diff --git a/aieng-eval-agents/aieng/agent_evals/report_generation/evaluation.py b/aieng-eval-agents/aieng/agent_evals/report_generation/evaluation.py
index 83c8324..c4101d3 100644
--- a/aieng-eval-agents/aieng/agent_evals/report_generation/evaluation.py
+++ b/aieng-eval-agents/aieng/agent_evals/report_generation/evaluation.py
@@ -27,7 +27,7 @@
     TRAJECTORY_EVALUATOR_TEMPLATE,
 )
 from langfuse._client.datasets import DatasetItemClient
-from langfuse.experiment import Evaluation
+from langfuse.experiment import Evaluation, LocalExperimentItem
 from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
 from openai.types.responses.response_output_message import ResponseOutputMessage
 from openai.types.responses.response_output_refusal import ResponseOutputRefusal
@@ -136,12 +136,12 @@ def __init__(
         self.reports_output_path = reports_output_path
         self.langfuse_project_name = langfuse_project_name
 
-    async def run(self, *, item: DatasetItemClient, **kwargs) -> EvaluationOutput:
+    async def run(self, *, item: LocalExperimentItem | DatasetItemClient, **kwargs: dict[str, Any]) -> EvaluationOutput:
         """Run the report generation agent against an item from a Langfuse dataset.
 
         Parameters
         ----------
-        item : DatasetItemClient
+        item : LocalExperimentItem | DatasetItemClient
             The item from the Langfuse dataset to evaluate against.
 
         Returns
@@ -157,7 +157,9 @@ async def run(self, *, item: DatasetItemClient, **kwargs) -> EvaluationOutput:
             reports_output_path=self.reports_output_path,
             langfuse_project_name=self.langfuse_project_name,
         )
-        result = await run_agent_with_retry(report_generation_agent, item.input)
+        # Handle both TypedDict and class access patterns
+        item_input = item["input"] if isinstance(item, dict) else item.input
+        result = await run_agent_with_retry(report_generation_agent, item_input)
 
         # Extract the report data and trajectory from the agent's response
         actions = []
diff --git a/aieng-eval-agents/aieng/agent_evals/tools/__init__.py b/aieng-eval-agents/aieng/agent_evals/tools/__init__.py
index cb8c308..db9e752 100644
--- a/aieng-eval-agents/aieng/agent_evals/tools/__init__.py
+++ b/aieng-eval-agents/aieng/agent_evals/tools/__init__.py
@@ -5,7 +5,13 @@
 - SQL Database access (sql_database.py)
 """
 
-from .search import GroundedResponse, GroundingChunk, create_google_search_tool, format_response_with_citations
+from .search import (
+    GroundedResponse,
+    GroundingChunk,
+    create_google_search_tool,
+    format_response_with_citations,
+    google_search,
+)
 from .sql_database import ReadOnlySqlDatabase, ReadOnlySqlPolicy
 
