Airia vs Lyzr (2026)

Breadth at scale, on the vendor's own pages: Airia connects agents to thousands of data sources and enterprise systems already in use, through a named Integration Framework of out-of-the-box connectors spanning SaaS platforms, databases and CRM systems, with Model Context Protocol support making the surface open-ended rather than fixed to a catalog. The MCP direction matters here. Inbound MCP, connecting the customer's systems to Airia, is tool consumption and counts on this axis; Airia's outbound MCP, exposing its knowledge and tools to external AI clients, counts on Extensibility instead. What is not documented, and worth noting on a comparison page, is the connector list itself: no catalog enumerating which SaaS platforms and CRMs are covered appears on any surface reached, so thousands of data sources is the vendor's characterization rather than a countable set. The grade rests on the framework and the MCP route being real, which they demonstrably are, rather than on the number.

Built-in connectors, custom REST integrations and native MCP, with Architect wiring in ERP, CRM, HRIS and document systems; tools are capped at 5, 10 and 25 on the self-serve tiers.

Multi-step orchestration and multi-agent composition are both explicit on the vendor's own agent builder page. The Routing Engine is the distinguishing mechanism: it distributes tasks to the most appropriate model, agent or interface, which makes routing a first-class platform service rather than something each builder wires by hand, and the vendor lists it among its six core capabilities as an operational reliability component rather than a convenience. Multi-agent composition is documented directly: the vendor describes designing collaborative agent systems that execute complex workflows while remaining visible, manageable and resilient as they grow in scope. That is the multi-agent half this axis looks for, and the emphasis on remaining manageable at scale is consistent with the governance-first positioning throughout the record. Both no-code and pro-code construction are supported in one environment so business and technical teams work on the same agents without breaking governance, and reusable logic patterns reduce policy drift across an estate. What is not documented anywhere is the control-flow primitive set: no branching, looping or conditional constructs are named, so how expressive the builder is cannot be said from these pages.

Deterministic orchestration through SuperFlow and dynamic orchestration through a Manager agent choosing among sub-agents; full orchestration is Enterprise only.

Airia documents both scheduled runs and event triggers, so agents wake on their own as well as when someone asks.

Voice over Twilio, Telnyx and Plivo, a REST endpoint per agent, custom UIs and chat connectors, but no scheduler, cron, event or webhook trigger is documented.

Beyond connectors to live data, Airia documents an indexed knowledge pipeline with hybrid search and knowledge graphs.

Durable knowledge bases built from documents, databases, websites and live data, with a Knowledge Graph and an API-accessible retrieval layer; storage is capped at 100MB to 1GB on self-serve tiers.

Memory Objects persist across executions with a stated scope, global, user or conversation, so this is memory the agent writes and reads rather than only real-time context from data sources. The missing lifetime and purge path keep it at Partial.

Cognis stores interactions, updates stale memories and remembers the same user across sessions, with session memory and org-level Global Context; no review or delete surface is documented.

The Full rests on the documented human approval steps: an approval step, a webhook approval that waits for an external callback, a form review and a pause step. Agent Constraints and runtime guardrails limit what an agent can reach and output, which is a different control from a person approving its work.

Guardrails that block, flag or reroute output, a Hallucination Manager, RBAC and audit logs, but no human approval step or review queue is documented.

A Vanta trust center exists at trust.airia.com but renders no certification content, and the FAQ states alignment with SOC 2 and ISO rather than a held report, which the published bar reads as Partial; a readable report entry on the trust center would move this to Full.

RBAC, SSO/SAML and audit logs, with HIPAA and SOC 2 stated in the docs and further badges on the site; no trust portal or named auditor has been found.

The strongest cell on the record. AI Discovery is the distinguishing capability and is rare enough to name: it inventories every agent in the organization, including ones the vendor did not build, ones embedded inside licensed third-party software, and ones employees connected personally. The vendor's framing is that whether agents were purpose-built, commercially adopted or never approved, the same controls apply the moment Airia is deployed. Observing agents you did not deploy is a materially different proposition from logging your own runs. Around it sit unified analytics on usage, cost and impact, continuous real-time monitoring, and audit-ready reporting, so what happened, what it cost and whether it was compliant are all answerable. The item that clears the reporting-is-not-auditing line decisively is continuous governance documentation, automatically generated and mapped to the EU AI Act, NIST AI RMF and SR 11-7, HIPAA, ISO 42001 and SOC 2. That is not a dashboard someone reads; it is evidence produced in the form an auditor asks for. Worth noting the asymmetry with Security, which sits at Partial: Airia produces compliance evidence for its customers more convincingly than it evidences its own certification status.

