Agents

In v2, agents are optional. When a task has no explicit agent, the framework synthesizes one automatically from the task description. You only need to declare an agent explicitly when you want full control over the persona (role, background, verbose logging, etc.).

For most use cases, rely on the zero-ceremony API and let the framework do the rest. See AgentSynthesizer below for details on how synthesis works.

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Creating an Agent (explicit, power-user)

Agent researcher = Agent.builder()
    .role("Senior Research Analyst")
    .goal("Find accurate, well-structured information on any given topic")
    .background("You are a veteran researcher with 15 years of industry experience.")
    .llm(model)
    .build();

The role, goal, and llm fields are required. All other fields are optional.

Bind the agent to a specific task:

Task task = Task.builder()
    .description("Research the latest developments in AI")
    .expectedOutput("A 400-word summary")
    .agent(researcher)   // explicit agent
    .build();

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Role and Goal

The role and goal fields are the most important parts of the agent identity. They are combined into the system prompt that the agent receives before each task.

• role: The agent's title. Keep it concise and descriptive. Example: "Data Scientist", "Legal Reviewer", "Code Reviewer".
• goal: The agent's primary objective. Write this as a directive. Example: "Analyse data and identify statistical patterns".

---

Background

The background field adds context to the system prompt, giving the agent a persona and relevant domain knowledge.

Agent analyst = Agent.builder()
    .role("Financial Analyst")
    .goal("Identify investment opportunities from market data")
    .background("You are a CFA-certified financial analyst with expertise in tech sector equities.")
    .llm(model)
    .build();

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Tools

The tools field registers tools available during the ReAct loop. Each entry must be either:

• An AgentTool instance (implements AgentTool interface)
• An object with @Tool-annotated methods

Agent researcher = Agent.builder()
    .role("Researcher")
    .goal("Find up-to-date information using web search")
    .llm(model)
    .tools(List.of(new WebSearchTool(), new CalculatorTool()))
    .build();

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Other Fields

Field	Type	Default	Description
maxIterations	int	25	Maximum tool-call iterations before forcing a final answer
allowDelegation	boolean	false	Whether this agent may delegate to other agents
verbose	boolean	false	Logs prompts and responses at INFO level
responseFormat	String	""	Formatting instruction appended to the system prompt

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AgentSynthesizer

When a task has no explicit agent, the framework auto-synthesizes one using the ensemble's configured AgentSynthesizer. The synthesized agent is ephemeral -- it exists only for the duration of that task execution.

Template-based synthesis (default)

The default synthesizer extracts a role noun from the task description using a verb-to-role lookup table. No extra LLM call is made:

First word	Synthesized role
Research / Investigate	Researcher
Write / Draft / Compose	Writer
Analyze / Analyse / Evaluate	Analyst
Design	Designer
Build / Implement / Develop	Developer
Test / Verify	Tester
Summarize / Summarise	Summarizer
Review	Reviewer
Plan	Planner
(anything else)	Agent

The goal is set to the full task description. The backstory is derived from the role.

// "Research AI trends" -> role "Researcher", goal "Research AI trends"
Task task = Task.of("Research AI trends and summarise findings");

LLM-based synthesis (opt-in)

For higher-quality personas, use AgentSynthesizer.llmBased(). This makes one additional LLM call per agentless task to generate a tailored role, goal, and backstory:

Ensemble.builder()
    .chatLanguageModel(model)
    .agentSynthesizer(AgentSynthesizer.llmBased())
    .task(Task.of("Analyse the quarterly earnings report"))
    .build()
    .run();

Model requirements

AgentSynthesizer.llmBased() calls ChatModel.chat(ChatRequest) during Ensemble.resolveAgents(), before any task executes. The configured ChatModel must properly implement chat(ChatRequest) or doChat(ChatRequest) (the LangChain4j 1.x override point). A model that does not implement either will throw RuntimeException: Not implemented at synthesis time.

For tests that use a stub or fake ChatModel, prefer the default AgentSynthesizer.template() (no LLM call during synthesis) or ensure the stub returns valid JSON persona responses. See the Testing guide for patterns.

Custom synthesizer

Implement the AgentSynthesizer interface to provide your own strategy:

AgentSynthesizer customSynthesizer = (task, ctx) -> Agent.builder()
    .role("Domain Expert")
    .goal(task.getDescription())
    .background("You are an expert in this domain.")
    .llm(ctx.model())
    .build();

Ensemble.builder()
    .chatLanguageModel(model)
    .agentSynthesizer(customSynthesizer)
    .task(Task.of("Process the data"))
    .build()
    .run();

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When to use explicit agents

Use an explicit agent when you need:

• A specific persona with a crafted background
• Verbose logging for debugging a particular task
• A custom responseFormat instruction
• An agent with allowDelegation = true
• A different LLM model than the ensemble default

For all other cases, the zero-ceremony API (Task.of(), Ensemble.run()) is simpler and equally effective.