 
@@ -13,6 +19,7 @@
     # Search tools
     "create_google_search_tool",
     "format_response_with_citations",
+    "google_search",
     "GroundedResponse",
     "GroundingChunk",
     # SQL Database tools
diff --git a/aieng-eval-agents/aieng/agent_evals/tools/_redirect.py b/aieng-eval-agents/aieng/agent_evals/tools/_redirect.py
new file mode 100644
index 0000000..76a9594
--- /dev/null
+++ b/aieng-eval-agents/aieng/agent_evals/tools/_redirect.py
@@ -0,0 +1,183 @@
+"""URL redirect resolution utilities.
+
+Provides utilities for resolving redirect URLs (especially Vertex AI grounding
+redirects) to their final destinations. Used by search and web fetch tools to
+display actual URLs.
+"""
+
+import asyncio
+import logging
+
+import httpx
+from tenacity import AsyncRetrying, retry_if_exception_type, stop_after_attempt, wait_exponential
+
+
+logger = logging.getLogger(__name__)
+
+REDIRECT_URL_PATTERNS = (
+    "vertexaisearch.cloud.google.com/grounding-api-redirect",
+    "vertexaisearch.cloud.google.com/redirect",
+)
+
+_REDIRECT_CONNECT_TIMEOUT = 10.0
+_REDIRECT_READ_TIMEOUT = 15.0
+_USER_AGENT = (
+    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
+)
+_redirect_cache: dict[str, str] = {}
+
+
+def _is_redirect_url(url: str) -> bool:
+    """Check if URL is a known redirect pattern."""
+    return any(pattern in url for pattern in REDIRECT_URL_PATTERNS)
+
+
+def _get_redirect_timeout() -> httpx.Timeout:
+    """Get timeout configuration for redirect resolution."""
+    return httpx.Timeout(
+        connect=_REDIRECT_CONNECT_TIMEOUT,
+        read=_REDIRECT_READ_TIMEOUT,
+        write=10.0,
+        pool=10.0,
+    )
+
+
+async def _resolve_with_head_async(client: httpx.AsyncClient, url: str) -> str | None:
+    """Try to resolve redirect using async HEAD request."""
+    try:
+        response = await client.head(url, headers={"User-Agent": _USER_AGENT})
+        return str(response.url)
+    except httpx.HTTPStatusError as e:
+        # Some servers return 405 Method Not Allowed for HEAD
+        if e.response.status_code in (405, 501):
+            return None  # Signal to try GET
+        raise
+    except Exception:
+        return None
+
+
+async def _resolve_with_get_async(client: httpx.AsyncClient, url: str) -> str:
+    """Resolve redirect using async GET request (fallback when HEAD fails)."""
+    # Use stream to avoid downloading the body
+    async with client.stream("GET", url, headers={"User-Agent": _USER_AGENT}) as response:
+        return str(response.url)
+
+
+async def _resolve_single_url_async(
+    client: httpx.AsyncClient,
+    url: str,
+    max_retries: int = 3,
+    base_delay: float = 1.0,
+) -> str:
+    """Resolve a single URL with retries and exponential backoff.
+
+    Uses tenacity for automatic retry handling with exponential backoff.
+
+    Parameters
+    ----------
+    client : httpx.AsyncClient
+        The HTTP client to use.
+    url : str
+        The URL to resolve.
+    max_retries : int
+        Maximum number of retry attempts.
+    base_delay : float
+        Base delay between retries (doubles each retry).
+
+    Returns
+    -------
+    str
+        The resolved URL, or original URL if not a redirect or on failure.
+    """
+    # Skip resolution for non-redirect URLs
+    if not _is_redirect_url(url):
+        return url
+
+    # Check cache first
+    if url in _redirect_cache:
+        return _redirect_cache[url]
+
+    try:
+        async for attempt in AsyncRetrying(
+            stop=stop_after_attempt(max_retries),
+            wait=wait_exponential(multiplier=base_delay, min=base_delay, max=60.0),
+            retry=retry_if_exception_type((httpx.TimeoutException, httpx.ConnectError, httpx.ReadError)),
+        ):
+            with attempt:
+                # Try HEAD first (faster, no body download)
+                final_url = await _resolve_with_head_async(client, url)
+
+                # Fall back to GET if HEAD failed
+                if final_url is None:
+                    logger.debug(f"HEAD failed for {url[:60]}..., trying GET")
+                    final_url = await _resolve_with_get_async(client, url)
+
+                if final_url != url:
+                    logger.debug(f"Resolved redirect: {url[:60]}... -> {final_url[:60]}...")
+
+                _redirect_cache[url] = final_url
+
+        # If we reach here, the retry loop succeeded
+        return _redirect_cache[url]
+
+    except Exception as e:
+        # All retries exhausted or non-retryable error
+        logger.warning(f"Failed to resolve redirect URL {url[:60]}...: {type(e).__name__}: {e}")
+        _redirect_cache[url] = url  # Cache failures to avoid repeated attempts
+        return url
+
+
+async def resolve_redirect_url_async(url: str) -> str:
+    """Async version of resolve_redirect_url with caching and retries.
+
+    Parameters
+    ----------
+    url : str
+        The URL to resolve (may be a redirect URL).
+
+    Returns
+    -------
+    str
+        The final destination URL after following redirects.
+    """
+    # Skip resolution for non-redirect URLs (fast path)
+    if not _is_redirect_url(url):
+        return url
+
+    # Check cache first (fast path)
+    if url in _redirect_cache:
+        return _redirect_cache[url]
+
+    async with httpx.AsyncClient(
+        timeout=_get_redirect_timeout(),
+        follow_redirects=True,
+    ) as client:
+        return await _resolve_single_url_async(client, url)
+
+
+async def resolve_redirect_urls_async(urls: list[str]) -> list[str]:
+    """Resolve multiple redirect URLs in parallel.
+
+    Resolves URLs concurrently with proper error handling per URL.
+
+    Parameters
+    ----------
+    urls : list[str]
+        List of URLs to resolve.
+
+    Returns
+    -------
+    list[str]
+        List of resolved URLs in the same order.
+    """
+    if not urls:
+        return []
+
+    async with httpx.AsyncClient(
+        timeout=_get_redirect_timeout(),
+        follow_redirects=True,
+        limits=httpx.Limits(max_connections=20, max_keepalive_connections=10),
+    ) as client:
+        # Resolve all URLs in parallel
+        tasks = [_resolve_single_url_async(client, url) for url in urls]
+        return list(await asyncio.gather(*tasks))
diff --git a/aieng-eval-agents/aieng/agent_evals/tools/search.py b/aieng-eval-agents/aieng/agent_evals/tools/search.py
index 90493de..33d18ae 100644
--- a/aieng-eval-agents/aieng/agent_evals/tools/search.py
+++ b/aieng-eval-agents/aieng/agent_evals/tools/search.py
@@ -1,18 +1,18 @@
 """Google Search tool for knowledge-grounded QA using ADK.
 
