docs/architecture/agents.md

Agents Architecture

DeepCritical uses Pydantic AI agents for all AI-powered operations. All agents follow a consistent pattern and use structured output types.

Agent Pattern

All agents use the Pydantic AI Agent class with the following structure:

• System Prompt: Module-level constant with date injection
• Agent Class: __init__(model: Any | None = None)
• Main Method: Async method (e.g., async def evaluate(), async def write_report())
• Factory Function: def create_agent_name(model: Any | None = None) -> AgentName

Model Initialization

Agents use get_model() from src/agent_factory/judges.py if no model is provided. This supports:

• OpenAI models
• Anthropic models
• HuggingFace Inference API models

The model selection is based on the configured LLM_PROVIDER in settings.

Error Handling

Agents return fallback values on failure rather than raising exceptions:

• KnowledgeGapOutput(research_complete=False, outstanding_gaps=[...])
• Empty strings for text outputs
• Default structured outputs

All errors are logged with context using structlog.

Input Validation

All agents validate inputs:

• Check that queries/inputs are not empty
• Truncate very long inputs with warnings
• Handle None values gracefully

Output Types

Agents use structured output types from src/utils/models.py:

• KnowledgeGapOutput: Research completeness evaluation
• AgentSelectionPlan: Tool selection plan
• ReportDraft: Long-form report structure
• ParsedQuery: Query parsing and mode detection

For text output (writer agents), agents return str directly.

Agent Types

Knowledge Gap Agent

File: src/agents/knowledge_gap.py

Purpose: Evaluates research state and identifies knowledge gaps.

Output: KnowledgeGapOutput with:

• research_complete: Boolean indicating if research is complete
• outstanding_gaps: List of remaining knowledge gaps

Methods:

• async def evaluate(query, background_context, conversation_history, iteration, time_elapsed_minutes, max_time_minutes) -> KnowledgeGapOutput

Tool Selector Agent

File: src/agents/tool_selector.py

Purpose: Selects appropriate tools for addressing knowledge gaps.

Output: AgentSelectionPlan with list of AgentTask objects.

Available Agents:

• WebSearchAgent: General web search for fresh information
• SiteCrawlerAgent: Research specific entities/companies
• RAGAgent: Semantic search within collected evidence

Writer Agent

File: src/agents/writer.py

Purpose: Generates final reports from research findings.

Output: Markdown string with numbered citations.

Methods:

• async def write_report(query, findings, output_length, output_instructions) -> str

Features:

• Validates inputs
• Truncates very long findings (max 50000 chars) with warning
• Retry logic for transient failures (3 retries)
• Citation validation before returning

Long Writer Agent

File: src/agents/long_writer.py

Purpose: Long-form report generation with section-by-section writing.

Input/Output: Uses ReportDraft models.

Methods:

• async def write_next_section(query, draft, section_title, section_content) -> LongWriterOutput
• async def write_report(query, report_title, report_draft) -> str

Features:

• Writes sections iteratively
• Aggregates references across sections
• Reformats section headings and references
• Deduplicates and renumbers references

Proofreader Agent

File: src/agents/proofreader.py

Purpose: Proofreads and polishes report drafts.

Input: ReportDraft Output: Polished markdown string

Methods:

• async def proofread(query, report_title, report_draft) -> str

Features:

• Removes duplicate content across sections
• Adds executive summary if multiple sections
• Preserves all references and citations
• Improves flow and readability

Thinking Agent

File: src/agents/thinking.py

Purpose: Generates observations from conversation history.

Output: Observation string

Methods:

• async def generate_observations(query, background_context, conversation_history) -> str

Input Parser Agent

File: src/agents/input_parser.py

Purpose: Parses and improves user queries, detects research mode.

Output: ParsedQuery with:

• original_query: Original query string
• improved_query: Refined query string
• research_mode: "iterative" or "deep"
• key_entities: List of key entities
• research_questions: List of research questions

Factory Functions

All agents have factory functions in src/agent_factory/agents.py:

def create_knowledge_gap_agent(model: Any | None = None) -> KnowledgeGapAgent
def create_tool_selector_agent(model: Any | None = None) -> ToolSelectorAgent
def create_writer_agent(model: Any | None = None) -> WriterAgent
# ... etc

Factory functions:

• Use get_model() if no model provided
• Raise ConfigurationError if creation fails
• Log agent creation

See Also

• Orchestrators - How agents are orchestrated
• API Reference - Agents - API documentation
• Contributing - Code Style - Development guidelines