For engineering leadership
Keep AI coding adoption moving without losing track of which workflows can change real systems.
Action control for AI-assisted engineering
Map what AI-assisted engineering can change.
Clyra maps SDLC action paths that can write, deploy, use credentials, or touch production, then shows what owner, approval, policy, and proof are missing before teams expand AI-assisted engineering autonomy.
Local/private scan by default. No raw source retained unless agreed. First map in 5 business days.
Why teams care
The workflow may be approved. The action path may not be.
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.
For platform and DevEx
Give teams AI speed without creating invisible CI/CD, credential, and release risk.
For security reviewers
Answer a customer, auditor, or incident review with evidence, not tribal knowledge.
What action-path reviews surface
Normal workflows with governable action paths.
Findings are redacted and buyer-readable. They show which paths can write, deploy, use credentials, reach production-adjacent systems, or lack owner, approval, policy, and proof coverage.
Example 01
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.
Example 02
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.
Example 03
Agent instructions 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.
Example 04
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.
What Clyra looks for
The signals that turn AI usage into an action path.
The first workflow map is not a generic AI inventory. It looks for concrete software-delivery paths where AI-assisted work can reach privileged systems.
AI agents, review bots, or coding assistants reaching PR-linked workflows
CI jobs that can write, execute, deploy, delete, or publish
MCP servers, tool manifests, and agent configs
Package scripts that execute commands in delivery paths
GitHub tokens, PATs, cloud keys, and service tokens
Cloud, repo, release, or production-adjacent targets
Mutable endpoints or workflows with business impact
Missing owner, purpose, approval, policy, or evidence
System object
The Agent Action BOM is the artifact. The action-control graph is the system view.
Clyra connects the human request, agent or workflow, credential, action, target, risk tier, approval rule, policy decision, and evidence so engineering and platform teams can see where authority becomes operational.
Delegation path
human request
→
agent workflow
→
repo / PR
Authority path
credential
→
reachable action
→
target system
Control path
approval rule
→
policy decision
→
evidence packet
What you get
A 5-business-day workflow map your engineering and platform teams can use.
Designed for low lift from your team: two working sessions and a local/private scan by default.
Action-control graph
Two to three repos or workflows mapped into owner, task, workflow, credential, action, target, risk tier, approval, and evidence paths.
High-risk workflow register
AI-assisted paths that can write, execute, deploy, use credentials, or affect cloud and release workflows.
Agent Action BOM
Credential and authority summary, owner/purpose gaps, and recommended allow / approve / block policy.
Evidence pack
Buyer-readable evidence of actor, owner, credential source, approval decision, validation, outcome, and remaining gaps.
Typical outputs:
action-control graph, high-risk workflow register, Agent Action BOM,
evidence packet, recommended allow / approve / block policy, and an exec-readable summary.
How it works
From delivery artifacts to action decisions.
Scan delivery artifacts
Clyra scans repo artifacts, CI workflows, MCP configs, agent instructions, review bot signals, 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 evidence gaps
Owner, purpose, approval, policy, and evidence gaps surfaced as an Agent Action BOM and evidence packet.
Field Notes
CAISI is the research layer behind Clyra.
Clyra is informed by CAISI field notes on AI-assisted software delivery, agent authority, MCP/tooling, and security-control drift.
FAQ
Questions engineering teams ask before AI-assisted action paths scale.
Clyra is intentionally narrow: it traces how AI-assisted work can reach repos, CI/CD, tools, credentials, cloud paths, and release workflows, then shows where approval and proof exist or break down.
What is Clyra?
Clyra is action control for AI-assisted engineering. It maps what AI-assisted workflows can change across repos, CI/CD, tools, credentials, and deploy paths so teams know which actions can stay fast, which need approval, and what proof exists afterward.
What problem does Clyra solve?
Most AI rollout plans track usage or approved tools. Clyra maps action paths: when AI-assisted work can reach workflow files, CI/CD secrets, service tokens, cloud commands, package publishing, internal tools, or release automation.
Why should engineering teams care now?
AI coding is moving from editor assistance into pull requests, CI/CD, tools, package scripts, cloud commands, and release workflows. If the action path is not mapped, teams may approve a code change without knowing which credentialed action it can trigger or whether there will be proof afterward.
What is a workflow map?
A workflow map is a focused private review of selected repos or workflows. It returns top governable SDLC paths, write/prod/credential/release reach, owner gaps, approval gaps, an Agent Action BOM, and a redacted evidence packet.
Will this slow developers down?
The goal is not to gate every prompt or code edit. Clyra helps teams keep normal AI-assisted engineering fast while identifying 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?
The first workflow map is designed for 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.
What if we only use Cursor, Claude Code, Codex, or Copilot locally?
Local coding assistants still change the software delivery surface when their output reaches PRs, scripts, CI jobs, MCP configs, credentials, or release paths. Clyra focuses on those delivery artifacts and action paths.
Do we need MCP, an agent registry, or an agent gateway first?
No. Clyra can map action paths before a registry, gateway, or internal control plane exists. MCP is one signal, but Clyra also looks at CI workflows, package scripts, agent instructions, repo automation, credential references, cloud commands, and release-adjacent 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 maps the upstream engineering action path: where the AI-assisted work came from, what authority it can use, what it can affect, and whether approval and proof exist.
What is an Agent Action BOM?
An Agent Action BOM is a buyer-readable 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.
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.
Design partners
Private Action-Path Review.
Bring selected repos or workflows. Clyra returns the top governable SDLC paths, write/prod/credential/release reach, owner gaps, approval gaps, and a redacted evidence packet.
Bring
selected PR, workflow, MCP, package, infra, or release paths
Get
top governable paths, reach, owner gaps, approval gaps, and redacted evidence