Action-control graph
Connected view of actor, workflow, repo, credential, reachable action, target, approval rule, policy decision, and evidence.
Action control for AI-assisted engineering
Map what AI-assisted engineering can change.
Clyra traces action paths across agents, repos, CI/CD, tools, credentials, approvals, and evidence so teams can keep AI coding fast without losing control of privileged actions.
Action-control graph. Agent Action BOM. Evidence packet.
Approved tools are not the same as controlled actions. Your delivery paths show what those tools can change.
Core object
Clyra maps the action path, not just the tool.
Each path connects who initiated work, what authority is used, what action can run, what system is affected, who approved it, and what evidence remains afterward.
actor
authority
action
target
approval
evidence
Product output
From path to proof.
Start with one workflow, then expand across repos, CI/CD, tools, credentials, deploy paths, approvals, and evidence.
Agent Action BOM
Shareable artifact showing path, authority, target, approval status, missing proof, and recommended next check.
Evidence packet
Redacted proof of actor, owner, credential source, approval decision, validation, outcome, and remaining gaps.
Policy boundary
Recommended allow / approve / 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 engineering is moving from suggestions into PRs, CI/CD, tools, package scripts, credentials, and release workflows. If the path is not mapped, teams may approve a code change without knowing which credentialed action it can trigger or whether proof will exist later.
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 concrete software-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 risky action gets a clearer approval 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 connects the human request, agent or workflow, credential, action, target, approval rule, policy decision, and evidence so engineering and platform teams can see where normal work becomes authority to change a system.
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 repo artifacts, CI workflows, MCP configs, agent instructions, package scripts, credential references, and PR-linked provenance when available.
Build the action-control graph
It connects owner, task, workflow, credential, reachable action, target, risk tier, approval rule, policy decision, and evidence.
Show decisions and gaps
Owner, purpose, approval, policy, and evidence gaps become an Agent Action 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. Approval or enforcement depends on covered boundaries, policy, and connected systems.
Clyra is
An action-control platform for AI-assisted software delivery. It maps actors, workflows, credentials, reachable actions, targets, approvals, policies, and evidence into reviewable artifacts.
Clyra 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 the buying group.
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 is action control for AI-assisted engineering. It connects AI-assisted workflows to the repos, CI/CD jobs, tools, credentials, deploy paths, approvals, and evidence they touch so teams know what can stay fast and what needs review.
What problem does Clyra solve?
Most AI rollout plans track approved tools or usage. They do not show when AI-assisted work can change workflow files, reach CI/CD secrets, use service tokens, call internal tools, publish packages, run cloud commands, or trigger release automation.
What is an Agent Action BOM?
An Agent Action BOM is a shareable engineering artifact that explains which agent or workflow is acting, where it was introduced, which declared tools or systems it can reach, what credential or identity it uses, what actions are reachable, and what owner, approval, policy, or evidence gaps exist.
Will this slow developers down?
The goal is not to gate every prompt or code edit. Clyra keeps normal AI-assisted engineering fast by flagging the specific actions that need approval because they can use credentials, change workflows, call tools, deploy, publish, or affect production-adjacent systems.
Will this require access to source code?
Clyra is designed to start from local or private scanning. Raw source is not retained unless explicitly agreed. The useful signals are often delivery artifacts such as workflow files, CI/CD configuration, package scripts, agent instructions, tool configuration, credential references, and release paths.
How is Clyra different from secret scanning, IAM, NHI, PAM, or agent gateways?
Secret scanning finds exposed secrets. IAM, NHI, and PAM tools inventory identities and permissions. Agent gateways can enforce runtime decisions. Clyra connects those controls back to the engineering workflow: where the AI-assisted work came from, what authority it can use, what it can affect, and whether approval and proof exist.
Who should own this?
Ownership usually starts with engineering leadership, platform, DevEx, CI/CD, release engineering, or AI tooling teams. Security reviewers often join because the output helps answer customer, audit, and incident questions with evidence instead of tribal knowledge.
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