---
title: "Agent Development Through Natural Language"
icon: "wand-magic-sparkles"
---

# Agent Development Through Natural Language

> Use natural language to go from idea → working agent → deployed experience in minutes.

Airia’s **Build Agents with AI** capability lets you describe what you want in plain language while an AI-powered builder plans, assembles, and iterates your agent directly in **Agent Studio**.

At a high level, the flow looks like this:

1. **Describe & Plan** – Tell the AI what you’re trying to achieve in plain language.  
2. **Build & Iterate** – Watch the AI assemble the workflow on the visual canvas and refine it with you.  
3. **Test & Deploy** – Try it, fix it, and ship it—using the same AI to improve what you already have.

---

## How it Works

### 1. Describe & Plan

![Thumbnail](./Images/BWAI-chat.png) 

Start by describing your use case in natural language:

- Your **goal** (e.g., “Qualify inbound leads and create tickets in Jira.”)  
- Any **systems** it should connect to (e.g., Jira, Slack, your internal APIs)  
- Relevant **constraints** (e.g., handoff rules, approval steps, routing logic)

The Airia Agent will:

- Ask clarifying questions when needed  
- Provide **feedback** on your idea (feasibility, gaps, edge cases)  
- Generate a **structured plan** for the agent workflow tailored to your use case

Example prompts:

- “I want an agent that triages customer support requests and routes them by priority.”  
- “Help me design an agent that collects product feedback and creates Jira tickets.”

---

### 2. Build & Iterate

![Thumbnail](./Images/BWAI-inprogress.png) 

Once you approve (or adjust) the plan, the Airia Agent:

- Autonomously creates and configures steps on the **visual canvas** in real time  
- Adds **AI model blocks**, **tool actions**, **routing logic**, and **memory**  
- Can incorporate **Python steps** to support more advanced behaviors

Typical capabilities it can add for you:

- AI model integration (OpenAI / Anthropic models preconfigured for your project)  
- Tool actions (e.g., Jira, Slack, HTTP calls, internal APIs)  
- Branching and routing logic  
- Custom Python steps  
- Memory and context configuration

You can then iterate using the same natural language chat:

- “Add a fallback branch if Jira is unavailable.”  
- “Log every error into this data source.”  
- “Split high-priority issues into a separate path.”

> ℹ️ **Note**  
> While the **Build with AI** chat is building or editing your workflow, the canvas is temporarily locked for manual edits to avoid conflicting changes.

---

### 3. Test & Deploy

Once the agent is built:

1. **Test** the agent directly from Agent Studio using your own example inputs.  
2. **Ask the AI to review** and suggest improvements:
   - “Suggest areas of improvement for this agent design.”  
   - “How can I improve my existing prompts for better response quality?”  
3. **Deploy** the agent once you’re satisfied with how it behaves.

You can also use the Airia Agent to **improve existing agents**:

- Review routing and error handling  
- Suggest prompt improvements  
- Propose additional steps or guardrails  
- Troubleshoot failed executions and offer fixes

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## Why This Matters for Enterprise Teams

Enterprise teams still struggle to turn business goals into reliable AI agents:

- Business objectives are **hard to translate** into agent workflows.  
- Many agents end up being **poorly structured** and difficult to maintain.  
- Teams depend heavily on **specialized AI engineers** for even small changes.  
- Non-technical users are blocked or forced to work through long handoff cycles.

Airia’s natural-language agent development is designed to solve these problems by:

- **Removing the steep learning curve** of new AI platforms  
- **Automatically structuring reliable workflows** that follow Airia best practices  
- **Reducing dependency on specialized AI developers** for day-to-day work  
- **Empowering business users** to create and adjust agents without deep technical expertise

---

## Availability & Access

### Feature Name

- Tenant setting: **Build Agents with AI**  
- In Agent Studio: **Build with AI** chat

### Current Access Model

- The feature is **disabled by default** at the tenant level.
- Only the following roles can **enable or disable** the tenant setting:
  - **Platform Admin**
  - **Admin**
- Once enabled, **any user who can access Agent Studio** can use **Build with AI** in draft mode (subject to their existing Agent Studio permissions).

![Thumbnail](./Images/BWAI-settings.png)

> ⚠️ **Important**
> The **Build Agents with AI** tenant toggle can only be changed by **Platform Admin** and **Admin** users.

---

## Enabling Build Agents with AI for a Tenant

> 🔐 **Permissions**
> Only **Platform Admin** and **Admin** users can enable or disable **Build Agents with AI** for the tenant.


