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¶

Pydantic AI Agents¶

Pydantic AI agents use the 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, oauth_token: str | None = None) -> AgentName

Note: Factory functions accept an optional oauth_token parameter for HuggingFace authentication, which takes priority over environment variables.

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

Magentic Agents¶

The following agents use the BaseAgent pattern from agent-framework and are used exclusively with MagenticOrchestrator:

Hypothesis Agent¶

File: src/agents/hypothesis_agent.py

Purpose: Generates mechanistic hypotheses based on evidence.

Pattern: BaseAgent from agent-framework

Methods: - async def run(messages, thread, **kwargs) -> AgentRunResponse

Features: - Uses internal Pydantic AI Agent with HypothesisAssessment output type - Accesses shared evidence_store for evidence - Uses embedding service for diverse evidence selection (MMR algorithm) - Stores hypotheses in shared context

Search Agent¶

File: src/agents/search_agent.py

Purpose: Wraps SearchHandler as an agent for Magentic orchestrator.

Pattern: BaseAgent from agent-framework

Methods: - async def run(messages, thread, **kwargs) -> AgentRunResponse

Features: - Executes searches via SearchHandlerProtocol - Deduplicates evidence using embedding service - Searches for semantically related evidence - Updates shared evidence store

Analysis Agent¶

File: src/agents/analysis_agent.py

Purpose: Performs statistical analysis using Modal sandbox.

Pattern: BaseAgent from agent-framework

Methods: - async def run(messages, thread, **kwargs) -> AgentRunResponse

Features: - Wraps StatisticalAnalyzer service - Analyzes evidence and hypotheses - Returns verdict (SUPPORTED/REFUTED/INCONCLUSIVE) - Stores analysis results in shared context

Report Agent (Magentic)¶

File: src/agents/report_agent.py

Purpose: Generates structured scientific reports from evidence and hypotheses.

Pattern: BaseAgent from agent-framework

Methods: - async def run(messages, thread, **kwargs) -> AgentRunResponse

Features: - Uses internal Pydantic AI Agent with ResearchReport output type - Accesses shared evidence store and hypotheses - Validates citations before returning - Formats report as markdown

Judge Agent¶

File: src/agents/judge_agent.py

Purpose: Evaluates evidence quality and determines if sufficient for synthesis.

Pattern: BaseAgent from agent-framework

Methods: - async def run(messages, thread, **kwargs) -> AgentRunResponse - async def run_stream(messages, thread, **kwargs) -> AsyncIterable[AgentRunResponseUpdate]

Features: - Wraps JudgeHandlerProtocol - Accesses shared evidence store - Returns JudgeAssessment with sufficient flag, confidence, and recommendation

Agent Patterns¶

DeepCritical uses two distinct agent patterns:

1. Pydantic AI Agents (Traditional Pattern)¶

These agents use the Pydantic AI Agent class directly and are used in iterative and deep research flows:

• Pattern: Agent(model, output_type, system_prompt)
• Initialization: __init__(model: Any | None = None)
• Methods: Agent-specific async methods (e.g., async def evaluate(), async def write_report())
• Examples: KnowledgeGapAgent, ToolSelectorAgent, WriterAgent, LongWriterAgent, ProofreaderAgent, ThinkingAgent, InputParserAgent

2. Magentic Agents (Agent-Framework Pattern)¶

These agents use the BaseAgent class from agent-framework and are used in Magentic orchestrator:

• Pattern: BaseAgent from agent-framework with async def run() method
• Initialization: __init__(evidence_store, embedding_service, ...)
• Methods: async def run(messages, thread, **kwargs) -> AgentRunResponse
• Examples: HypothesisAgent, SearchAgent, AnalysisAgent, ReportAgent, JudgeAgent

Note: Magentic agents are used exclusively with the MagenticOrchestrator and follow the agent-framework protocol for multi-agent coordination.

Factory Functions¶

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

Factory functions: - Use get_model() if no model provided - Accept oauth_token parameter for HuggingFace authentication - 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