opening a pull request, granting access, restarting a service) pauses in an approval inbox with the exact action laid out, and waits for a human to approve, edit, or reject.
Anything that writes pauses
Read-only steps run. A step that changes state (opening a PR, granting access, restarting a service) waits in an approval inbox for a person to decide.
You see the proposed action
The reviewer gets the exact change the agent wants to make, in context, before it runs. Approve, edit, or reject.
Durable, not fire-and-forget
Runs are orchestrated so an approval can take minutes or days. The work resumes from where it paused, or unwinds cleanly if it has to.
Audit trail
Every step (the run, each authorization decision, each approval, each tool call) lands in a tamper-evident trail you can export. When you have to account for an AI action, you have the record, not a reconstruction.
The same trail feeds your compliance evidence, so AI activity sits alongside everything else the platform did under one set of controls.
each entry chained to the last
Scoped credentials
Each agent run gets short-lived, scoped credentials from a dedicated secrets store, tied to a time-boxed identity. There is no long-lived key to leak, and the secret never reaches the model's context or the logs.
Its own scoped credentials
Each agent gets short-lived credentials from a dedicated secrets store, scoped to exactly the tools its policy allows.
Time-boxed identity
An agent run carries an identity that expires. There is no long-lived key sitting around for a prompt to leak.
Nothing in the prompt
Secrets stay in the store and out of the model context, the logs, and the database, which holds a reference rather than a value.
The proof
The claim holds because of how it is built. Each control runs in the path, records what it did, and maps to the framework you report against.
Policy gate on every model call
A policy-as-code rule evaluates the model identity, prompt scope, and caller identity before any request reaches an AI provider. Calls that violate policy are rejected at the gateway. The rejection is written to the audit log with a reason code, so you have a record of blocked attempts as well as permitted ones.
Human approval checkpoint for state-changing actions
Any agent action that writes state (opening a pull request, requesting access, triggering a deployment) is paused and routed to a named approver inbox. The agent cannot proceed until a person confirms or denies. The decision, the approver identity, and the timestamp are recorded in the tamper-evident audit trail before execution continues.
Short-lived scoped credentials per agent session
Each agent run receives credentials scoped only to the connectors that run is authorized to use, issued from a dedicated secrets store for the duration of the session. Credentials expire automatically when the session ends. No long-lived shared tokens exist in the agent runtime, so a compromised session cannot be replayed.
Maps to
The platform maps your controls to these frameworks. The mapping helps you demonstrate them; it is not a certification.
The artifact is the proof
AI Governance Evidence Export
An exportable, time-ordered record of every model call, the policy decision that evaluated it, any human approval with approver identity and timestamp, and the connectors accessed during the session, ready to hand to an auditor.
Under the hood
This job is not a separate product. It is the platform seen from one angle. Here are the capabilities it runs on.
AI Gateway
One policy-checked path for every model call
AIAgentic AI
Governed AI agents that take action across the SDLC
AIAI Copilot
A governed copilot that drafts, you approve
GovernPolicy as Code
Write governance rules as versioned, tested code
SecureAccess Governance
Just-in-time, time-bound access instead of standing keys
GovernCompliance & Audit
Tamper-evident audit trail with one-bundle evidence export
No. The approval checkpoint is enforced by the workflow layer, not by the agent. The agent submits a proposed action and waits. It has no path to proceed without a recorded human decision, regardless of what the policy scope permits.
The policy gate evaluates every outbound call before it reaches the provider. You define which data categories are permitted in prompts, and calls that exceed scope are rejected and logged. The agent never holds direct database credentials. It calls tools, and each tool is individually gated by the same policy layer.
No. The gateway sits in front of your existing provider endpoints. You register your providers and set per-provider policies. Teams use the same developer interface regardless of which model runs behind it, and you can add or rotate providers without changing the governance rules.
Every connector action the agent takes is recorded by the platform layer the agent calls through, not by the agent itself. The agent cannot omit or alter its own record. Exported evidence bundles include connector-side records alongside the model call log, giving auditors a chain they can verify independently.
Request a demo and put an agent behind the gateway, the policy gate, and the approval inbox. Connect the model and tools you already use. Self-host when the data can't leave.