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.