Clarifier Agent

The Clarifier Agent provides human-in-the-loop (HITL) interaction before deep research begins. It gathers context and, when the request is vague, optionally asks the user to narrow the scope or clarify the type of output requested.

Location: src/aiq_agent/agents/clarifier/agent.py

Purpose

Deep research is expensive in both time and compute. The Clarifier reduces wasted effort by:

1. Gathering context (including optional tool calls such as web search) about the request

2. Asking focused clarification questions only when the request is genuinely ambiguous

3. Optionally clarifying the type of output the user wants (for example, report, table, comparison, prediction, or brief answer) when that is unclear

The clarifier runs on the deep research path and also when a shallow query escalates to deep. It can be disabled entirely using enable_clarifier: false in the orchestrator config.

Internal Flow

graph TD A[Receive ClarifierAgentState] --> B[Load clarification prompt] B --> C[Invoke LLM with conversation + tools] C --> D{Tool calls requested?} D -->|yes| E[Execute tools using ToolNode] E --> F[Append JSON reminder] F --> C D -->|no| G[Parse ClarificationResponse JSON] G --> H{needs_clarification?} H -->|yes| I{iteration < max_turns?} I -->|yes| J[Prompt user through HITL callback] J --> K[Append user response to messages] K --> C I -->|no| L[Auto-complete clarification] H -->|no| L[Clarification complete] L --> N[Return ClarifierResult
with clarifier_log] style A fill:#e1f5fe style N fill:#e8f5e9 style J fill:#fff3e0

State Model

ClarifierAgentState

Field	Type	Default	Description
messages	Annotated[list[AnyMessage], add_messages]	required	Conversation history with LangGraph message reducer
data_sources	list[str] or None	None	Data source IDs for tool filtering. None uses all configured tools; [] keeps only unmapped utility tools; a populated list scopes to the named sources plus unmapped utility tools.
available_documents	list[dict[str, Any]] or None	None	User-uploaded documents (file name, summary) for context; the user may refer to these
max_turns	int	3	Maximum clarification Q&A turns
clarifier_log	str	""	Accumulated clarification dialog log
iteration	int	0	Current clarification turn counter

Computed property:

• remaining_questions = max_turns - iteration

ClarifierResult

Returned to the orchestrator after the clarification dialog completes:

Field	Type	Description
clarifier_log	str	Full clarification dialog log

ClarificationResponse

Structured JSON response parsed from the LLM output during clarification:

Field	Type	Description
needs_clarification	bool	Whether more clarification is needed
clarification_question	str or None	The question to ask the user

Configuration

Configured through ClarifierConfig (NeMo Agent Toolkit type name: clarifier_agent):

Parameter	Type	Default	Description
llm	LLMRef	required	LLM for generating clarification questions
tools	list[FunctionRef | FunctionGroupRef]	[]	Tools for context gathering (for example, web search)
max_turns	int	3	Maximum clarification Q&A turns before auto-completing
log_response_max_chars	int	2000	Maximum characters to log from LLM responses
verbose	bool	false	Enable verbose logging with VerboseTraceCallback

Example YAML:

functions:
  clarifier_agent:
    _type: clarifier_agent
    llm: nemotron_llm
    tools:
      - web_search_tool
    max_turns: 3
    verbose: true

Prompt Templates

Located in src/aiq_agent/agents/clarifier/prompts/:

Template	Purpose
research_clarification.j2	Generates clarification questions. Includes conditional sections for uploaded documents context. Instructs the LLM to respond with JSON containing needs_clarification and clarification_question. Template variables: clarifier_result, available_documents, tools, tool_names

HITL Interaction Patterns

The clarifier uses NeMo Agent Toolkit’s user_interaction_manager to prompt the user. The callback is injected during registration:

Agent:  "Could you clarify whether you're interested in renewable energy
         adoption in all G7 nations or specific ones?"
User:   "Focus on Germany and Japan."
Agent:  "Got it. Are you interested in economic impacts from a GDP perspective,
         job creation, or both?"
User:   "Both GDP impact and job creation."
Agent:  [clarification complete, proceeds to deep research]

When the desired output form is unclear, the clarifier may instead ask which type of output you want (for example, a full report, a comparison table, or a brief answer) before research begins.