-This module provides the GoogleSearchTool configuration for use with
-Google ADK agents, enabling explicit and traceable web search capabilities.
+This module provides a search tool that returns actual URLs the agent can fetch,
+enabling a proper research workflow: search → fetch → verify → answer.
 """
 
 import logging
-from typing import TYPE_CHECKING
+from typing import Any
 
-from google.adk.tools.google_search_tool import GoogleSearchTool
+from aieng.agent_evals.configs import Configs
+from google.adk.tools.function_tool import FunctionTool
+from google.genai import Client, types
 from pydantic import BaseModel, Field
 
-
-if TYPE_CHECKING:
-    pass
+from ._redirect import resolve_redirect_urls_async
 
 
 logger = logging.getLogger(__name__)
@@ -56,28 +56,6 @@ def format_with_citations(self) -> str:
         return "\n".join(output_parts)
 
 
-def create_google_search_tool() -> GoogleSearchTool:
-    """Create a GoogleSearchTool configured for use with other tools.
-
-    This creates a GoogleSearchTool with bypass_multi_tools_limit=True,
-    which allows it to be used alongside other custom tools in an ADK agent.
-    The tool calls are explicit and visible in the agent's reasoning trace.
-
-    Returns
-    -------
-    GoogleSearchTool
-        A configured GoogleSearchTool instance.
-
-    Examples
-    --------
-    >>> from aieng.agent_evals.tools import create_google_search_tool
-    >>> search_tool = create_google_search_tool()
-    >>> # Use with an ADK agent
-    >>> agent = Agent(tools=[search_tool])
-    """
-    return GoogleSearchTool(bypass_multi_tools_limit=True)
-
-
 def format_response_with_citations(response: GroundedResponse) -> str:
     """Format a grounded response with inline citations.
 
