Airia review

Airia is an enterprise AI governance and runtime control plane. It brings discovery, inventory, policy enforcement and governance evidence together around the models, agents and tools an organization uses. Its role is to sit across an AI stack, not just host agents built within one environment: the vendor says it can discover systems built outside its platform, including unapproved AI tools and MCP servers.

The platform’s inventory tracks agents, models and tools, with risk classification and human approvals. Its runtime layer can allow, block, require approval, redact context or log policy decisions. For agent workflows, the vendor says controls can run before a tool call or other action executes. Airia also supports designing, orchestrating and launching agents across models and data sources. The homepage presents a platform and dashboard, but does not establish whether the service is hosted, self-managed or available in both forms. Confirm deployment architecture and enforcement coverage for each system before treating it as a universal control point.

The vendor describes a platform for building and running agents across models, frameworks and data sources, alongside a governed gateway for models and tools. Its named integration surface is connections to systems including Jira, Slack, Linear, Claude Code, Notion, Google Cloud, Gemini, Salesforce, AWS, Snowflake, Microsoft Teams, Outlook and Airtable. Those connections suggest an enterprise integration layer, but the homepage does not name a REST API, webhooks, CLI, programming languages or SDKs.

That leaves implementation questions for the vendor’s technical documentation: confirm how to register agents, route model calls, connect tools and enforce policies from your own code. Confirm SDK support for your language and whether integrations use APIs, connectors or another mechanism. The homepage describes discovery and dashboard views, plus agent orchestration, but gives no code samples or protocol details. If your architecture depends on a specific SDK, deployment workflow or integration method, validate it before estimating engineering effort.

The pricing page lists a free plan at $0/month for one user, up to 10 agents and 100 agent executions per month, plus a $10 signup credit. The Individual plan is $50/month for one user and 1,000 agent executions monthly. Professional is listed at $25/month for two team members, up to 2,500 workflow executions monthly and five agent workflows. Paid prices are monthly; annual-billing prices are not published.

Team is listed at $250/month, but its limits vary in the pricing information: user limits are shown as unlimited or 15 members, while usage is shown as 10,000 agent executions or up to 50,000 workflow executions. It also lists 10 admins and either 20 agents or 100 agent workflows. Confirm which schedule applies to your account. Enterprise is custom-quoted, with custom execution limits. Individual and Team list 14-day trials; signup credits differ by plan. The pricing page lists no add-ons or overage rates, so ask how excess usage is handled before budgeting a production workload.

The vendor’s homepage presents Airia as a platform, but does not publish an open-source licence or say that its code is available. It also does not establish whether Airia can be self-hosted. Treat both as procurement questions rather than assuming you can run the control plane in your own environment.

The homepage names integrations and describes an inventory, audit trail and governance documentation, but does not explain data export formats or migration paths. Before adopting Airia as a control point, ask how to export inventory, policies, audit records and workflow definitions, and what remains usable if the service ends. The more policies and agent workflows you configure in the platform, the more migration depends on those export and portability options.

Airia’s core production promise is to enforce policy at runtime, before an agent action executes, and to retain audit evidence. The vendor also says the platform can generate governance documentation mapped to the EU AI Act, NIST AI RMF, SR 11-7, HIPAA, ISO 42001 and SOC 2. Treat that as support for governance work, not proof that a deployment meets a regulation or standard on its own.

Before rollout, map which models, tools and agent frameworks are covered by discovery and enforcement, and test what happens when policy evaluation is unavailable. The listed plans impose monthly execution limits, but the pricing information uses different agent-execution and workflow-execution measures; confirm the meter that applies to your workloads. The vendor does not publish API rate limits or service-level reliability terms on the homepage. Ask about uptime commitments, incident handling, audit-log retention, data residency and overage behavior, then validate these against your security and compliance requirements.