Action-control graph
Shows how a request or workflow reaches credentials, actions, targets, approvals, and evidence.
Clyra Control for AI-assisted engineering
AI-assisted workflows now reach repos, CI/CD, tools, credentials, and deploy paths. Clyra Control maps the action path so teams know what can run, what needs review, and what proof remains.
Action-control graph → Agent Action BOM → Evidence packet.
The control gap
A normal PR can change code. It can also change the workflow that uses a standing token.
Clyra shows the path from assisted work to credentialed action before teams expand agent autonomy.
Core object
An action path connects actor, authority, action, target, approval, and evidence for one workflow. It gives engineering, platform, and security a concrete object to review, not another AI inventory.
One object for reachability, control, and proof.
What you get
Clyra turns one workflow into three reviewable outputs: an action-control graph, an Agent Action BOM, and an evidence packet.
Shows how a request or workflow reaches credentials, actions, targets, approvals, and evidence.
Summarizes path, authority, target, approval status, proof coverage, and the next review.
The receipt for high-impact actions: owner, credential source, approval decision, validation, outcome, and gaps.
Allow, review, approve, or block decisions for credentialed, tool, deploy, publish, cloud, or destructive actions.
Why teams care
The goal is not to slow normal coding. It is to give each owner the same view of the action path, the authority behind it, and the proof that remains after high-impact work.
Keep AI coding adoption moving without losing track of which workflows can change real systems.
Give teams AI speed without creating invisible CI/CD, credential, and release risk.
Answer customer, audit, or incident questions with evidence instead of tribal knowledge.
What Clyra maps
Clyra maps delivery paths where AI-assisted work can write, execute, deploy, use credentials, call tools, publish packages, or touch production-adjacent systems.
Workflow
Engineering can keep the workflow moving while the sensitive action gets a clearer review boundary.
Tool path
Platform and DevEx can see which tool paths should be registered, approved, or constrained first.
Instruction
Teams can separate real action paths from review candidates without treating every AI file as an incident.
Execution
Engineering can keep the workflow while reviewers focus on the specific command, credential, and target.
System view
Clyra shows where a normal workflow becomes authority to change a system: which credential is used, which action is reachable, which target is affected, and what approval or evidence exists.
Product view / Action-control graph
How it works
Clyra reads workflow files, CI jobs, MCP configs, agent instructions, package scripts, credential references, and PR-linked provenance when available.
It connects the workflow to reachable actions, credentials, targets, approvals, and evidence.
Approval, policy, owner, and evidence gaps become a BOM, evidence packet, and first control boundary.
Trust boundaries
Clyra helps teams move from approved-tool lists to action control. Discovery shows reachable paths. Enforcement depends on covered boundaries, policy, and connected systems.
An action-control platform for AI-assisted software delivery. It turns reachable workflow paths into reviewable artifacts.
A generic AI inventory, SIEM, IAM, PAM, CNAPP, GRC tool, model gateway, or replacement for your CI/CD controls. Those tools matter; Clyra shows which delivery paths use them to change systems.
Clyra does not assume controls are missing. It resolves each path as detected, declared, externally referenced, not applicable, or unresolved based on available evidence.
Clyra separates source-only, non-prod, and low-impact workflows from paths that can write, execute, use credentials, deploy, or affect production.
Clyra is designed to start from local or private scanning. Raw source is not retained unless explicitly agreed; the useful output is a redacted graph, BOM, and evidence packet.
Static discovery can show reachable paths and proof that is not visible in scanned artifacts. Runtime enforcement, final outcome verification, and cloud/IAM depth depend on the systems connected.
Practical guides
Share these guides when platform, DevEx, release, security, and engineering leaders need concrete language for secrets, approvals, tools, and evidence.
CI/CD
Check whether AI-assisted PRs, workflow edits, package scripts, or CI jobs can reach secrets and deploy credentials.
Policy
Keep normal coding fast while requiring approval for credentialed, tool, deploy, publish, cloud, and destructive actions.
MCP
Map each tool call to its credential, target system, approval boundary, and proof.
Evidence
Know which fields should exist after AI-assisted work reaches CI/CD, credentials, tools, releases, or production.
FAQ
Practical answers for teams deciding what stays fast, what needs approval, and what evidence should remain after AI-assisted work reaches delivery systems.
Clyra Control maps what AI-assisted workflows can change across repos, CI/CD, tools, credentials, and deploy paths, then shows what can stay fast and what needs review.
Approved-tool lists do not show when AI-assisted work can change workflow files, reach CI/CD secrets, call tools, publish packages, run cloud commands, or trigger release automation.
Not exactly. Monitoring shows what happened after an agent or workflow runs. Clyra maps what the workflow can change before it becomes a control problem: authority, action, target, approval, and proof.
An Agent Action BOM is a shareable artifact for one workflow: actor, authority, reachable action, target, approval status, proof coverage, and next review.
No. Clyra is meant to keep normal coding fast and review only actions that can use credentials, change workflows, call tools, deploy, publish, or affect production.
Clyra supports local or private scanning. Raw source is not retained unless agreed; most signals come from workflows, package scripts, tool config, and credential references.
Those tools find secrets, identities, permissions, or runtime decisions. Clyra ties them back to the engineering path: where work came from, what it can affect, and whether approval or proof exists.
Clyra does not assume a control is missing just because it is not visible in one artifact. It labels coverage as detected, declared, externally referenced, not applicable, or unresolved for the specific action path.
Ownership usually starts with engineering leadership, platform, DevEx, CI/CD, release engineering, or AI tooling. Security joins when customer, audit, or incident evidence is needed.
AI Action Control makes agents accountable by tying each action to authority, target, policy, review, and evidence. Clyra starts with one mapped workflow so the path is reviewable and provable.
Get started
Start with one AI-assisted delivery path close to PRs, CI/CD, credentials, tools, or releases. Clyra maps what it can change, which authority it uses, and what approval or proof is missing.