@@ -96,3 +74,197 @@ def format_response_with_citations(response: GroundedResponse) -> str:
     This is a convenience wrapper around ``response.format_with_citations()``.
     """
     return response.format_with_citations()
+
+
+async def _google_search_async(query: str, model: str) -> dict[str, Any]:
+    """Execute a Google search and return results with actual URLs.
+
+    This function calls Gemini with Google Search grounding enabled,
+    extracts the grounding URLs, resolves any redirects, and returns
+    a structured response the agent can use for further fetching.
+
+    Parameters
+    ----------
+    query : str
+        The search query.
+    model : str
+        The Gemini model to use for search.
+
+    Returns
+    -------
+    dict
+        Search results with the following keys:
+
+        - **status** (str): "success" or "error"
+        - **summary** (str): Brief text summary of search results
+        - **sources** (list[dict]): List of source dicts, each containing:
+            - **title** (str): Title of the webpage
+            - **url** (str): Actual URL that can be fetched
+        - **source_count** (int): Number of sources found (success case only)
+        - **error** (str): Error message (error case only)
+    """
+    client = Client()
+
+    try:
+        response = client.models.generate_content(
+            model=model,
+            contents=query,
+            config=types.GenerateContentConfig(
+                tools=[types.Tool(google_search=types.GoogleSearch())],
+                temperature=0.0,
+            ),
+        )
+
+        # Extract text summary
+        summary = ""
+        if response.candidates and response.candidates[0].content and response.candidates[0].content.parts:
+            for part in response.candidates[0].content.parts:
+                if hasattr(part, "text") and part.text:
+                    summary += part.text
+
+        # Extract grounding URLs
+        sources = []
+        gm = getattr(response.candidates[0], "grounding_metadata", None) if response.candidates else None
+
+        if gm and hasattr(gm, "grounding_chunks") and gm.grounding_chunks:
+            redirect_urls = []
+            titles = []
+            for chunk in gm.grounding_chunks:
+                if hasattr(chunk, "web") and chunk.web:
+                    redirect_urls.append(getattr(chunk.web, "uri", "") or "")
+                    titles.append(getattr(chunk.web, "title", "") or "")
+
+            # Resolve redirect URLs to actual URLs
+            if redirect_urls:
+                resolved_urls = await resolve_redirect_urls_async(redirect_urls)
+                for title, url in zip(titles, resolved_urls):
+                    if url and not url.startswith("https://vertexaisearch"):
+                        sources.append({"title": title, "url": url})
+
+        return {
+            "status": "success",
+            "summary": summary,
+            "sources": sources,
+            "source_count": len(sources),
+        }
+
+    except Exception as e:
+        logger.error(f"Search failed: {e}")
+        return {
+            "status": "error",
+            "error": str(e),
+            "summary": "",
+            "sources": [],
+        }
+
+
+async def google_search(query: str, model: str | None = None) -> dict[str, Any]:
+    """Search Google and return results with actual URLs for fetching.
+
+    Use this tool to find information on the web. The results include:
+    - A summary of what was found
+    - A list of source URLs that you can fetch with web_fetch to verify information
+
+    IMPORTANT: The summary is from search snippets which may be incomplete or outdated.
+    Always use web_fetch on the source URLs to verify information before answering.
+
+    Parameters
+    ----------
+    query : str
+        The search query. Be specific and include key terms.
+    model : str, optional
+        The Gemini model to use for search. If not provided, uses
+        default_worker_model from Configs.
+
+    Returns
+    -------
+    dict
+        Search results with the following keys:
+
+        - **status** (str): "success" or "error"
+        - **summary** (str): Brief text summary of search results
+        - **sources** (list[dict]): List of source dicts, each containing:
+            - **title** (str): Title of the webpage
+            - **url** (str): Actual URL that can be fetched
+        - **source_count** (int): Number of sources found (success case only)
+        - **error** (str): Error message (error case only)
+
+    Examples
+    --------
+    >>> result = await google_search("highest single day snowfall Toronto")
+    >>> # Check the sources
+    >>> for source in result["sources"]:
+    ...     print(f"{source['title']}: {source['url']}")
+    >>> # Then fetch to verify
+    >>> page = await web_fetch(result["sources"][0]["url"])
+    """
+    if model is None:
+        config = Configs()  # type: ignore[call-arg]
+        model = config.default_worker_model
+
+    return await _google_search_async(query, model=model)
+
+
+def create_google_search_tool(config: Configs | None = None) -> FunctionTool:
+    """Create a search tool that returns actual URLs for fetching.
+
+    This tool calls Google Search, extracts grounding URLs, resolves redirects,
+    and returns actual URLs the agent can use with web_fetch for verification.
+
+    Parameters
+    ----------
+    config : Configs, optional
+        Configuration settings. If not provided, creates default config.
+        Uses config.default_worker_model for the search model.
+
+    Returns
+    -------
+    FunctionTool
+        A search tool that returns fetchable URLs.
+
+    Examples
+    --------
+    >>> from aieng.agent_evals.tools import create_google_search_tool
+    >>> from aieng.agent_evals.configs import Configs
+    >>> config = Configs()
+    >>> search_tool = create_google_search_tool(config=config)
+    >>> # Use with an ADK agent
+    >>> agent = Agent(tools=[search_tool])
+    """
+    if config is None:
+        config = Configs()  # type: ignore[call-arg]
+
+    model = config.default_worker_model
+
+    async def google_search(query: str) -> dict[str, Any]:
+        """Search Google and return results with actual URLs for fetching.
+
+        Use this tool to find information on the web. The results include:
+        - A summary of what was found
+        - A list of source URLs that you can fetch with web_fetch to verify
+
+        IMPORTANT: The summary is from search snippets which may be incomplete
+        or outdated. Always use web_fetch on the source URLs to verify information
+        before answering.
+
+        Parameters
+        ----------
+        query : str
+            The search query. Be specific and include key terms.
+
+        Returns
+        -------
+        dict
+            Search results with the following keys:
+
+            - **status** (str): "success" or "error"
+            - **summary** (str): Brief text summary of search results
+            - **sources** (list[dict]): List of source dicts, each containing:
+                - **title** (str): Title of the webpage
+                - **url** (str): Actual URL that can be fetched
+            - **source_count** (int): Number of sources found (success case only)
+            - **error** (str): Error message (error case only)
+        """
+        return await _google_search_async(query, model=model)
+
+    return FunctionTool(func=google_search)
diff --git a/aieng-eval-agents/pyproject.toml b/aieng-eval-agents/pyproject.toml
index 2476b07..d8a7d71 100644
--- a/aieng-eval-agents/pyproject.toml
+++ b/aieng-eval-agents/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "aieng-eval-agents"
-version = "0.1.0"
+version = "0.2.0"
 description = "Agentic AI Evaluation reference implementation from Vector Institute AI Engineering"
 authors = [{name = "Vector AI Engineering", email = "ai_engineering@vectorinstitute.ai"}]
 requires-python = ">=3.12,<4.0"
@@ -24,8 +24,16 @@ dependencies = [
     "sqlalchemy>=2.0.46",
     "sqlglot>=28.6.0",
     "weaviate-client>=4.18.3",
+    "pypdf",
+    "httpx>=0.27.0",
+    "tenacity>=8.2.0",
+    "python-dotenv>=1.0.0",
+    "html-to-markdown>=2.24.0",
 ]
 
