AI DevOps Agent

Enterprise controls for AI coding agents

Policy enforcement, approval gates, model routing, and immutable audit trails — wrapped around the AI tools your engineers already use. Runs in your cloud.

Request a demoView on GitHub

Runtime config

yaml
governance:
  default: confirm
  require_approval:
    - destructive_commands
    - external_network
cloud:
  provider: aws
executor:
  strategy: local
telemetry:
  enabled: true
budgets:
  per_commit_tokens: 5000
Why it matters

The problem isn't AI agents. It's running them without guardrails.

Ship faster without losing control

Engineers keep their preferred AI tools. Security gets approval gates and audit trails. Nobody compromises.

Approve before it executes

Destructive commands, external writes, and sensitive operations require explicit confirmation before they run.

One policy across every repo

Same rules, same budgets, same audit format — whether the agent runs in VS Code, CI, or a cloud worker.

Decision loop

How every tool call gets evaluated

Step 1

Intercept

Catch the tool call at execution time — not after a bad change already landed.

Step 2

Classify

Score risk using tool metadata, policy rules, and learned approval history.

Step 3

Enforce

Allow, confirm, or block — then record the outcome for audit and future tuning.

Allow

Low-risk reads and deterministic checks run without friction.

Confirm

Sensitive operations pause for explicit human approval.

Block

Policy violations stop before the action executes.

Deployment

Same controls, any cloud

AWS
Live

Bedrock routing, IAM-scoped workers, VPC endpoints, and audit telemetry.

Microsoft Azure
Supported

Azure OpenAI, Functions, Key Vault, and private networking.

Google Cloud
Supported

Vertex AI, Cloud Run, Secret Manager, and Firestore.

Try it on one repo.

Prove approvals, audit, and policy on a real workflow. Then roll it out.

Read the docsView on GitHub