Refactor AML investigation agent

aieng-eval-agents/aieng/agent_evals/aml_investigation/__init__.py

@@ -1,5 +1,6 @@
 """Utilities for AML Investigation agent."""
 
+from .agent import create_aml_investigation_agent
 from .data.cases import (
     AnalystOutput,
     CaseFile,
@@ -9,14 +10,17 @@
     build_cases,
     parse_patterns_file,
 )
+from .task import AmlInvestigationTask
 
 
 __all__ = [
+    "AmlInvestigationTask",
     "AnalystOutput",
     "CaseFile",
     "CaseRecord",
     "LaunderingPattern",
     "GroundTruth",
     "build_cases",
+    "create_aml_investigation_agent",
     "parse_patterns_file",
 ]

aieng-eval-agents/aieng/agent_evals/aml_investigation/agent.py

@@ -0,0 +1,215 @@
+"""AML investigation agent.
+
+This module defines the primary factory used to build the AML investigation agent.
+
+The returned agent is a Google ADK ``LlmAgent`` configured to:
+
+- Investigate one AML case at a time.
+- Use read-only SQL tools for schema discovery and data retrieval.
+- Return structured output that conforms to ``AnalystOutput``.
+
+Examples
+--------
+>>> from aieng.agent_evals.aml_investigation.agent import create_aml_investigation_agent
+>>> agent = create_aml_investigation_agent()
+>>> agent.name
+'AmlInvestigationAnalyst'
+"""
+
+from aieng.agent_evals.aml_investigation.data import AnalystOutput
+from aieng.agent_evals.async_client_manager import AsyncClientManager
+from aieng.agent_evals.langfuse import init_tracing
+from google.adk.agents import LlmAgent
+from google.adk.agents.base_agent import AfterAgentCallback, BeforeAgentCallback
+from google.adk.agents.llm_agent import AfterModelCallback, BeforeModelCallback
+from google.adk.tools.function_tool import FunctionTool
+from google.genai.types import GenerateContentConfig, ThinkingConfig
+
+
+_DEFAULT_AGENT_DESCRIPTION = "Conducts multi-step investigations for money laundering patterns using database queries."
+
+ANALYST_PROMPT = """\
+You are an Anti‑Money Laundering (AML) Investigation Analyst at a financial institution. \
+Your job is to investigate one case by reviewing activity in the available database and explaining whether the \
+observed behavior within the case window is consistent with money laundering or a benign explanation.
+
+You have access to database query tools. Use them. Do not guess or invent transactions.
+
+## Core Principle: Falsification
+Start with the hypothesis that the case is benign. Prefer legitimate explanations unless the transaction-level evidence supports laundering.
+
+## Input
+You will be given a JSON object with these fields:
+- `case_id`: unique case identifier.
+- `seed_transaction_id`: identifier for the primary transaction that triggered the case.
+- `seed_timestamp`: timestamp of the seed transaction (end of the investigation window).
+- `window_start`: timestamp of the beginning of the investigation window.
+- `trigger_label`: upstream alert/review label or heuristic hint (may be wrong).
+
+### Time Scope Constraint
+**Critical**: Only analyze events with timestamps between `window_start` and `seed_timestamp` (inclusive). Exclude any events after `seed_timestamp`.
+
+## Investigation Workflow
+
+### Step 1: Orient
+Review the `trigger_label` as context only. Do not assume it is correct.
+
+### Step 2: Seed Transaction Review
+- Query the seed transaction using `seed_transaction_id`
+- Extract: involved parties, amounts, payment channels, instruments, and other relevant attributes
+
+### Step 3: Scope and Collect
+Pull related activity for involved entities within the investigation window (`window_start` to `seed_timestamp`, inclusive).
+
+### Step 4: Assess Benign Explanations (Default Hypothesis)
+Attempt to explain observed activity as legitimate first:
+- State which evidence supports the benign hypothesis
+- Identify what additional data would strengthen this explanation
+- Only proceed to Step 5 if benign explanations are insufficient
+
+### Step 5: Test Laundering Hypotheses (If Needed)
+If benign explanations fail to account for the evidence:
+- Test whether the evidence supports known laundering typologies
+- Cite concrete indicators that rule out benign explanations
+
+## Typologies / Heuristics
+When assessing patterns, consider these typologies:
+- FAN-IN (aggregation): Many sources aggregating to one destination
+- FAN-OUT (dispersion): One source dispersing to many destinations
+- GATHER-SCATTER / SCATTER-GATHER: Aggregation followed by dispersion (or reverse) over short time windows
+- STACK / LAYERING: Multiple hops meant to obscure origin
+- CYCLE: Circular fund movement
+- BIPARTITE: Structured flows between two distinct groups
+- RANDOM: Complex pattern with no discernible structure
+
+## Output Format
+Return a single JSON object matching the configured output schema exactly. Populate every field.
+Use `pattern_type = "NONE"` when no laundering pattern is supported by evidence in the investigation window.
+
+## Handling Uncertainty
+If you lack sufficient information to make a determination, explicitly state what data is missing. \
+Do not fabricate transaction details or make unsupported inferences. When uncertain between benign and suspicious, \