+[project.scripts]
+knowledge-agent = "aieng.agent_evals.knowledge_agent.cli:main"
+
 [dependency-groups]
 dev = [
     "pytest>=8.3.5",
diff --git a/aieng-eval-agents/tests/aieng/agent_evals/tools/test_search.py b/aieng-eval-agents/tests/aieng/agent_evals/tools/test_search.py
index 72f031a..6ca13ee 100644
--- a/aieng-eval-agents/tests/aieng/agent_evals/tools/test_search.py
+++ b/aieng-eval-agents/tests/aieng/agent_evals/tools/test_search.py
@@ -1,6 +1,6 @@
 """Tests for Google Search tool."""
 
-from unittest.mock import MagicMock, patch
+from unittest.mock import MagicMock
 
 import pytest
 from aieng.agent_evals.tools import (
@@ -8,7 +8,9 @@
     GroundingChunk,
     create_google_search_tool,
     format_response_with_citations,
+    google_search,
 )
+from google.adk.tools.function_tool import FunctionTool
 
 
 class TestGroundingChunk:
@@ -81,16 +83,17 @@ def test_format_with_citations_no_sources(self):
 class TestCreateGoogleSearchTool:
     """Tests for the create_google_search_tool function."""
 
-    @patch("aieng.agent_evals.tools.search.GoogleSearchTool")
-    def test_creates_tool_with_bypass_flag(self, mock_tool_class):
-        """Test that the tool is created with bypass_multi_tools_limit=True."""
-        mock_tool = MagicMock()
-        mock_tool_class.return_value = mock_tool
+    def test_creates_function_tool(self):
+        """Test that the tool is created as a FunctionTool wrapping google_search."""
+        # Create a mock config with the required attribute
+        mock_config = MagicMock()
+        mock_config.default_worker_model = "gemini-2.5-flash"
 
-        result = create_google_search_tool()
+        result = create_google_search_tool(config=mock_config)
 
