Principle:CrewAIInc CrewAI Agent Configuration
| Knowledge Sources | |
|---|---|
| Domains | Multi_Agent_Systems, NLP |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
A design pattern for defining autonomous AI agents through declarative configuration of role, objective, context, language model, and available tools.
Description
Agent Configuration establishes the identity and capabilities of an autonomous agent in a multi-agent system. Each agent is defined by three core attributes: a role (what the agent is), a goal (what the agent aims to achieve), and a backstory (contextual background that shapes the agent's behavior). These attributes are injected into the system prompt sent to the language model, creating a persona that guides the agent's reasoning and output style.
The technique draws on prompt engineering research showing that role-based instructions improve task performance. Beyond identity, configuration includes selecting the language model, assigning tools the agent can invoke, and setting behavioral constraints like maximum iterations, delegation permissions, and guardrails.
Usage
Use this principle when defining any agent that will participate in a crew. Every agent in both sequential and hierarchical workflows requires at minimum a role, goal, and backstory. Agent configuration is the foundational step before task assignment and crew assembly.
Theoretical Basis
Agent configuration implements the Role-Based Prompting technique combined with the ReAct (Reasoning + Acting) paradigm. The role/goal/backstory triple is compiled into a system prompt that primes the LLM for specific behavior. When tools are assigned, the agent operates in a ReAct loop: it reasons about the current state, selects a tool action, observes the result, and iterates until the task is complete or the maximum iteration count is reached.
Pseudo-code:
# Abstract agent configuration
agent = configure_agent(
role="Domain specialist description",
goal="Specific objective to accomplish",
backstory="Background context shaping behavior",
llm=select_language_model(provider, model),
tools=select_tools(domain_requirements),
max_iterations=15,
allow_delegation=False,
)
system_prompt = compile_prompt(agent.role, agent.goal, agent.backstory)