Enterprise Agent Orchestration Platform for Secure LLM Deployment

airia · Tech · 2025 · source Agentic: yes · promotional bias: med · source-backed · extracted with prompt 0.5.6 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Enterprise agent orchestration

Airia is addressing the gap between prototype agents and production enterprise agents by providing orchestration, testing, security guardrails, tool access controls, red teaming, and authentication support.

Constraints:

Enterprise teams often have application and IT expertise but limited AI-agent experience.
Agents need tool access to business systems but can perform destructive or risky actions.
Standard integration tests are insufficient for conversational and agentic behavior.
Tool calling introduces security, governance, identity, and API-key risks.
Agent constraints must act faster than humans can intervene during execution.

Architecture

Airia provides a model- and tool-agnostic canvas for assembling, testing, managing, monitoring, and constraining agentic business software. The platform adds evaluations, red teaming, security guardrails, tool-calling constraints, and authentication/identity controls so agents can call business tools without bypassing enterprise security practice.

Diagram confidence: explicit (topology described by the source).

flowchart LR
  agent-builder-canvas["Agent builder canvas"] --> tool-calling-layer["Tool calling layer"]
  agent-builder-canvas --> evaluation-and-red-teaming["Evaluation and red teaming"]
  tool-calling-layer --> security-guardrails["Security guardrails"]
  security-guardrails --> authentication-and-identity["Authentication and identity"]
  authentication-and-identity --> business-applications["Business applications"]
  evaluation-and-red-teaming --> monitoring-and-traces["Monitoring and traces"]
  monitoring-and-traces --> agent-builder-canvas
  business-applications --> tool-calling-layer

Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component	Role	Interacts with
`Agent builder canvas`	Model- and tool-agnostic platform for assembling agentic business software.	`Tool calling layer`, `Evaluation and red teaming`
`Tool calling layer`	Lets agents access business tools and applications.	`Security guardrails`
`Security guardrails`	Constrains agent actions based on context, tool use, and sensitive information.	`Authentication and identity`
`Evaluation and red teaming`	Uses adversarial personalities and conversations to test behavior, trust, and expected outputs.	`Monitoring and traces`
`Authentication and identity`	Ensures agents act with appropriate user or agent identities and credentials.	`Business applications`
`Monitoring and traces`	Captures agent behavior and failures for debugging and trust assessment.	`Agent builder canvas`
`Business applications`	Systems such as Atlassian, Salesforce, Jira, email, and internal APIs that agents may access.	`Tool calling layer`

Data flow

Teams build agents on a provider-agnostic orchestration canvas, equip them with tools, run evaluations and adversarial red-team conversations, monitor behavior, and enforce real-time tool-calling constraints based on context such as sensitive data access.

“We just wanted to put together a canvas, if you will, to assemble a Gente business software, to test it, to manage it, to monitor it”transcript, stated

“The evaluations, the red teaming, the security guard rails, a lot of the constraint stuff that I talked about earlier”transcript, stated

Why this architecture

Airia argues enterprises need a reusable orchestration and guardrail layer because agents become valuable when they call tools, but that same tool access creates security and governance risks that traditional app teams are not prepared to solve agent-by-agent.

“there's an enormous amount of complexity in these new technologies they're brand new there's not a huge knowledge set out there in the market.”transcript, stated

“There's insane value to these tool calls. The um the the value though is about letting the LLMs go get this information and interact with these agents or interact with these apps in business.”transcript, stated

Models used: LLMs · LLM-as-Judge · MCP · OAuth

Scale

The platform is model-provider, tool, and token agnostic.
“We put together a system that's agnostic of the model provider, of the tool, of the token, of whatever it is.”transcript, stated
The speaker gives a five-tool agent as the security/authentication example.
“when we go execute an agent and it has five tools that when it calls those tools it's doing it with the credentials of the user that initiated the call”transcript, stated

Alternatives considered (roads not taken)

Let each enterprise team assemble agents and security controls independently.

Rejected because: Most app and IT teams have capability but not enough AI-specific experience or reusable tooling.