-        mock_tool_class.assert_called_once_with(bypass_multi_tools_limit=True)
-        assert result is mock_tool
+        assert isinstance(result, FunctionTool)
+        # The function tool should wrap the google_search function
+        assert result.func.__name__ == "google_search"
 
 
 class TestFormatResponseWithCitations:
@@ -160,7 +163,51 @@ class TestGoogleSearchToolIntegration:
     """
 
     def test_create_google_search_tool_real(self):
-        """Test creating a real GoogleSearchTool instance."""
+        """Test creating a real FunctionTool instance wrapping google_search."""
         tool = create_google_search_tool()
-        # The tool should be a GoogleSearchTool instance with bypass flag
-        assert tool is not None
+        # The tool should be a FunctionTool wrapping google_search
+        assert isinstance(tool, FunctionTool)
+
+    @pytest.mark.asyncio
+    async def test_google_search_returns_urls(self):
+        """Test that google_search returns actual URLs, not redirect URLs."""
+        result = await google_search("capital of France")
+
+        # Should have success status
+        assert result["status"] == "success"
+
+        # Should have a summary
+        assert result["summary"], "Expected non-empty summary"
+
+        # Should have sources with URLs
+        assert result["source_count"] > 0, "Expected at least one source"
+        assert len(result["sources"]) == result["source_count"]
+
+        # Each source should have title and url
+        for source in result["sources"]:
+            assert "title" in source
+            assert "url" in source
+            # URL should be a real URL, not a redirect URL
+            assert source["url"].startswith("http"), f"Expected URL, got: {source['url']}"
+            assert "vertexaisearch" not in source["url"], "URL should not be a redirect URL"
+
+    @pytest.mark.asyncio
+    async def test_google_search_response_structure(self):
+        """Test the complete response structure from google_search."""
+        result = await google_search("Python programming language")
+
+        # Check all expected keys exist
+        assert "status" in result
+        assert "summary" in result
+        assert "sources" in result
+        assert "source_count" in result
+
+        # Sources should be a list
+        assert isinstance(result["sources"], list)
+
+        # If we have sources, verify their structure
+        if result["sources"]:
+            source = result["sources"][0]
+            assert isinstance(source, dict)
+            assert "title" in source
+            assert "url" in source
diff --git a/implementations/aml_investigation/agent.py b/implementations/aml_investigation/agent.py
index 4d48e61..8fa34b4 100644
--- a/implementations/aml_investigation/agent.py
+++ b/implementations/aml_investigation/agent.py
@@ -14,7 +14,7 @@
 import logging
 import os
 import uuid
-from functools import lru_cache
+from functools import lru_cache, partial
 from pathlib import Path
 
 import google.genai.types
@@ -216,7 +216,7 @@ async def _analyze_cases_to_jsonl(
 
     output_path.parent.mkdir(parents=True, exist_ok=True)
     tasks = [
-        asyncio.create_task(rate_limited(lambda r=record: _safe_analyze_case(runner, r), semaphore)) for record in cases
+        asyncio.create_task(rate_limited(partial(_safe_analyze_case, runner, record), semaphore)) for record in cases
     ]
 
     analyzed_by_id: dict[str, CaseRecord] = {}
@@ -280,6 +280,7 @@ async def _main() -> None:
             tp = fp = fn = tn = 0
             for r in scored:
                 gt = r.groundtruth.is_laundering
+                assert r.analysis is not None  # Guaranteed by filter above
                 pred = r.analysis.is_laundering
                 if gt and pred:
                     tp += 1
diff --git a/implementations/aml_investigation/data/cli.py b/implementations/aml_investigation/data/cli.py
index dd9635d..0ae1d27 100644
--- a/implementations/aml_investigation/data/cli.py
+++ b/implementations/aml_investigation/data/cli.py
@@ -104,12 +104,50 @@ def wrapper(*args, **kwargs):
     return wrapper
 
