Not based on a specific customer deployment. The company, figures, decisions, and outcomes below are a constructed example that shows how Intended’s mechanism applies to this kind of problem. They are not measured results from a named customer. Where we publish a real, attributed customer outcome, we will say so explicitly.
Gating Unauthorized Production Deployments by AI Agents
Consider a large engineering org running many daily deployments across dozens of microservices, where AI agents drive CI/CD pipelines, rollbacks, and infrastructure scaling decisions.
01 · the challenge
The kind of problem this addresses.
02 · how it works
See the difference.
Code Push
Build
Test
Intended Gate
Deploy
Monitor
03 · the solution
What they deployed.
- — Installed the SDLC domain pack (OI-100) with 44 deployment and infrastructure intent classifications
- — Integrated Intended as a GitHub Action in every deployment workflow
- — Deployed Kubernetes admission controller for infrastructure changes
- — Configured thresholds: staging auto-approve, production requires risk < 40 or human approval
- — Enabled Slack + PagerDuty escalation for high-risk production deployments
04 · implementation
From zero to governed.
Week 1
Connect
Added Intended GitHub Action to all 40 microservice repos. Installed Kubernetes admission controller.
Week 2
Configure
Installed SDLC domain pack. Defined staging vs. production policies, risk thresholds, and team-based approval chains.
Week 3
Shadow
Ran in shadow mode across all pipelines. Tuned risk scoring to match team expectations. Zero false positives in final 3 days.
Week 4
Enforce
Enabled enforcement. All 200+ daily deployments now flow through Intended authority gates.
05 · illustrative outcomes
What this is designed to deliver.
Modeled figures for this scenario — what the workflow above is built to achieve, not measured results from a named customer.
0%
Production deploys gated
No deploy without an authority token
<0ms
Latency added per deploy
Design target — imperceptible in the pipeline
0%
Deployment decisions auditable
With cryptographic proof
0
Deployment intents classified
Via the SDLC domain pack
06 · decision replay
Example decisions, full trace.
Sample decision records that show the shape of the evidence Intended produces. Illustrative, not drawn from a live customer’s logs.
sdlc.deploy.stagingRISK: 12/100ALLOW34msDeploy cart-service v2.14.3 to staging environment
Resolved by: Policy: auto-approve staging deployments
sdlc.deploy.productionRISK: 78/100ESCALATE28msDeploy payment-service v3.8.1 to production (database migration included)
Resolved by: Lead Engineer (approved in 6m 44s after review)
sdlc.deploy.productionRISK: 22/100ALLOW31msDeploy search-service v1.22.0 to production (config change only)
Resolved by: Policy: auto-approve config-only deploys with risk < 40
sdlc.infra.scale-clusterRISK: 65/100ESCALATE26msScale checkout cluster from 8 to 24 nodes (Black Friday prep)
Resolved by: SRE Lead (approved in 2m 11s)
sdlc.deploy.rollbackRISK: 45/100ALLOW22msRollback inventory-service from v4.1.0 to v4.0.9 (error rate spike)
Resolved by: Policy: auto-approve rollbacks to known-good version
the takeaway
The point of a deployment gate is proof: every production deploy is reviewed against policy before it ships, and that review leaves a signed, auditable record. Authority tokens turn an implicit trust assumption into something an SRE or a CISO can verify.
Why this pattern matters — not a customer quote.
Start governing AI deployments
Free to start. See every AI decision from day one.