# Triggering a Kaiya Mission

Here, we break down the entire agentic Mission—from when a user types a query to when the Mission is executed and completed using AI agents in Tellius. It applies universally to all Agentic Missions regardless of use case (e.g., revenue analysis, product discontinuation, churn prediction etc.).

{% hint style="danger" %}
To trigger a user-defined Mission or let Kaiya plan and automate the analysis step by step, click the **Deep Insight** button.
{% endhint %}

### 1. User initiates query

When the user interacts with Kaiya by entering a natural language query in the chat interface, it is evaluated in real time against a list of triggers configured for published agentic flows.

Each Kaiya Mission contains one or more trigger phrases defined during Mission creation in Composer. These triggers allow Kaiya to intelligently map user input to the correct Mission, even if the phrasing is not exact. Trigger variations are supported, meaning you can ask a question in different ways, and Kaiya will still recognize the intent.

### 2. Kaiya maps query to a trigger

If a match is found, Kaiya responds with the name and description of the matching agentic Mission, and prompts user confirmation to initiate the Mission execution. This user confirmation ensures control and transparency before any backend processing begins.

Choose “**Yes**” to continue, else select “No” to abort the Mission exection.

<figure><img src="/files/r4xCuZdYJ4lQe6uTY0dv" alt="" width="563"><figcaption><p>Matching the trigger with the Mission</p></figcaption></figure>

### 3. Mission initialization

Upon confirmation, Kaiya activates the Planner Agent. The Planner Agent's job is to:

* Interpret the Mission's objective
* Break it into logical, executable steps
* Identify which agents need to be involved
* Sequence these agents correctly

<figure><img src="/files/M8S6pN0hcUcn9t4xYSUS" alt="" width="563"><figcaption><p>Supervision Agent working behind the scenes</p></figcaption></figure>

The Planner Agent acts as the orchestrator or mission control of the entire agentic flow. It ensures the right agents are activated at the right time in the right order.

### 4. Squad of AI Agents

A message appears as follows: “*Squad of AI Agents... Finding the best agent to execute next step.*” This indicates that Kaiya is identifying and activating the most suitable agent for the task at hand. The agents activated depend entirely on the Mission steps defined in **Composer**. Agents begin executing each Mission step , using the logic defined in **Composer**. Commonly involved agents include:

| <mark style="color:blue;">**Agent Name**</mark> | <mark style="color:blue;">**Role**</mark>                            |
| ----------------------------------------------- | -------------------------------------------------------------------- |
| Validation Agent                                | Checks query structure, permissions, and data readiness              |
| Planner Agent                                   | Breaks down requests, assigns agent tasks, sequences execution       |
| Data Prep Agent                                 | Filters, joins, and loads relevant data from selected Business Views |
| Visualization Agent                             | Generates appropriate visuals (charts, tables, graphs)               |
| Insights Agent                                  | Performs root cause analysis, trend detection, and pattern mining    |
| Summary Agent                                   | Converts insights into plain-language narratives and summaries       |
| Knowledge Graph Agent                           | Maps relationships, finds entity connections, powers recommendations |

Each of these agents has a specific, modular purpose, and they are invoked only if required by the defined step logic.

<figure><img src="/files/jBr5GSyGs9ugFpqo5eit" alt="" width="563"><figcaption><p>Analytic Agent working behind the scenes</p></figcaption></figure>

Conditional logic and dependencies between steps are respected. Prompts for additional user input (e.g., filters, time periods, dimensions) may be triggered dynamically. This modular structure enables Kaiya to adapt Missions based on data state, user interaction, or complexity of analysis.

### 7. Result generation

Once the Kaiya Mission completes execution, visuals, summaries, and insights are presented back to the user. Responses appear conversational but are powered by structured backend analysis.

* Charts and graphs (auto-generated)
* Tabular data&#x20;
* Root cause explanations
* Text summaries with recommendations
* Drill-down links or options

<figure><img src="/files/JqXGkXM3i8m8nZctwUTE" alt="" width="563"><figcaption><p>Result generation</p></figcaption></figure>

### 8. Post-execution actions

* Ask follow-up questions to go deeper
* Share results or trigger a different Mission
* Request variations or filtered views of the current output

<figure><img src="/files/KrNq2qJtQFyr6qzHnzSg" alt="" width="563"><figcaption><p>Post-execution actions</p></figcaption></figure>


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://help.tellius.com/tellius-6.3/kaiya/understanding-ai-agents-and-agentic-flows/kaiya-missions/triggering-a-kaiya-mission.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.