 
-def _validate_dataset_options(illicit_ratio: str, transactions_size: str) -> None:
-    """Validate dataset option values."""
-    if illicit_ratio not in get_args(IllicitRatios):
+def _validate_illicit_ratio(value: str) -> IllicitRatios:
+    """Validate and narrow illicit_ratio to its Literal type.
+
+    Parameters
+    ----------
+    value : str
+        The illicit ratio value to validate.
+
+    Returns
+    -------
+    IllicitRatios
+        The validated and type-narrowed illicit ratio.
+
+    Raises
+    ------
+    ValueError
+        If value is not a valid illicit ratio.
+    """
+    if value not in get_args(IllicitRatios):
         raise ValueError(f"illicit_ratio must be one of {sorted(get_args(IllicitRatios))}")
-    if transactions_size not in get_args(TransactionsSizes):
+    return value  # type: ignore[return-value]
+
+
+def _validate_transactions_size(value: str) -> TransactionsSizes:
+    """Validate and narrow transactions_size to its Literal type.
+
+    Parameters
+    ----------
+    value : str
+        The transactions size value to validate.
+
+    Returns
+    -------
+    TransactionsSizes
+        The validated and type-narrowed transactions size.
+
+    Raises
+    ------
+    ValueError
+        If value is not a valid transactions size.
+    """
+    if value not in get_args(TransactionsSizes):
         raise ValueError(f"transactions_size must be one of {sorted(get_args(TransactionsSizes))}")
+    return value  # type: ignore[return-value]
 
 
 @click.group()
@@ -159,7 +197,10 @@ def create_db(illicit_ratio: str, transactions_size: str, ddl_file_path: Path, d
     FileNotFoundError
         If the DDL file does not exist.
     """
-    _validate_dataset_options(illicit_ratio, transactions_size)
+    # Validate and narrow types
+    ratio = _validate_illicit_ratio(illicit_ratio)
+    size = _validate_transactions_size(transactions_size)
+
     if not ddl_file_path.exists():
         raise FileNotFoundError(f"DDL file not found: {ddl_file_path}")
 
@@ -168,8 +209,8 @@ def create_db(illicit_ratio: str, transactions_size: str, ddl_file_path: Path, d
 
     # Download datasets from Kaggle
     click.echo("Downloading dataset files...")
-    path_to_transc_csv = download_dataset_file(illicit_ratio, transactions_size, "Trans.csv")
-    path_to_accts_csv = download_dataset_file(illicit_ratio, transactions_size, "accounts.csv")
+    path_to_transc_csv = download_dataset_file(ratio, size, "Trans.csv")
+    path_to_accts_csv = download_dataset_file(ratio, size, "accounts.csv")
     click.echo("✅ Download complete.")
 
     with sqlite3.connect(db_path) as conn:
@@ -256,7 +297,10 @@ def create_cases(
     ValueError
         If any numeric argument is negative or option values are invalid.
     """
-    _validate_dataset_options(illicit_ratio, transactions_size)
+    # Validate and narrow types
+    ratio = _validate_illicit_ratio(illicit_ratio)
+    size = _validate_transactions_size(transactions_size)
+
     for name, value in [
         ("num_laundering_cases", num_laundering_cases),
         ("num_normal_cases", num_normal_cases),
@@ -269,8 +313,8 @@ def create_cases(
     if lookback_days == 0:
         logger.warning("lookback_days=0 creates very narrow windows (can be seed timestamp only); consider >= 1.")
 
-    path_to_transc_csv = download_dataset_file(illicit_ratio, transactions_size, "Trans.csv")
-    path_to_patterns_txt = download_dataset_file(illicit_ratio, transactions_size, "Patterns.txt")
+    path_to_transc_csv = download_dataset_file(ratio, size, "Trans.csv")
+    path_to_patterns_txt = download_dataset_file(ratio, size, "Patterns.txt")
     click.echo("✅ Downloaded dataset files.")
 
     transc_df = pd.read_csv(path_to_transc_csv, dtype_backend="pyarrow")
diff --git a/pyproject.toml b/pyproject.toml
index a3a9c89..df429fe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -50,6 +50,7 @@ dev = [
     "pytest-mock>=3.14.0",
     "ruff>=0.14.7",
     "transformers>=4.57.3",
+    "pandas-stubs>=2.3.3.260113",
 ]
 docs = [
     "jinja2>=3.1.6", # Pinning version to address vulnerability GHSA-cpwx-vrp4-4pq7
@@ -158,3 +159,9 @@ filterwarnings = [
     [tool.coverage.run]
     source=["aieng-eval-agents/aieng"]
     omit=["aieng-eval-agents/tests/*", "*__init__.py", "scripts/*"]
+
+[tool.mypy]
+mypy_path = "aieng-eval-agents"
+namespace_packages = true
+explicit_package_bases = true
+exclude = ["tests/"]
diff --git a/uv.lock b/uv.lock
index e4a14c3..6f2e15c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.12, <4.0"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
@@ -56,6 +56,7 @@ dev = [
     { name = "jupyterlab" },
     { name = "mypy" },
     { name = "nbqa" },
+    { name = "pandas-stubs" },
     { name = "pip" },
     { name = "pip-audit" },
     { name = "pre-commit" },
@@ -118,6 +119,7 @@ dev = [
     { name = "jupyterlab", specifier = ">=4.4.8" },
     { name = "mypy", specifier = ">=1.19.0" },
     { name = "nbqa", specifier = ">=1.9.1" },
+    { name = "pandas-stubs", specifier = ">=2.3.3.260113" },
     { name = "pip", specifier = ">=26.0" },
     { name = "pip-audit", specifier = ">=2.9.0" },
     { name = "pre-commit", specifier = ">=4.2.0" },
@@ -146,12 +148,14 @@ web-search = [
 
