Fail-closed authorization
Every agent action is checked against an authorization registry that denies by default when no rule allows it.
AI
Agentic AI
Governed AI agents that take action across the SDLC
Run AI agents that take action across the SDLC, not just answer questions. Every agent runs against a fail-closed authorization registry, on a durable runtime, with human-in-the-loop approval for state-changing actions, its own scoped and time-limited identity, and a full audit trail. This is distinct from the gateway (which routes calls) and the copilot (which assists).
- AI agents that act on your SDLC with explicit authorization, not unchecked access
- A person in the loop for every action that changes a system
- A complete, tamper-evident record of what every agent did and why
The problem
Your team wants AI to do more than suggest the next step. You want agents that open pull requests, trigger remediations, and step through multi-stage workflows. But without governance, an agent that can mutate your systems is a risk you cannot explain to an auditor: no defined boundary on what it is allowed to touch, no approval before it acts, and no record of what it actually did.
Without IntegraCI
- Agents act without an authorization check
- State-changing actions happen without sign-off
- No record of what ran, or why
- Agent credentials persist with unconstrained scope
With IntegraCI
- Every action checked against a fail-closed authorization registry
- State-changing steps pause for human approval
- Every action written to a tamper-evident audit trail
- Each agent runs on its own scoped, time-limited identity
What you get
Human-in-the-loop approval
State-changing actions pause for human approval, so an agent cannot mutate your systems unattended.
Scoped, time-limited identity
Each agent gets its own identity that is narrowly scoped and expires, limiting what it can ever touch.
Full audit trail
Every action an agent takes is recorded, giving you a complete account of what ran and why.
How it works
- 1
Authorize the agent
The agent runs against a fail-closed registry that decides which actions it is permitted to attempt.
- 2
Run on durable workflows
Work executes on durable workflows so multi-step actions survive restarts and can be tracked.
- 3
Approve and audit
State-changing steps wait for human approval and every action lands in the audit trail.
How it stays governed
The same gates everyone passes, applied here.
Gated by policy
Every agent action is evaluated against policy as code before it can proceed. The authorization registry denies by default, so an agent can only act on what a rule explicitly permits. No rule, no action, regardless of what the agent was asked to do.
Recorded, tamper-evident
Every action an agent takes lands once in a tamper-evident audit trail with the identity behind it, the step it was part of, and the outcome. You get a complete account of what ran and why without reconstructing events from scattered logs.
A human in the loop
State-changing actions pause inside the durable workflow and wait for a human to approve before they execute. An agent cannot mutate your systems unattended. The approval step is recorded whether the action is approved, rejected, or never acted on.
Works with your stack
Connect the tools you already run.
Agents act through the connectors you have configured, so the authorization registry gates actions against the tools already in your environment.
- Atlassian
- Gerrit
- Gitea
- GitHub
- GitLab
- Microsoft
- Akuity
- Amazon Web Services
- Buildkite
- CircleCI
- CNCF Tekton
- Drone CI
- Harness
- Jenkins
- Aqua Security
- DefectDojo
- Elastic
- Google Cloud
- +22 more
Who it’s for
Where teams reach for it.
Governed vulnerability remediation
An agent detects a finding, proposes a fix, and prepares a pull request. A person reviews and approves before any code reaches your repository, so nothing changes without explicit sign-off and a record to show for it.
Multi-step service onboarding
An agent steps through a service onboarding workflow, provisioning resources and wiring integrations in sequence. Each state-changing step pauses for approval, the full sequence runs on durable workflows so it survives restarts, and the complete history lands in the audit trail.
Compliance evidence collection
An agent gathers evidence across your connected tools and maps findings to compliance controls. Every step is recorded with the identity that ran it, and a person reviews before any control is marked satisfied.
Questions, answered.
Does this mean AI is acting in my systems without a person involved?
No. AI proposes and prepares actions, but anything that changes a system pauses for human approval before it executes. You stay in control of every state-changing step, and the agent cannot proceed without that approval.
How do I define what an agent is allowed to do?
Permissions are written as policy as code in the authorization registry. The registry denies everything by default, so an agent can only act on what you have explicitly granted. You do not need to think about what to block, only what to allow.
Is this the same as the AI gateway or the copilot?
No. The gateway routes model calls, and the copilot assists by answering questions. Agentic AI takes governed action: it runs against the authorization registry, executes on durable workflows, pauses for human approval, and writes every step to the audit trail.
Can I see a full history of what an agent did after the fact?
Yes. Every action, the scoped identity behind it, and the outcome are written to the tamper-evident audit trail as the workflow runs. You can reconstruct the complete sequence of what happened and why without digging through logs across multiple systems.
Put Agentic AI on your stack.
Request a demo, or read the docs to see how it fits the tools you already run.