Action-control graph
Shows how a request or workflow reaches credentials, actions, targets, approvals, and evidence.
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.
Agent Action BOM
Summarizes path, authority, target, approval status, missing proof, and the next review.
Evidence packet
The receipt for high-impact actions: owner, credential source, approval decision, validation, outcome, and gaps.
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.
Scan delivery artifacts
Clyra reads workflow files, CI jobs, MCP configs, agent instructions, package scripts, credential references, and PR-linked provenance when available.
Build the action-control graph
It connects the workflow to reachable actions, credentials, targets, approvals, and evidence.
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.
CI/CD
Can AI coding agents access CI secrets?
Check whether AI-assisted PRs, workflow edits, package scripts, or CI jobs can reach secrets and deploy credentials.
Policy
AI coding-agent approval policy
Keep normal coding fast while requiring approval for credentialed, tool, deploy, publish, cloud, and destructive actions.
MCP
MCP tool security for engineering teams
Map each tool call to its credential, target system, approval boundary, and proof.
Evidence
AI-assisted delivery evidence packet
Know which fields should exist after AI-assisted work reaches CI/CD, credentials, tools, releases, or production.
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