Does this replace code review?

No. Your reviewers still approve every merge. Agents add what a diff cannot show: the live limits, dependencies and past incidents the change touches.

What access does it need?

Read access to the repositories and pipelines you choose, plus read-only access to the environments they deploy to. You can revoke it at any time.

Can agents block or roll back a release?

Only where your policy allows it. Most teams start with comments and warnings, then let agents block known-dangerous patterns and roll back clear regressions.

Will it slow our pipelines down?

Reviews run in parallel with your existing checks and post to the pull request. You choose whether a flag blocks the merge or only warns.

AWS Partner

SOC 2 Reports

Frontier Investigationfor Change and Release Risk

Every deploy checked for risk before it ships and verified after.

Connect your repositories, pipelines and the systems they deploy to. Agents read each change before it ships, check it against what broke last time, and watch production after it lands. Risky changes reach review with the reason they are risky. Bad releases arrive with a rollback ready to approve.

Book a Demo See how it works

GitHub pull requesthigh risk

payments-service #2184: raise connection pool to 200

Change: Pool size 50 to 200 across 12 pods. Config only
Blast radius: 2,400 connections against an RDS max of 1,000
History: Same pattern caused the March 4 checkout outage
Evidence: RDS parameter group, HPA limits, past RCA
Proposal: Cap at 70 per pod or add RDS Proxy first

Reviewed in 41s. Posted to the pull request

[The work behind every release]

Teams ship many times a day.Review still depends on who is around.

01The manual work

Review

Reviewers check the diff, not the system it lands on. Nobody has time to compare each change with the limits, dependencies and past incidents it touches.

02The agent handoff

Risk scored

Frontier agents read the change against live config and incident history, flag the risk with evidence, and watch the release once it ships.

03Your engineers’ role

Approve

Set which changes need a second look. Review the flagged risk and decide what goes out and when.

[Where CloudThinker fits]

Your stack stays.Agents work inside it.

01Proposed change

Already in your pipeline

GitHub and GitLabPull and merge requests
GitHub Actions and JenkinsBuild and test runs
TerraformPlans for infrastructure changes
HelmChart and values changes

No change to your workflow

02Delivery and history

Your system of record

Argo CDRollout history and sync state
KubernetesLive cluster state
PagerDutyPast incidents and their causes
SentryErrors by release

Source of truth stays put

03Investigation

CloudThinker

Read the diffWhat the change touches and why
Match the pastIncidents caused by similar changes
Score the riskBlast radius before the merge
Verify the rolloutHealth checked after release

Read-only by default

04Response

Safer releases

GitHubRisk notes on the pull request
SlackRelease status for the team
Amazon CloudWatch, Azure MonitorPost-release health checked
Datadog and GrafanaRelease compared to baseline

Review stays with your team

Logos show common stacks. CloudThinker connects to each one through read-only access you approve.

[Example scenario]

Friday, 16:10. A one-line config change.It looks harmless in the diff.

A 25-engineer fintech team on EKS and RDS PostgreSQL, deploying 30 times a day with GitHub Actions and Argo CD. Month-end payments run on Monday.

16:10
Pull request openedSignal
payments-service #2184 raises the database connection pool from 50 to 200. One line. Two approvals already.
16:11
Agent reads the systemAgent
Checks the change against the HPA limits, the RDS parameter group and every past incident that touched this service.
16:11
Risk found, with proofAgent
12 pods at peak times 200 connections is 2,400. RDS allows 1,000. The same pattern caused the March 4 checkout outage.
16:12
Safer change proposedAgent
Cap the pool at 70 per pod, or put RDS Proxy in front first. Posted on the pull request and marked as a required check.
16:30
Author takes the fixYour team
The author switches to 70 per pod. The check passes. The release goes out before the weekend.
17:05
Release verifiedAgent
Connection count peaks at 790. Error rate and p99 flat for 30 minutes after rollout. Release marked healthy.

#payments-deploys4 messages

GitHub16:10
Pull request #2184 opened: raise payments-service DB pool to 200.
CloudThinker16:12
High risk. At peak scale this opens 2,400 connections against an RDS max of 1,000. Same pattern as the March 4 outage (INC-311). Suggest 70 per pod, or RDS Proxy first.
Payments engineer16:30
Good catch. Switched to 70 per pod, pushed.
CloudThinker17:05
v3.8.1 healthy after 30 minutes. Peak connections 790 of 1,000. No error or latency change.

from pull request to flagged risk: 2 min
repeat outage avoided in this example: 1
of production watched after release: 30 min

An illustrative example. Team, systems and times are representative, not a specific customer.

[Frontier investigation agents]

Every change gets checked twice.Before it ships and after.

Agents review changes against the running system, so a risky deploy is caught in the pull request, and a bad one is rolled back before users notice.

Read the change in context: Code, config and infrastructure diffs checked against the live limits, dependencies and versions they touch.
Learn from past incidents: Each change compared with the root causes your team has already found, so the same mistake is caught early.
Verify after release: Error rates, latency and saturation watched against the baseline once the change is live.
Hand over a rollback: A bad release comes with a rollback or a fix that runs only at the approval level you set.

[What changes]

Same team. Same tools.Far less of the work by hand.

Moment	Today	With frontier agents
Reviewing a change	The diff, read by whoever is free	The diff, checked against the live system
Knowing the blast radius	A guess in the review comments	Services, limits and dependencies listed
Past incidents	Remembered by a few senior engineers	Matched against every new change
After the deploy	Watching dashboards for a while	Verified against the baseline automatically
Bad releases	Found by users, rolled back by hand	Caught early with a rollback ready

[Integrations]

Connects to the rest of your stack.Read-only to start.

GitHub
GitLab
GitHub Actions
Jenkins
CircleCI
Argo CD
Terraform
Helm
Kubernetes
Amazon CloudWatch
Datadog
Grafana
Sentry
PagerDuty
Slack

[Adoption path]

One pilot.Then company-wide.

The rollout follows the four phases of the AWS Cloud Adoption Framework, so it fits the plan your cloud team already runs.

01Envision
Pick one busy repository
Connect one service read-only and let agents comment on pull requests in shadow mode. Compare their flags with your reviewers’.
02Align
Agree the release policy
Decide which risks block a merge, which only warn, and when agents may roll back without waiting.
03Launch
Roll out team by team
Add each team’s repositories and pipelines on the same policies, risk format and audit trail.
04Scale
Make it the default
New services launch with change review on. Flagged risks feed release checklists and platform guardrails.

[AWS guidance]

Built the way AWS says to run it.Reviews start from a known reference.

[Trust and control]

Agents do the work.Your team keeps control.

You approve every change: Agents propose. Nothing touches production until someone on your team says yes, and you set that rule per system.
Every action on the record: Each step is logged, attributed and reversible, ready for your auditors.
Certified for enterprise: SOC 2 Type II and ISO 42001, with reports in our trust center.
Runs where you need it: In our cloud, through AWS Marketplace, or inside your own account.

[Questions]

What teams askbefore they start.

Does this replace code review?: No. Your reviewers still approve every merge. Agents add what a diff cannot show: the live limits, dependencies and past incidents the change touches.
What access does it need?: Read access to the repositories and pipelines you choose, plus read-only access to the environments they deploy to. You can revoke it at any time.
Can agents block or roll back a release?: Only where your policy allows it. Most teams start with comments and warnings, then let agents block known-dangerous patterns and roll back clear regressions.
Will it slow our pipelines down?: Reviews run in parallel with your existing checks and post to the pull request. You choose whether a flag blocks the merge or only warns.

[Go deeper]