Sub-Agents & Agentic Structures in Latitude

Latitude’s agentic framework lets you build powerful, modular, and autonomous LLM workflows. This page covers how to design, implement, and orchestrate sub-agents, specialized agents that can be invoked by other agents, enabling complex, multi-step reasoning and tool use.

What Are Agents and Sub-Agents?

Agent: A PromptL prompt with type: agent that can plan, act, and iterate autonomously, calling tools or other agents as needed. Sub-Agent: Any agent that is exposed as a callable function/tool within another agent, allowing for modular, reusable, and composable workflows.

When to Use Agents vs. Step Chains

Use an Agent when…	Use a Chain when…
The workflow is open-ended or branching	The workflow is strictly sequential
The model must decide which tools/agents to call	The steps are always the same
You want dynamic planning or iteration	You want deterministic, fixed steps

If you find yourself writing lots of conditional logic in a chain, consider switching to an agentic approach.

Defining an Agent in PromptL

To turn any prompt into an agent, add the following to your configuration header:

---
type: agent
provider: openai
model: gpt-4o
tools:
  - latitude/search
maxSteps: 40
---

type: agent enables agentic mode.
tools: lists external tools the agent can call.
maxSteps: (optional) limits the number of agent cycles.

Exposing Sub-Agents

You can expose other PromptL agent files as callable sub-agents using the agents: configuration key:

---
type: agent
agents:
  - agents/summarizer
  - agents/sentiment_analyzer
  - agents/researcher
---

Each listed agent becomes available as a callable function/tool. Sub-agents can themselves call tools or other sub-agents, enabling deep composition.

Sub-Agent Design Patterns

1. Single-Responsibility Helpers

Keep sub-agents focused. Example: a summarizer agent that only summarizes text.

---
type: agent
schema:
  type: object
  properties:
    summary: { type: string }
  required: [summary]
---

<system>
You are a summarizer. Return a concise summary of the provided text.
</system>

<user>
{{ input_text }}
</user>

It is essential that you include parameters in subagents so that the main agent can send them information.

2. Specialist Pool

A generalist agent can delegate to a pool of specialists:

---
type: agent
agents:
  - agents/summarizer
  - agents/sentiment_analyzer
  - agents/fact_checker
---

The agent can decide which specialist to call based on the task.

3. Sequential Orchestration

For strict order, use <step> blocks and specify which agent to call:

<step agents={{ ["agents/parser"] }}>
Parse the raw email and extract fields.
</step>

<step agents={{ ["agents/team_lookup"] }}>
Enrich team data via LinkedIn search.
</step>

<step agents={{ ["agents/recommender"] }}>
Produce a recommendation.
</step>

Agent Loop & Execution

On each cycle, the agent can return:

Tool calls only: Latitude executes the tools, appends results, and continues.
Text + tool calls: Treated as internal thinking; tools are run.
Text only: The loop ends; this is the agent’s final answer.

The loop stops when a text-only response is returned or maxSteps is reached.

Best Practices

Single Responsibility: Each sub-agent should do one thing well.
Clear I/O: Use schema to define expected outputs for each agent.
Resource Awareness: Each sub-agent call counts toward the parent’s maxSteps.
Testing: Use the Playground to debug and trace agent/sub-agent interactions.

Example: Multi-Agent Researcher

Main Agent Configuration

---
type: agent
agents:
  - agents/web_search
  - agents/summarizer
  - agents/citation_checker
schema:
  type: object
  properties:
    report: { type: string }
---

Main Agent Prompt

<system>
You are a research assistant. Use your sub-agents to gather, summarize, and fact-check information before producing a final report.
</system>
<user>
Research the latest trends in renewable energy.
</user>

Sub-Agents

agents/web_search: Searches the web for relevant articles.
agents/summarizer: Summarizes article content.
agents/citation_checker: Verifies the credibility of sources.

Debugging & Tracing

Use the Latitude Playground to step through agent execution.
Inspect each sub-agent’s output and reasoning.
Adjust schemas and instructions for clarity and reliability.

Examples

To see how agents and subagents work in context you are welcome to check out the following example agents:

Example 1: Deep Search Agent
Example 2: Customer Support Email Generator

Summary

Sub-agents and agentic structures in Latitude unlock modular, reusable, and powerful LLM workflows. By designing clear, focused agents and orchestrating them with the agentic loop, you can tackle complex, multi-stage tasks with reliability and transparency.

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Subagents

Sub-Agents & Agentic Structures in Latitude

What Are Agents and Sub-Agents?

When to Use Agents vs. Step Chains

Defining an Agent in PromptL

Exposing Sub-Agents

Sub-Agent Design Patterns

1. Single-Responsibility Helpers

2. Specialist Pool

3. Sequential Orchestration

Agent Loop & Execution

Best Practices

Example: Multi-Agent Researcher

Main Agent Configuration

Main Agent Prompt

Sub-Agents

Debugging & Tracing

Further Reading

Examples

Summary

​Sub-Agents & Agentic Structures in Latitude

​What Are Agents and Sub-Agents?

​When to Use Agents vs. Step Chains

​Defining an Agent in PromptL

​Exposing Sub-Agents

​Sub-Agent Design Patterns

​1. Single-Responsibility Helpers

​2. Specialist Pool

​3. Sequential Orchestration

​Agent Loop & Execution

​Best Practices

​Example: Multi-Agent Researcher

​Main Agent Configuration

​Main Agent Prompt

​Sub-Agents

​Debugging & Tracing

​Further Reading

​Examples

​Summary

Sub-Agents & Agentic Structures in Latitude

What Are Agents and Sub-Agents?

When to Use Agents vs. Step Chains

Defining an Agent in PromptL

Exposing Sub-Agents

Sub-Agent Design Patterns

1. Single-Responsibility Helpers

2. Specialist Pool

3. Sequential Orchestration

Agent Loop & Execution

Best Practices

Example: Multi-Agent Researcher

Main Agent Configuration

Main Agent Prompt

Sub-Agents

Debugging & Tracing

Further Reading

Examples

Summary