 [[package]]
 name = "aieng-eval-agents"
-version = "0.1.0"
+version = "0.2.0"
 source = { editable = "aieng-eval-agents" }
 dependencies = [
     { name = "google-adk" },
     { name = "google-genai" },
     { name = "gradio" },
+    { name = "html-to-markdown" },
+    { name = "httpx" },
     { name = "kagglehub" },
     { name = "langfuse" },
     { name = "openai" },
@@ -162,9 +166,12 @@ dependencies = [
     { name = "pandas" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
+    { name = "pypdf" },
+    { name = "python-dotenv" },
     { name = "rich" },
     { name = "sqlalchemy" },
     { name = "sqlglot" },
+    { name = "tenacity" },
     { name = "weaviate-client" },
 ]
 
@@ -180,6 +187,8 @@ requires-dist = [
     { name = "google-adk", specifier = ">=1.23.0" },
     { name = "google-genai", specifier = ">=1.52.0" },
     { name = "gradio", specifier = ">=6.0.2" },
+    { name = "html-to-markdown", specifier = ">=2.24.0" },
+    { name = "httpx", specifier = ">=0.27.0" },
     { name = "kagglehub", specifier = ">=0.4.1" },
     { name = "langfuse", specifier = ">=3.10.3" },
     { name = "openai", specifier = ">=2.8.1" },
@@ -190,9 +199,12 @@ requires-dist = [
     { name = "pandas", specifier = ">=2.3.0" },
     { name = "pydantic", specifier = ">=2.12.0" },
     { name = "pydantic-settings", specifier = ">=2.12.0" },
+    { name = "pypdf" },
+    { name = "python-dotenv", specifier = ">=1.0.0" },
     { name = "rich", specifier = ">=13.9.0" },
     { name = "sqlalchemy", specifier = ">=2.0.46" },
     { name = "sqlglot", specifier = ">=28.6.0" },
+    { name = "tenacity", specifier = ">=8.2.0" },
     { name = "weaviate-client", specifier = ">=4.18.3" },
 ]
 
@@ -224,27 +236,27 @@ dependencies = [
     { name = "virtualenv" },
     { name = "weaviate-client" },
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+sdist = { url = "https://files.pythonhosted.org/packages/72/f8/ac5d1d0a93443732c6d39f64bc052af18f0486a57c9f73db83f155113f90/aieng_platform_onboard-0.5.0.tar.gz", hash = "sha256:061951723b8f99b26d561ec8a9f0d8c8a331e58ac40daf74a82c88d7e841557e", size = 24513 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8e/48/88e44a0496e8e5923ab81f50c9279bc253d29617906bc85cfc631842697a/aieng_platform_onboard-0.5.0-py3-none-any.whl", hash = "sha256:ca9f41780e1dc7021298dc59e0abe8cd7734293fdde8822b9b916cf354d61ff3", size = 29697, upload-time = "2026-01-15T19:02:02.957Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/48/88e44a0496e8e5923ab81f50c9279bc253d29617906bc85cfc631842697a/aieng_platform_onboard-0.5.0-py3-none-any.whl", hash = "sha256:ca9f41780e1dc7021298dc59e0abe8cd7734293fdde8822b9b916cf354d61ff3", size = 29697 },
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 version = "24.1.0"
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@@ -4903,9 +4943,9 @@ dependencies = [
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