To enable the feature:

1. Go to **Settings** in the Airia platform.  
2. Open the **Build with AI** page.  
3. Turn on the toggle **Build Agents with AI**.

Once enabled, this will:

- Activate the **Build and Edit with AI** chat in **Agent Studio** for your tenant.  
- Allow any user who can access **Agent Studio** to use **Build with AI** in **draft mode**, subject to their existing Agent Studio permissions.

---

## Using Build Agents with AI in Agent Studio

### Draft-Only Editing

The **Build with AI** chat is available **only in draft mode**:

- ✅ You **can** build and edit **draft** versions of agents.  
- ❌ You **cannot** modify **published** versions via the AI chat.

To edit an existing published agent:

1. Open the agent in Agent Studio.  
2. Switch or navigate to its **draft** version.  
3. Use the **Build with AI** chat from there.

---

### Starting the Build with AI Chat

1. Navigate to **Agent Studio**.  
2. Open the agent you want to build or improve (draft).  
3. Use the **Build with AI** chat, available from the **left navigation controls** on the canvas.

From there, you can:

- Start with a **new idea** and ask the AI to design the workflow.  
- Ask questions about your **existing configuration**.  
- Request **changes or improvements** to the current design.

---

### What the Airia Agent Understands

The Airia Agent is **domain- and platform-aware**. It understands:

- Your **project configuration**  
- Existing **components and steps** in the agent  
- Which **step types** Agent Studio currently supports  
- How to map your requirements to **best-practice patterns** in Airia

This makes it a strong assistant for:

- Choosing the right step types for your use case  
- Suggesting routing and branching patterns  
- Proposing guardrails, validation, and error-handling flows

Example prompts:

- “Walk me through how this agent currently routes conversations.”  
- “Explain what each step does and where data is stored.”  
- “Recommend a better structure for this workflow given we want stricter approval.”

---

### Troubleshooting & Debugging

The Airia Agent is particularly good at **troubleshooting failing agent executions**:

- Analyze failed runs, logs, or error messages  
- Suggest **concrete changes** to steps or configuration  
- Propose additional logging, retries, or fallback paths

Example prompts:

- “This execution failed when calling the Jira tool. What should I change?”  
- “Help me debug why this branch is never triggered.”  
- “Suggest improvements to make this workflow more resilient.”

---

### Python Code Assistance

The Airia Agent can also **write and troubleshoot Python code** used in your workflows:

- Generate new Python steps based on your description  
- Propose refactors or performance improvements  
- Help you debug failed Python executions

Example prompts:

- “Write a Python step that normalizes user input and scores it from 1–5.”  
- “My Python step is failing. Here’s the error—what should I fix?”  
- “Optimize this Python logic for readability and reliability.”

---

## Billing & Model Usage

As of today:

- All executions in the **Build with AI** chat in Agent Studio are **charged to the tenant**.  
- The system is optimized for:
  - **Maximum quality** of the generated workflows and guidance  
  - **Best possible experience** for complex agent design and troubleshooting tasks

### Models Used

We currently use a combination of:

- **Sonnet 4.5**  
- **GPT 5.1**

As we expand the skills of the Airia Agent, we may:

- Route specific tasks to **smaller, specialized models** optimized for those tasks  
- Continue to tune the underlying models and instructions for **quality and reliability**

> 🔐 **Important**  
> Today, users **cannot change or override** the models used by **Build with AI**.  
> The feature is tightly optimized and tested with a specific setup to ensure consistent behavior across tenants.

---

## Known Behaviors & Limitations

- **Canvas locking during AI edits**  
  - While the **Build with AI** chat is actively building or modifying your workflow, the canvas is **temporarily blocked** from manual edits to prevent conflicts.

- **Draft-only operations**  
  - You can only use Build with AI on **draft** versions. To edit a published agent, open its draft version first.

- **Model configuration**  
  - Model selection for Build with AI is **managed by Airia** and cannot be customized per tenant at this time.

---

## Best Practices

- Start with **one clear objective** per agent (e.g., “qualify leads,” “triage tickets”).  
- Let the AI propose a **first version of the workflow**, then iterate instead of over-specifying upfront.  
- Use the AI not just to **build**, but also to **explain** and **review** your workflows regularly.  
- When something fails, **send the error/context** to the Airia Agent and let it suggest the next steps.

If you’re unsure what to ask, a good starting point is:

> “Explain how this agent works and suggest improvements to make it more reliable for production use.”
