Vega’s performance is driven by context. The more precise the context, the better the output.
AI agents are how Vega gathers that context from the right sources at the right time. When you know how they work and how to trigger them, you get:
more accurate answers in the Vega chat
better pre-meeting preparation sheet and post-meeting analysis
stronger email drafts
Vega’s AI agents
What they are and how they work
At a high level, Vega is not a single model answering everything in isolation.
Instead, Vega acts as an orchestrator. When you ask a question, or when Vega processes a meeting or an email, it evaluates what the request actually requires and then decides which specialized AI agents to activate to handle each part of the work.
What is an AI agent in Vega?
An AI agent is a specialized component inside Vega that has:
domain-specific expertise
access to dedicated data sources
a well-defined role in the overall reasoning process
Rather than treating every request the same way, Vega routes pieces of your request to the AI agent (or agents) best equipped to handle them.
Each agent knows:
where to look for the right data
what information is relevant
how to interpret that information for the user's workflows
This is what allows Vega’s answers and outputs to feel informed, precise, and grounded in your actual environment, instead of generic AI responses.
In practical terms, you can think of Vega as coordinating a panel of subject-matter experts rather than relying on a single generalist.
How Vega uses RAG under the hood
This is also where retrieval-augmented generation (RAG) comes into play.
RAG is the process by which Vega:
retrieves the most relevant information from the AI agents (firm data, economic data, market data, and more)
injects that retrieved context directly into the AI’s reasoning process
then generates the final response using both your request and the retrieved information
Because each agent performs its own targeted retrieval before the final answer is composed, Vega’s responses are always built on current, specific, and context-aware data, not on generic training knowledge alone.
In other words: AI agents gather the right context first. Then Vega thinks and responds.
The main AI agents in Vega
The AI agents inside Vega were created specifically for advisor workflows.
Each agent is responsible for a distinct domain of knowledge. When Vega processes a request, it activates only the agents that are relevant. This is how Vega maintains both accuracy and speed.
Below are the main AI agents in Vega today.
Contacts
The Contacts agent retrieves detailed client and prospect information from your CRM as well as relevant context from past email conversations. This allows Vega to personalize meeting preparation, follow-ups, and recommendations using real relationship data.
Chat query example*: "Give me the latest interactions with John Doe."
Economic data
This agent focuses on macroeconomic information such as inflation, interest rates, employment data, GDP, and broader economic indicators that influence planning decisions.
Chat query example*: “What does the latest inflation data suggest about interest rates this year?”
Market data
This agent provides market-level insights including asset class behavior, market trends, historical performance context, and financial market structure.
Chat query example*: "Why has Nvidia outperformed Intel recently?"
Web**
The Web agent retrieves external information from the web, including financial publications, regulatory content, and current news. Within the Web agent, Vega also activates specialized sub-agents, such as the IRS data agent, which provides access to tax rules, contribution limits, deadlines, and regulatory guidance.
Chat query example*: “What are the 2026 IRA contribution limits for individuals under 50?”
Firm data
This agent retrieves information about your firm including internal processes, preferences, templates, branding, and any firm-specific knowledge that has been stored in Vega. It ensures that Vega’s responses are aligned with how your firm actually operates.
Chat query example*: "What makes my firm special?"
Support
The Support agent acts as Vega’s built-in product expert. Any time you ask how something works in Vega, how to configure a feature, or how to troubleshoot an issue, this agent is activated to provide precise guidance.
Chat query example*: “What are the most important things I should know when getting started with Vega?”
*Disclaimer: These examples are written as chat queries you can use inside the Vega chat. However, these AI agents are not limited to chat. They are also triggered automatically by Vega’s other features, including pre-meeting preparation sheet, post-meeting analysis, automatic email drafts, and more.
**Important note on data quality: The Web agent does not “browse the entire internet.” Vega intentionally restricts this agent to a curated set of relevant, trustworthy, and authoritative sources. For example, Vega will never retrieve information from community forums like Reddit or other unreliable sources the way ChatGPT often does. This design choice is what allows Vega to deliver professional-grade, compliance-conscious answers rather than generic web content.
Together, these agents allow Vega to reason across your firm’s internal knowledge, your client relationships, real-world financial data, external regulatory and market information, all within a single output.
Audit Vega's AI agents
One of Vega’s strengths is transparency. You can see exactly which AI agents were used for a given response and understand how Vega built that output.
In the Vega chat
After any response:
Click the star-shaped icon next to the copy button at the start of Vega's reply
You’ll see which AI agents were activated
You’ll also see what each agent contributed to the answer
If an agent is greyed out, it simply means it was not needed for that request.
This gives you a clear, inspectable view into how Vega built the answer.
In the Vega Outlook plugin and the Vega Gmail assistant
The same AI agent system runs under the hood when you use the chat inside the Outlook plugin and the Gmail assistant.
If an AI agent is used for a chat response, Vega will include the source links at the bottom of that response.
Pre-meeting preparation sheet
For pre-meeting preparation, Vega primarily relies on the Contacts agent. This agent pulls context from your CRM and your communication history, including: past emails, CRM notes, tasks, and contact records.
Depending on how your meeting templates are configured, additional agents may also be activated to enrich specific sections.
For each major section of the preparation sheet (such as Recent activity, Vision and concerns, etc.), Vega explicitly shows the sources it used to generate the content.
This allows you to trace every insight back to its origin and understand exactly how the preparation was constructed.