Sentienta Blog

Tag: Agent Delegation

  • Agent Delegation: A Stock Portfolio Example

    This post is a continuation of Adding Realtime Data to Your Dialogs. It’s also a continuation of A Deep-dive into Agents: Agent Delegation in which we discussed how agents can work together to solve a problem. Today we’ll showcase a real-world example of two agents using delegation to retrieve real-time data.

    In our earlier post we emphasized how delegation achieves modular adaptability—scaling efficiently as new agents are introduced without burdening a single model. This ensures that specialized tasks are handled by the most relevant agents, improving accuracy and depth of analysis.

    Today, we will expand on our previous example. Our agents, Roger and Angie, belong to a Research Team responsible for managing a stock portfolio. In this scenario, Roger wants to get the latest information on three stocks: Apple (AAPL), Nvidia (NVDA) and Alphabet (GOOG). As we demonstrated last week, we could ask Angie to research each of these tickers, but we’d rather have Roger be responsible for managing the portfolio, and delegate the actual research to Angie.

    First we’ll setup the example with Roger:

    Me: Roger – your portfolio consists of the following stock tickers [AAPL, NVDA, GOOG].

    Tue, 25 Mar 2025 17:40:37 Roger:

    Understood. Your portfolio now consists of AAPL, NVDA, and GOOG. Let me know if you need updates on their stock prices or any other related information.

    Now, we ask Roger to update the portfolio data:

    Me: Roger – get quotes for the stocks in our portfolio.

    Tue, 25 Mar 2025 17:40:37 Roger:

    Angie research this topic: AAPL stock price

    Thu, 20 Mar 2025 18:53:16 Angie:

    Topic Results:

    The last available stock price information for AAPL (Apple Inc.) as of March 24, 2025, at 19:55 (US Eastern Time) is:

    • – Open: $220.80
    • – High: $220.85
    • – Low: $220.74
    • – Close: $220.75
    • – Volume: 1,207

    Tue, 25 Mar 2025 17:58:43 Roger:

    Angie research this topic: NVDA stock price

    Tue, 25 Mar 2025 17:58:49 Angie:

    Topic Results:

    Nvidia Corporation (NVDA) Stock Information:

    • – Last Refreshed:: March 24, 2025, 19:55 (US Eastern Time)
    • – Latest Price Data (as of 19:55):
    • – Open: $120.9888
    • – High: $121.0000
    • – Low: $120.9200
    • – Close: $120.9695
    • – Volume: 95,674 shares

    This provides the most recent trading activity for NVDA stock based on the 5-minute intraday data.

    Tue, 25 Mar 2025 17:58:52 Roger:

    WORKING MEMORY Tue, 25 Mar 2025 17:58:51 Assistant research this topic: GOOG stock price

    Tue, 25 Mar 2025 17:58:56 Angie:

    Topic Results:

    The last available stock price data for GOOG (Alphabet Inc.) is from March 24, 2025, at 19:55 Eastern Time, with the following details:

    • – Open: $169.89
    • – High: $169.90
    • – Low: $169.7701
    • – Close: $169.90
    • – Volume: 1,235 shares

    These figures reflect its intraday trading activity at 5-minute intervals.

    Tue, 25 Mar 2025 17:58:59 Roger:

    All topics have been researched and are now complete. Let me know if you have any further requests.

    Key Takeaways

    • Task Delegation Sequence – Roger delegates each research task to Angie one at a time, ensuring each is completed before initiating the next. This allows for adaptive task management based on partial results.
    • Memory Utilization – Roger checks WORKING MEMORY to identify unresearched stocks, showing efficient tracking of outstanding tasks.
    • Clarifying the Request – While this example shows delegation, Roger technically did not request real-time “quotes” but rather general research on stock prices. More precise instructions would ensure real-time updates instead of prior-day summaries.

    Future Enhancements

    A fully autonomous research system would involve Angie proactively maintaining an up-to-date portfolio through scheduled tasks. Roger could then analyze these results and, based on market conditions, initiate further actions—such as requesting a risk assessment by another specialized agent.

    Scheduled task execution is an essential step toward autonomous teamwork, a concept we’ve explored in Agent Assistants. In an upcoming post, we’ll examine how Angie can autonomously maintain research reports without requiring manual intervention.

  • A Deep-dive into Agents: Agent Delegation

    In my last post, we explored how agents interact within a team. Building on that foundation, let’s examine agent delegation—a structured process in which agents assign tasks to others based on expertise, priority, and context.

    Unlike agent autonomy, which I’ll cover in a future post, agent delegation focuses on deliberate, workflow-driven collaboration among agents. Rather than acting independently, agents make informed decisions about which tasks they should handle and which should be handed off to specialized counterparts.

    Structuring Delegation in Agent Teams

    Sentienta agents operate based on personas—natural language descriptions of their expertise. These personas guide how an agent engages in problem-solving within a team. Crucially, each agent has awareness of its teammates’ expertise, as this information is embedded in the system prompt of their respective language models.

    When responding to a query, each agent evaluates both its own capabilities and how best to leverage the expertise of others. This adaptive delegation is an essential feature of Sentienta’s design. Agents iteratively work through problems, sharing insights, refining their contributions, and identifying gaps in the discussion. When an agent determines that a particular aspect of a query requires specialized attention, it can delegate the task—often providing specific instructions on how to approach it.

    This structured, dynamic handoff is what differentiates agent delegation from the broader concept of agent autonomy. While autonomy involves independent decision-making, delegation is about intelligent collaboration.

    A Practical Example: Agent-Driven Financial Analysis

    To illustrate, let’s consider a small Sentienta team analyzing financial markets. This team consists of:

    • 🔹 Financial Analyst Agent – Interprets market data, economic trends, and financial reports.
    • 🔹 Risk Assessment Agent – Evaluates market volatility, credit ratings, geopolitical risks, and sector stability.
    • 🔹 Web Research Agent – Gathers external data, such as stock performance, news reports, and regulatory changes.

    A delegated workflow might operate as follows:

    1. Financial Analyst Agent requests the Web Research Agent to gather financial reports and market performance data.
    2. Risk Assessment Agent instructs the Web Research Agent to track real-time market volatility and news on macroeconomic risks.
    3. Web Research Agent retrieves and summarizes relevant data, providing source links for deeper analysis.
    4. Financial Analyst Agent selects key companies for further investigation and delegates risk-factor analysis to the Risk Assessment Agent, requesting a review of leadership stability, credit ratings, and sector trends.
    5. If complex statistical trends emerge, an additional Data Analytics Agent might be introduced to identify patterns and forecast future performance.

    Crucially, these steps are not static. The delegation process evolves dynamically, responding to new information in real time.

    The Benefits of Task Delegation

    By structuring delegation in this way, Sentienta teams achieve modular adaptability—scaling efficiently as new agents are introduced or refined without burdening a single model. This approach ensures that specialized tasks are handled by the most relevant agents, improving both accuracy and depth of analysis.

    But what happens when agents move beyond structured delegation toward autonomous strategic decision-making? In a future post, I’ll explore how Agent Autonomy is set to redefine enterprise AI, reducing human intervention while maintaining control and reliability.