4 min read

Agent Teams

Overview

For highly complex, multi-step tasks, a single agent may struggle to maintain focus or lack the necessary combination of skills. Agent Teams solve this by orchestrating multiple specialized agents to work together.

In a team, one Supervisor agent decomposes a large task, delegates subtasks to specialized Worker agents, reviews their work, and compiles the final result.

Purpose

Agent Teams are designed for complex, automated workflows where you need:

How Team Execution Works

When you assign a task to a Team Supervisor, the execution follows a strict 4-phase model:

Phase 1: Task Decomposition

The Supervisor analyzes your request and breaks it down into concrete, actionable subtasks. It evaluates the roster of Worker agents (their roles, skills, and bios) and assigns each subtask to the most qualified worker.

Phase 2: Worker Execution (Plan-Only)

The assigned Worker agents receive their subtasks and begin execution.

Phase 3: Supervisor Review & Revision

The Supervisor reviews the output and planned actions from all Workers.

Phase 4: Final Execution

Once the Supervisor approves all worker outputs, the Planned Actions are executed for real, and the Supervisor compiles a final, cohesive summary of what the team accomplished.

Execution Strategies

When creating a team, you define how the workers should operate:

StrategyDescriptionBest For
ParallelWorkers execute their subtasks simultaneously.Independent tasks (e.g., "Search Slack AND search Jira"). Faster execution.
SequentialWorkers execute one after another. Each worker receives the output of the previous worker.Dependent tasks (e.g., "Pull data from BigQuery THEN create a dashboard from that data").

Model Tiers

Agent capability is driven by the underlying AI model. In Vantage, models are grouped into three tiers. When building agents for a team, it's common to mix tiers (e.g., a Deep Thinking supervisor with Fast workers).

TierDescriptionTypical Use in Teams
FastQuick responses, lower cost.Simple data retrieval, formatting, or single-action workers.
ProBalanced capability and speed.The standard tier for most worker agents requiring tool use.
Deep ThinkingMaximum reasoning power and context retention.Best for the Supervisor agent that must decompose complex logic and review outputs.

Agent Credits & Usage

Because Agent Teams involve multiple LLM calls across multiple agents, they consume more credits than single-agent interactions.

Base Costs:

Tool Operation Costs: Different tools consume different credit amounts based on their complexity. For example:

Tip: To optimize credit usage, assign specific, limited skills to your worker agents so they don't load unnecessary tool sets, and use the Fast model tier for workers performing simple tasks.