Assistants open PRs, edit workflow files, call tools, or trigger CI/CD.
For teams scaling AI-assisted software delivery
Know what AI-assisted workflows can change before you let them do more.
AI-assisted software delivery now reaches PRs, CI/CD, tools, credentials, and release paths. Clyra shows what those workflows can actually change.
Local/private scan first
No raw source retained by default
Redacted Agent Action BOM, path map, and evidence packet
Where the risk starts
A normal PR can change code. It can also change the workflow that uses a release token.
AI tool
PR
release.yml
NPM_TOKEN
npm publish
no visible approval/proof
Clyra maps the action path so teams can see what can happen before a workflow does more.
When teams call us
The problem usually appears as an evidence or approval question.
Clyra is most useful when AI coding moves into delivery paths and someone has to explain what can run, who approved it, and what proof exists.
Security questionnaires, SOC 2 reviews, or ITGC reviews ask how AI-assisted delivery is controlled.
A normal PR can change the job that uses a standing token, publishes, or deploys.
The team cannot reconstruct the actor, workflow, credential, approval, and outcome.
Start with the artifact
Use the BOM before the theory.
These are the highest-signal resources for a first internal conversation: one sample, one system view, one checklist, and one short founder-led notes feed.
Sample
Redacted Agent Action BOM
See the artifact shape: path, credential, approval gap, missing proof, and next check.
System view Action path system viewUnderstand how a workflow, credential, reachable action, target, approval, and evidence connect.
Checklist AI coding agent securityReview CI/CD, workflow files, tools, credentials, approvals, and evidence without slowing every PR.
Field notes Founder notesTechnical notes on AI-assisted delivery control, action paths, and evidence.
System view
Inventory shows tools. The path shows reach.
Approved-tool lists show what teams are allowed to use. They do not show whether an AI-assisted workflow can reach credentials, trigger CI/CD, publish packages, or bypass approval. Clyra connects those signals into one action path.
Product flow
Map the path. Decide the boundary. Prove the outcome.
One workflow produces the three artifacts teams need to delegate safely: the path map, the Agent Action BOM, and the evidence packet.
Map the path
Shows how a request or workflow reaches credentials, CI/CD jobs, tools, release actions, approvals, and evidence.
Output: action path map
Decide the boundary
Summarizes the reachable credential, target, approval state, and recommended run / review / approve / block decision.
Output: Agent Action BOM
Prove the outcome
Leaves a receipt for high-impact workflow changes: owner, credential source, approval decision, validation, outcome, and open items.
Output: evidence packet
Why teams care
Built for the platform team asked to make AI coding safe to scale.
Platform, DevEx, and AI tooling owners usually carry the rollout. Clyra gives them one map for deciding which AI-assisted workflows can run alone, need review, need approval, or should be blocked.
Shared question: can this workflow write, execute, use credentials, call tools, deploy, or touch production?
Platform and DevEx
Give teams AI speed without creating invisible CI/CD, credential, and release risk.
Engineering leadership
Keep AI coding adoption moving without losing track of which workflows can change real systems.
Release, audit, and customer trust
Answer customer, audit, or incident questions with evidence instead of tribal knowledge.
Coverage and limits
Clear about privacy, existing controls, and limits.
Approved-tool lists are a starting point. Workflow-level decisions need reachable paths, visible approvals, and evidence from connected systems. Clyra ties those signals to the action path.
Clyra is
An action-control platform that turns AI-assisted delivery paths into evidence a team can review.
Clyra is not
A generic AI inventory, SIEM, IAM, PAM, CNAPP, GRC tool, model gateway, or CI/CD replacement. Those controls matter; the path shows where they are used to change systems.
Existing controls
Existing controls are not treated as missing by default. Each path is resolved as detected, declared, externally referenced, not applicable, or unresolved based on evidence.
Not every path is high risk
Source-only, non-prod, and low-impact workflows are separated from paths that can write, execute, use credentials, deploy, publish, or affect production.
Source privacy
Local or private scanning comes first. Raw source is not retained unless explicitly agreed; the useful output is a redacted path map, Agent Action BOM, and evidence packet.
Coverage limits
Static discovery can show reachable paths and missing approval evidence. Runtime enforcement, final outcome verification, and cloud/IAM depth depend on connected systems.
FAQ
Short answers for engineering, platform, and release reviewers.
Practical answers for teams deciding what stays fast, what needs approval, and what evidence should remain after AI-assisted work reaches delivery systems.
What is Clyra?
Clyra helps teams see what an AI-assisted software delivery workflow can change: which repo, workflow, credential, tool, release path, approval, and evidence are involved.
What problem does Clyra Control solve?
Approved-tool policies are useful, but they do not show whether the delivery environment actually enforces the boundary. Clyra shows when AI-assisted work can change workflow files, reach CI/CD secrets, call tools, publish packages, run cloud commands, or trigger release automation.
Will this slow developers down?
No. Normal coding should stay fast. Review should focus on actions that can use credentials, change workflows, call tools, deploy, publish, or affect production.
Can AI coding agents access CI/CD secrets?
Sometimes indirectly. The agent may not read a secret directly, but an AI-assisted PR can change a workflow, package script, tool config, agent instruction, or release path that later runs with CI/CD secrets or release credentials.
How should teams approve AI-generated PRs?
Normal code review should stay fast. Approval should become explicit when the PR can change workflow files, use credentials, call tools, deploy, publish, run cloud commands, or affect production.
What evidence should AI-assisted software delivery leave?
Teams should keep evidence of the actor, workflow, changed files, reachable credential, target action, owner, approval reason, validation, outcome, and any unresolved gaps.
Why not use GitHub, Okta, CI/CD approvals, or an agent gateway?
Use those controls. They each see or enforce part of the path. Clyra maps the cross-system delivery path across PRs, workflows, tools, credentials, targets, approvals, and evidence so teams can see whether those controls cover the action.
Won't better AI models make this unnecessary?
No. Better models make teams delegate more work. The control question remains: what can the workflow change, under which authority, what needs review or approval, and what evidence survives afterward?
How is Clyra different from secret scanning, IAM, NHI, PAM, agent gateways, or monitoring?
Those tools find secrets, identities, permissions, runtime decisions, or activity after the fact. Clyra ties them back to the delivery path: where work came from, what it can affect, and whether approval or evidence exists.
Get started
Start with one workflow or 10 recent AI-assisted PRs.
Clyra shows what changed, which credentials or release paths were reachable, and what approval or evidence remains before teams expand the pattern.
Bring
one workflow or 10 AI-assisted PRs near agent rules, CI/CD, tools, credentials, or releases
Get
action path, credential reach, approval gaps, Agent Action BOM, and evidence packet