AI farms ship 10x the code.Your security has to keep up.
Five short chapters for leaders: how DevSecOps works today, what AI coding agents change, why the old stack falls behind, the stack that replaces it and where software development goes next.
A scanner at every gate.A person behind every scanner.
Classic DevSecOps adds a security tool to each stage of delivery. Every tool finds things, and every finding waits in a queue until a person decides what it means.
Plan
Threat modeling and security requirements
- Jira
- OWASP
An architect reviews the design
Code
Code scanners (SAST) and secret scans
- SonarQube
- Semgrep
- GitGuardian
A reviewer triages each finding
Build
Dependency and container image scans
- Snyk
- Trivy
Someone ranks the CVE list
Test
Web app scans (DAST) and a yearly pentest
- OWASP ZAP
- Burp Suite
A tester writes up the report
Deploy
Policy gates and change approval
- Terraform
- ServiceNow
A board approves the release
Run
Cloud posture, SIEM and runtime alerts
- Splunk
- Datadog
- PagerDuty
The SOC and on-call triage alerts
Sample tools at each stage. Your stack may differ.
In one line
It worked because people could keep up: a few releases a week, and time between them to clear the queue.
One engineer now runsa farm of coding agents.
Engineers brief several coding agents at once, such as Claude Code, Codex, Cursor or Kiro. Each agent works its own branch, day and night.
Illustrative: a team that merged 40 pull requests a week merges 400 once each engineer runs a few coding agents.
In parallel
One branch per agent, many at once.
Around the clock
Changes land at night and on weekends.
No one to ask
The author is an agent, so nobody remembers why a line changed.
In one line
Writing code got cheap. Checking it didn't.
Ten times the changes.The same people checking them.
Every tool in the old stack hands its findings to a person. When changes grow tenfold and the team doesn't, the gap between what ships and what gets a real check grows every week.
01
Reviews pile up
Pull requests wait days for a reviewer, or get approved without a real read.
02
Real risks hide in the noise
Scanners flag more than anyone can read, so the finding that matters sits deep in a list.
03
Security tests go stale
Your apps change every week. A test from last quarter describes a different app.
04
Gates stall or get skipped
Release approvals either slow the whole farm down or get waved through.
In one line
The bottleneck isn't the tools. It's the human queue behind every tool.
Keep your tools.Let agents check every change.
Frontier Investigation Agents read each change where it lands, check whether a finding is real, prove what they find and draft the fix. Your team makes the call.
- AI farm
- Coding agents open changes in parallel, around the clock.
- Your pipeline
- The same six stages, with many times the changes.
- Frontier Investigation Agents
- Check each change where it lands and prove what they find.
- Governance
- You decide what runs on its own and what waits for approval.
Works with the tools you already run
Code and CI
- GitHub
- GitLab
- Bitbucket
- Azure DevOps
- GitHub Actions
- Jenkins
- CircleCI
Security scanners
- Semgrep
- SonarQube
- GitGuardian
- Prowler
Cloud and deploy
- AWS
- Azure
- Google Cloud
- Kubernetes
- Argo CD
- Terraform
- Vault
Monitoring and teamwork
- Datadog
- Jira
- Slack
- Microsoft Teams
Plus any tool with an MCP server.
Plan
BeforeDesign reviews a few times a quarter
Review
Review reads your business rules and the linked ticket on every change.
Code
BeforeA reviewer triages every scanner finding
Review
Review reads each pull request and suggests the fix inline.
Build
BeforeSomeone ranks a long CVE list by hand
ReviewCyber
Pipeline Analysis explains failed builds. Code Audit scans the code and verifies each finding.
Test
BeforeA yearly test of apps that change every week
Cyber
Cyber tests the apps you own and backs each finding with the request and response.
Deploy
BeforeA board approves each release
Review
Release Risk gives each release a verdict your deploy can wait on.
Run
BeforeOn-call reads alerts after a bad deploy
Resolve
Resolve checks whether a deploy broke production and drafts the fix.
You decide what runs on its own and what waits for approval. Agents never merge, and every action lands in one audit log.
Already running on production code
~97%
Review precision on FPT Cloud's live merge requests
63%
Lower tooling cost at MSM, which runs Review, Resolve and Cyber on one platform
In one line
Your people stop working queues and start making decisions.
Engineers direct agents.Agents prove their work.
Writing code is getting cheap. Knowing a change is safe isn't. Here's how the work shifts.
Writing every line
Directing coding agents
Reading every diff
Judging the evidence
Gates that stop a release
Proof that ships with every change
A security test once a year
A check on every change
Counting findings
Measuring time to fix
In one line
Shipping fast is now cheap. Shipping safe is what sets you apart.
How to get there
Start read-only.Hand over one stage at a time.
01Connect
Connect your git host, CI, scanners and cloud with read-only access. Turn Review on for one busy repository.
02Verify
Add Pipeline Analysis and run Cyber against a staging app. Compare its verified findings with your backlog.
03Fix under approval
Let agents draft fixes as merge requests. A person still merges.
04Run continuously
Schedule Code Audit and Cyber runs, gate releases on Release Risk and track time to fix.
FAQ
Questions about agentic DevSecOps
Bring DevSecOps up to agent speed.
Frontier Investigation Agents check every change your AI farm ships, prove what they find and draft the fix. You decide what runs on its own and what waits for approval.