“they are probably capable of doing all of this if we just give them the right tools and put it together in the right way.”transcript, stated

Rely on standard integration tests for agent QA.

Rejected because: Agent behavior depends on conversation, persona, and adversarial contexts.

“as you build these agents QA is just really tough standard integration tests just don't work right”transcript, stated

Pass API keys around to agents and MCP servers.

Rejected because: That discards decades of security practice and creates identity/governance risks.

“We're just yeeting around API keys. It's nuts. It's almost as if we've taken decades of of security practice and just thrown it out the window.”transcript, stated

Patterns

`tool-use-orchestration` (orchestration)

Airia equips agents to call enterprise tools while enforcing constraints on what actions they may take.

“that ability to let your agents go call tools, but then also not do some of the more destructive actions”transcript, stated

`safety-policy-enforcement` (governance)

Rules restrict actions such as emailing externally after the agent has looked up sensitive information.

“if an agent goes and looks up something in my Atlassian in my Jira, right? and then it goes to send an email. I don't want that email to be sent outside the company”transcript, stated

`eval-driven-development` (evaluation)

The platform evaluates whether agents produce expected outputs and behave correctly in adversarial conversation scenarios.

“How accurate were the aers that came out? How closely do they compare with what you expected?”transcript, stated

`privacy-preserving-processing` (security)

Airia focuses on identity and authentication so agents use user-scoped credentials rather than shared API keys.

“We need to give these agents identities or we need to let them use the identity of the person executing them”transcript, stated

Tradeoffs

Platform scope

Chose Provider- and tool-agnostic orchestration. over A platform tied to one model provider or tool stack..

Rationale: Enterprises need a reusable way to assemble, test, monitor, and secure agents across changing AI providers and apps.

“agnostic of the model provider, of the tool, of the token, of whatever it is.”transcript, stated

Authentication design

Chose Agent or user identity with dynamic credentials. over Shared API keys in code or email..

Rationale: Agent tool calls need to match existing enterprise authorization practice.

“doing it in a secure way with identity that matches our existing practices”transcript, stated

Gotchas (what bit them)

Agents can leak sensitive context through later tools.

Root cause: A tool call can place sensitive Jira or customer information into the agent context before another tool sends email.

Resolution: Use real-time rules that adjust allowed actions based on what the agent has done and where it is going.

“this simple idea of this if this then that statements around agent execution around tool calling and security dynamically adjusts in real time based on what that agent has done”transcript, stated

Agent red teaming cannot be a one-question test.

Root cause: Conversation behavior changes with user persona, verbosity, language ability, and adversarial pressure.

Resolution: Generate adversarial conversations with different personalities and evaluate behavior across them.

“you can't just ask it one question right you have to have a convers conversation”transcript, stated

LLM-as-judge is not automatically sufficient for high-risk red teaming.

Root cause: Red-team evaluation can be high-risk and may require more than a judge model's binary assessment.

Resolution: Treat judge models as one part of a broader evaluation approach.

“I am not convinced that you can just LLM as a judge it, especially for something like red teaming where it is a little bit more high risk.”transcript, stated

Evaluation

Airia uses adversarial red-team conversations, expected-output comparisons, behavioral trust checks, and monitoring of where agents fail rather than relying only on standard integration tests.

Metrics: Accuracy against expected answers · Behavior under adversarial personas · Tool-calling failures · Security-rule compliance · Trust in agent behavior

“How well can you trust that this agent is going to behave the right ways in these environments?”transcript, stated

Outcomes

Created a platform for assembling, testing, managing, monitoring, and securing enterprise agents.
Added evaluations, red teaming, security guardrails, and real-time constraints.
Focused on secure tool calling and agent identity as key production enablers.
Made security and guardrail features available even when agents are not built inside Airia.

The source gives limited customer metrics but states the platform is model/tool/token agnostic and addresses agents with multiple tools and business-app integrations.

“all of the security and guardrail and stuff that we that we market that we provide value with is available whether you build your agents inside area or not.”transcript, stated