Step-by-step run traces with per-step latency, audit logs and API-accessible monitoring; logs are kept seven days on self-serve tiers, custom on Enterprise.

Airia can be deployed within the customer's own dedicated cloud environments across four providers: AWS, Microsoft Azure, Google Cloud Platform and Oracle Cloud Infrastructure. The published bar names region selection, VPC, on-premises or running inside the customer's own cloud as qualifying, so no self-host or on-premises build is needed, and Airia publishes a dedicated Deployment Options page documenting exactly that. The government cloud coverage settles it, with AWS GovCloud and Azure Government Cloud both named; those are FedRAMP-boundary environments that exist precisely for workloads that cannot sit in commercial regions, and a vendor does not list them unless the deployment is real. A fully managed SaaS option sits alongside on AWS, Azure and Google Cloud with a 99.9 percent uptime commitment and automated backup and disaster recovery, so the customer chooses between the vendor operating it and running it in their own account.

Managed cloud, on-premise inside the customer's VPC, or hybrid with data connectors on-premise, plus regional hosting and air-gapped operation; on-prem deployment is an Enterprise entitlement.

Vendor-supplied adoptable material, which is what the wider bar asks: whether packaged assets can be adopted ready-made by any route. The agent builder page describes leveraging trusted agent templates and reusable logic patterns to reduce policy drift and ensure consistent execution across the enterprise, and policy templates are referenced alongside them. The framing is unusual and coherent with the rest of the record: templates here exist to make governance consistent rather than to save build time, which is a different argument for the same feature. No template count is given, because none is published on a vendor page. The template library itself was not seen: a browsable catalog would let a count be cited first party, and a library of only a handful of patterns would put this cell back in question against the wider bar.

Blueprints are a metered entitlement (Few on self-serve tiers, All on Enterprise), the Studio offers templates, and named role agents ship with their own pages.

The vendor states agents are built and run across any model, framework and data source, and a customer quote on the platform page puts it in the buyer's own words: the ability to use any model within a guided and controlled environment. Bring-your-own-model flexibility and multi-model orchestration are described consistently across pages, all first party. The Routing Engine is what lifts this above a model picker. It distributes tasks to the most appropriate model automatically, so model choice operates per task at runtime rather than being a setting fixed per agent, and it is positioned as an operational reliability capability, meaning routing also covers failover rather than only cost or quality. The governance framing is the part worth carrying to comparison pages. Because every agent inherits centralized policies governing model access, an administrator decides which models the organization may use while builders retain choice within that boundary. Model flexibility and model control are usually presented as opposites, and doing both well is the mature form of this axis and the one enterprises can actually adopt.

Model set per agent across OpenAI, Anthropic, Gemini, Bedrock and DeepSeek, swappable by config; the full roster and Bring Your Own Model are Enterprise only.

Airia documents an agent API, API keys, an SDK and outbound MCP that exposes its knowledge and tools to external AI clients, alongside the inbound MCP that connects customer systems to Airia.

A versioned REST API, a pip-installable Python ADK and a TypeScript SDK, with every deployed agent exposing its own endpoint, native MCP support and an open source core framework.

Beyond a prototyping studio and red teaming, Airia documents a version-comparing evaluation harness with imported test cases.

Agent Eval generates test cases and scores production readiness, and a Simulation Engine runs adversarial and regression scenarios; Agent Eval is Enterprise only.

Every action path Airia describes runs through a programmatic interface: an Integration Framework of connectors to SaaS platforms, databases and CRM systems, Model Context Protocol support, and connection to thousands of enterprise data sources. Agent Constraints govern which data and tools an agent may reach, which is a tool-permission model rather than a screen-control one. Nothing describes a browser, a rendered interface, navigation, form filling or session control. The positioning makes this coherent rather than surprising. Airia's proposition is bringing agents inside a governance perimeter where every action is policy-checked and inventoried; driving software through its user interface is the opposite of that, because it produces actions that cannot be constrained by a tool policy or attributed cleanly in an audit trail. A governance-first platform has good reasons not to offer computer use. The absence was read from marketing pages rather than from Airia's documentation, which is the main limit on this cell.

No browser, desktop or computer-use capability is documented; Lyzr acts through connectors, REST calls and MCP tools. Lyzr Nitro and Lyzr Optimus are not documented on any page reached.