In `@src/backend/base/langflow/components/agentics/agentics.py` around lines 31 -
36, Fix the metadata on the Agentics component class: correct the typo in the
description attribute from "amongs" to "among" and update the documentation
string to include the protocol prefix (e.g.,
"https://github.com/IBM/agentics/"); verify the display_name and icon attributes
(Agentics, "List") remain unchanged. Ensure the changes are made on the Agentics
class attributes: display_name, description, documentation, icon.

In `@src/backend/base/langflow/components/agentics/agentics.py` around lines 31 -
36, The Agentics component class (Agentics) lives in a non-standard subdirectory
(agentics/) and the package __init__.py is empty; either move the Agentics class
file into an existing category (preferably processing/ or agents/) and remove
the agentics/ folder, or if you intend to keep agentics/ as a new category,
populate agentics/__init__.py to export and forward the component (import
Agentics and any helpers and expose them via __all__) and add any package-level
registration logic required by the component loader; update imports throughout
the codebase to reference the new location or package export accordingly.

In `@src/backend/base/langflow/components/agentics/agentics.py` around lines 146 -
178, Remove the three dead helper methods _format_row_as_toml, _create_base_row,
and _add_metadata from the Agentics class: these methods are never invoked (not
used by transduce) and reference undefined attributes (self.output_column_name,
self.enable_metadata), so delete their definitions to avoid dead code and
invalid attribute references; ensure no other part of the class or tests call
these method names before removing.

In `@src/backend/base/langflow/components/agentics/agentics.py` around lines 184 -
220, The model-instantiation block in Agentics must validate and exhaustively
handle providers and return typed errors: first check that self.model is a
non-empty list (avoid IndexError on self.model[0]) and raise a clear ValueError
if empty; use get_model_classes() to resolve metadata.get("model_class") as you
already do, then add explicit branches for the other supported providers
(OpenAI, Anthropic, Ollama) and instantiate the correct LLM (or equivalent
client) with the proper parameters (use get_api_key_for_provider(self.user_id,
provider, self.api_key) for API keys and self.get_project_name() where needed);
never return a string on failure—raise a descriptive exception (fix the typo
“paramters”) for unsupported/invalid provider or missing API key so downstream
consumers get a consistent error type.

In `@src/backend/base/langflow/components/agentics/agentics.py` around lines 180 -
248, In transduce(), llm can remain None when self.model is not a list; before
creating target = AG(..., llm=llm) detect and handle already-instantiated models
by checking self.model's type/interface (e.g., isinstance(self.model, LLM) or a
known Agentic wrapper) and set llm = self.model, or if self.model is a
dict/object that needs instantiation, construct the LLM similarly to the list
branch; if no valid model is available raise a clear ValueError. Update the
logic around the existing llm variable in transduce (refer to the llm local, the
isinstance(self.model, list) branch, and the AG(...) instantiation) so llm is
always a valid model or a clear error is thrown before calling AG.

In `@src/backend/base/langflow/components/agentics/agentics.py` around lines 252 -
253, The method currently constructs df = output.to_dataframe() and returns
df.to_dict(orient="records") while its signature declares -> DataFrame; fix by
either returning the DataFrame object (return df) to match the DataFrame return
type, or change the function's return type annotation to list[dict] (or
List[Dict[str, Any]]) to reflect df.to_dict(orient="records"); update the
signature accordingly and adjust any downstream callers/types if you choose the
latter.