Clyra Control for AI-assisted engineering

Map what AI-assisted engineering can change.

Clyra Control maps how AI-assisted workflows reach repos, CI/CD, tools, credentials, and deploy paths, so teams know what can run, what needs review, and what proof remains.

Action-control graph → Agent Action BOM → Evidence packet.

The control gap

Your AI policy lists approved tools. Clyra shows what those tools can change.

AI Action Control makes agents accountable by tying each action to authority, approval, and evidence.

Core object

Clyra maps the action path, not just the tool.

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.

actor authority action target approval evidence

One object for reachability, control, and proof.

What you get

A graph, a BOM, and an evidence packet.

Clyra turns one workflow into three reviewable outputs: an action-control graph, an Agent Action BOM, and an evidence packet.

01

Action-control graph

Shows how a request or workflow reaches credentials, actions, targets, approvals, and evidence.

02

Agent Action BOM

Summarizes path, authority, target, approval status, missing proof, and the next review.

03

Evidence packet

The receipt for high-impact actions: owner, credential source, approval decision, validation, outcome, and gaps.

04

Control boundary

Allow, review, approve, or block decisions for credentialed, tool, deploy, publish, cloud, or destructive actions.

Why teams care

PR review can approve code without approving the downstream action.

AI-assisted work now enters PRs, CI/CD, tool configs, package scripts, credentials, and releases. A PR can look routine while the downstream action uses standing authority.

AI-assisted PR updates a package script → CI runs it with a release token → PR approval covered code, not the credentialed action

Engineering leadership

Keep AI coding adoption moving without losing track of which workflows can change real systems.

Platform and DevEx

Give teams AI speed without creating invisible CI/CD, credential, and release risk.

Security and trust

Answer customer, audit, or incident questions with evidence instead of tribal knowledge.

What Clyra maps

Normal delivery paths that can become privileged actions.

Clyra maps delivery paths where AI-assisted work can write, execute, deploy, use credentials, call tools, publish packages, or touch production-adjacent systems.

Workflow

Release workflow can use standing authority

  • Seen in: CI workflow and release script
  • Action: write, execute, deploy-adjacent
  • Gap: approval or evidence is not visible

Engineering can keep the workflow moving while the sensitive action gets a clearer review boundary.

Tool path

MCP or tool path has unclear review boundary

  • Seen in: MCP config, tool manifest, or agent settings
  • Action: call tool, reach internal or external service
  • Gap: owner, purpose, or policy is missing

Platform and DevEx can see which tool paths should be registered, approved, or constrained first.

Instruction

Agent rules can imply privileged delivery behavior

  • Seen in: agent instructions or repo rules
  • Action: deploy, database, cloud, or tool operation hints
  • Gap: review candidate, not confirmed runtime execution

Teams can separate real action paths from review candidates without treating every AI file as an incident.

Execution

Package script can execute with CI authority

  • Seen in: package script and CI job
  • Action: execute command with repo or cloud context
  • Gap: target and credential review are incomplete

Engineering can keep the workflow while reviewers focus on the specific command, credential, and target.

PR-linked agent workflows
CI jobs and reusable workflows
MCP servers and tool manifests
Package scripts and build hooks
GitHub tokens, PATs, cloud keys, service tokens
Package registries, deploy jobs, databases, feature flags
Mutable endpoints with business impact
Missing owner, purpose, approval, policy, or evidence

System view

Inventory is not control. The graph shows reachability.

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

Context Mapped path Approval / policy boundary
Delegation path
human request
agent workflow
repo / PR
Authority path
credential
reachable action
target system
Control path
approval rule
policy decision
evidence packet

How it works

From delivery artifacts to action decisions.

01

Scan delivery artifacts

Clyra reads workflow files, CI jobs, MCP configs, agent instructions, package scripts, credential references, and PR-linked provenance when available.

02

Build the action-control graph

It connects the workflow to reachable actions, credentials, targets, approvals, and evidence.

03

Show decisions and gaps

Approval, policy, owner, and evidence gaps become a BOM, evidence packet, and first control boundary.

Trust boundaries

Clear about coverage, privacy, and limits.

Clyra helps teams move from approved-tool lists to action control. Discovery shows reachable paths. Enforcement depends on covered boundaries, policy, and connected systems.

Clyra Control is

An action-control platform for AI-assisted software delivery. It turns reachable workflow paths into reviewable artifacts.

Clyra Control is not

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.

Source privacy

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.

Coverage limits

Static discovery can show reachable paths and missing proof. Runtime enforcement, final outcome verification, and cloud/IAM depth depend on the systems connected.

Practical guides

Use the same model with your team.

Share these guides when platform, DevEx, release, security, and engineering leaders need concrete language for secrets, approvals, tools, and evidence.

Policy

AI coding-agent approval policy

Keep normal coding fast while requiring approval for credentialed, tool, deploy, publish, cloud, and destructive actions.

FAQ

Short answers for engineering, platform, and security 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 Control?

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.

What problem does Clyra Control solve?

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.

What is an Agent Action BOM?

An Agent Action BOM is a shareable artifact for one workflow: actor, authority, reachable action, target, approval status, missing proof, and next review.

Will this slow developers down?

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.

Will this require access to source code?

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.

How is Clyra different from secret scanning, IAM, NHI, PAM, or agent gateways?

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.

Who should own this?

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.

How does Clyra help make agents accountable?

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

Map one workflow, then expand the graph.

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.

Bring selected PR, workflow, MCP, package, infra, or release paths
Get action graph, Agent Action BOM, evidence packet, and first control boundary