What Is Argument Correctness?

What Is Argument Correctness?

Argument correctness is an LLM-evaluation metric for checking whether a model or agent passed the right values into a function, API, or tool call. It appears in eval pipelines and production tool-call traces after the model selects a tool and emits arguments. A call can use the right tool and valid JSON while still being wrong because account_id, date range, locale, permission scope, or search query does not match the user request. FutureAGI evaluates this with ParameterValidation.

Why Argument Correctness Matters in Production LLM and Agent Systems

Argument failures are quiet because the system often keeps running. The API returns 200, the trace looks complete, and the final answer sounds plausible. The damage appears later: a support agent refunds the wrong order, a sales agent searches the wrong account, a finance assistant pulls the wrong quarter, or an MCP-connected workflow sends an update to a stale record. Tool-selection accuracy may pass because the selected tool is correct. Schema compliance may pass because the object has the required fields. Argument correctness is the check that asks whether the values are the right values.

Developers feel this as flaky agent behavior: the same prompt sometimes calls the right tool with a subtly wrong ID. SREs see retries, downstream validation errors, elevated p99 latency from repair loops, and eval-fail-rate-by-tool rising after a prompt or model change. Product teams see user trust fall because a single incorrect parameter can change the business action while leaving the response readable. Compliance teams feel it when audit logs prove an action happened but not why the argument was selected.

This matters more in 2026 multi-step pipelines than in single-turn chat. Agents chain retrieval, planning, tool calls, and final summaries. One wrong argument can poison later spans, and the final answer may hide the original error unless traces preserve the tool-call boundary.

How FutureAGI Handles Argument Correctness

FutureAGI’s approach is to score argument correctness at the tool-call boundary, not only at the final answer. The specific surface for this entry is eval:ParameterValidation, implemented by the ParameterValidation evaluator class. In a FutureAGI eval workflow, each dataset row stores the user request, retrieved context or reference state, the selected tool, the emitted argument object, and the expected parameter constraints. ParameterValidation validates function call parameters against that schema, while FunctionCallAccuracy checks the broader function-calling behavior and JSONValidation catches malformed structured output.

A real example: an enterprise support agent receives “pause renewals for Acme EMEA until July 31.” The agent should call update_subscription with customer_region="EMEA", action="pause_renewal", and the correct expires_at value. A generic JSON Schema check can confirm that expires_at is a string. It cannot prove the date came from the user request, that the customer region matches the account, or that the action is not a cancellation. Argument correctness closes that gap.

In production, an engineer instruments the LangChain agent with traceAI-langchain, records the tool-call span, and attaches the ParameterValidation result to the trace. If argument-fail-rate crosses a release threshold for update_subscription, the engineer reviews failed traces, tightens the prompt contract, adds regression rows to the golden dataset, or routes high-risk calls to human approval. Unlike final-answer scoring, this catches the error before the agent writes a misleading success message.

How to Measure or Detect Argument Correctness

Measure argument correctness where the tool call is emitted, then roll it up by tool, route, prompt version, and model version:

• fi.evals.ParameterValidation — validates function call parameters against a schema and marks argument objects that violate the expected contract.
• FunctionCallAccuracy — checks whether the function-calling step, including selected function and arguments, matches the expected behavior.
• Trace signal — compare tool-call spans from traceAI-langchain against dataset references or production policy constraints.
• Dashboard signal — track argument-fail-rate-by-tool, repair-loop count, downstream 4xx rate, and p99 latency for calls that require retries.
• User-feedback proxy — monitor manual-review overturns, “wrong account” tickets, escalations, and undo actions after agent tool use.

Minimal Python:

from fi.evals import ParameterValidation

metric = ParameterValidation(schema={"required": ["account_id", "expires_at"]})
result = metric.evaluate(
    arguments={"account_id": "acct_123", "expires_at": "2026-07-31"}
)
print(result.score)

Use deterministic checks when the expected argument is canonical. For open-ended search queries or free-text notes, pair argument correctness with a rubric or TaskCompletion so valid paraphrases are not rejected.

Common Mistakes

Engineers usually miss argument correctness when they stop at structure instead of intent:

• Treating valid JSON as a correct tool call. JSON parsing says the object is readable, not that the values match the task.
• Scoring only the final response. A polished answer can hide a wrong intermediate argument that already changed state.
• Ignoring copied IDs. Agents may reuse an old account_id or document ID from memory, cache, or prior turns.
• Using one global threshold. High-risk tools need stricter argument checks than read-only search or summarization tools.
• Not slicing by tool. A 97% overall score can hide a failing parameter pattern for one write-action API.