+default to "NONE" and document why evidence is insufficient
+"""
+
+
+def create_aml_investigation_agent(
+    name: str = "AmlInvestigationAnalyst",
+    *,
+    description: str | None = None,
+    instructions: str | None = None,
+    temperature: float | None = None,
+    top_p: float | None = None,
+    top_k: float | None = None,
+    max_output_tokens: int | None = None,
+    presence_penalty: float | None = None,
+    frequency_penalty: float | None = None,
+    seed: int | None = None,
+    before_agent_callback: BeforeAgentCallback | None = None,
+    after_agent_callback: AfterAgentCallback | None = None,
+    before_model_callback: BeforeModelCallback | None = None,
+    after_model_callback: AfterModelCallback | None = None,
+    enable_tracing: bool = True,
+) -> LlmAgent:
+    """Create a configured AML investigation agent.
+
+    This factory builds a Google ADK ``LlmAgent`` with domain-specific instructions,
+    read-only SQL tools, and a strict structured output schema.
+
+    Parameters
+    ----------
+    name : str, default="AmlInvestigationAnalyst"
+        Name assigned to the agent. This name appears in traces and logs and can
+        help distinguish multiple agents in a shared environment.
+    description : str | None, optional
+        Optional short description of the agent's purpose. If not provided, a
+        default AML investigation description is used.
+    instructions : str | None, optional
+        Optional system prompt for the agent. If omitted, the module-level
+        ``ANALYST_PROMPT`` is used.
+    temperature : float | None, optional
+        Sampling temperature for model generation. ``None`` uses provider/model
+        defaults.
+    top_p : float | None, optional
+        Nucleus sampling parameter. ``None`` uses provider/model defaults.
+    top_k : float | None, optional
+        Top-k sampling parameter. ``None`` uses provider/model defaults.
+    max_output_tokens : int | None, optional
+        Maximum number of tokens the model can generate in a single response.
+        ``None`` uses provider/model defaults.
+    presence_penalty : float | None, optional
+        Penalty to encourage introducing new tokens. ``None`` uses
+        provider/model defaults.
+    frequency_penalty : float | None, optional
+        Penalty to discourage repeated tokens. ``None`` uses provider/model
+        defaults.
+    seed : int | None, optional
+        Optional random seed for more repeatable generations where supported by
+        the model/provider.
+    before_agent_callback : BeforeAgentCallback | None, optional
+        Callback executed before each agent run.
+    after_agent_callback : AfterAgentCallback | None, optional
+        Callback executed after each agent run.
+    before_model_callback : BeforeModelCallback | None, optional
+        Callback executed before each model call.
+    after_model_callback : AfterModelCallback | None, optional
+        Callback executed after each model call.
+    enable_tracing : bool, optional, default=True
+        Whether to initialize Langfuse tracing for this agent. If ``True``, Langfuse
+        tracing is initialized with the agent's name as the service name.
+
+    Returns
+    -------
+    LlmAgent
+        Configured AML investigation agent with:
+
+        - Planner model from global configuration.
+        - Read-only SQL tools for schema and query execution.
+        - ``AnalystOutput`` as the enforced response schema.
+        - Reasoning/thought collection enabled through thinking config.
+
+    Examples
+    --------
+    >>> # Build the agent with defaults:
+    >>> agent = create_aml_investigation_agent()
+    >>> isinstance(agent, LlmAgent)
+    True
+    >>> # Build the agent with a custom name and deterministic settings:
+    >>> agent = create_aml_investigation_agent(
+    ...     name="aml_eval_agent",
+    ...     temperature=0.0,
+    ...     seed=42,
+    ... )
+    >>> agent.name
+    'aml_eval_agent'
+    """
+    # Get the client manager singleton instance
+    client_manager = AsyncClientManager.get_instance()
+    db = client_manager.aml_db(agent_name=name)
+
+    # Initialize tracing if enabled and a name is provided
+    if enable_tracing:
+        init_tracing(service_name=name)
+
+    return LlmAgent(
+        name=name,
+        description=description or _DEFAULT_AGENT_DESCRIPTION,
+        before_agent_callback=before_agent_callback,
+        after_agent_callback=after_agent_callback,
+        model=client_manager.configs.default_planner_model,
+        instruction=instructions or ANALYST_PROMPT,
+        tools=[FunctionTool(db.get_schema_info), FunctionTool(db.execute)],
+        generate_content_config=GenerateContentConfig(
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            max_output_tokens=max_output_tokens,
+            presence_penalty=presence_penalty,
+            frequency_penalty=frequency_penalty,
+            seed=seed,
+            thinking_config=ThinkingConfig(include_thoughts=True),
+        ),
+        output_schema=AnalystOutput,
+        before_model_callback=before_model_callback,
+        after_model_callback=after_model_callback,
+    )