Argy
Request a demo

Argy AI

The Argy AI copilot: accelerate configuration, governance decisions, and operations with context-aware assistance.

Argy AI is Argy’s copilot.

It is designed to help teams industrialize DevSecOps without friction by turning platform knowledge into actionable, contextual assistance—across delivery and operations.

Argy AI is not a generic chatbot. It is most valuable when it is connected to:

  • your automations catalog (golden paths),
  • configuration schemas (what can be configured and what cannot),
  • governance signals (policies, approvals, audit logs),
  • operational signals (runbooks, baselines, incidents).

What Argy AI helps you do

1) Choose the right golden path

When a developer asks “how do I ship X?”, Argy AI can:

  • recommend the best-fitting golden path,
  • explain the trade-offs (secure-by-default vs. flexibility),
  • point to the relevant documentation.

2) Configure faster (schema-aware)

Because golden paths are schema-driven, Argy AI can help:

  • explain required parameters,
  • suggest safe defaults,
  • highlight incompatible combinations,
  • generate example configurations.

This reduces the time spent rediscovering conventions and platform decisions.

3) Explain governance decisions

Governance becomes friction when the “why” is unclear.

Argy AI can help teams understand:

  • which policy failed,
  • what evidence is missing,
  • what change would make the request compliant,
  • whether an exception process exists (time‑boxed and auditable).

4) Support run & operations

In operations, time is lost when context is missing.

Argy AI can assist by:

  • surfacing the relevant runbook section,
  • summarizing ownership and escalation paths,
  • helping interpret observability baselines (dashboards/alerts),
  • suggesting next checks based on common failure patterns.

See also: Run & Operations.

Examples of practical flows

Example A — New service bootstrap

  1. “I need a service exposed publicly.”
  2. Argy AI suggests a secure-by-default golden path that fits.
  3. It generates a minimal configuration based on the schema.
  4. It highlights required governance checks (tags, network controls, artifact provenance).

Example B — “Why did my deployment fail?”

  1. A policy blocks the change.
  2. Argy AI explains the failed guardrail and links to the policy doc.
  3. It proposes a compliant alternative (e.g. approved network pattern).

Example C — Incident response

  1. An alert fires.
  2. Argy AI surfaces the runbook, recent deploy history, and ownership.
  3. It suggests a rollback path if the golden path supports it.

What Argy AI is not

  • It is not a replacement for your platform engineers.
  • It is not a “magic button” that makes insecure systems secure.
  • It should not bypass governance. The goal is to reduce friction, not remove guardrails.

Next steps