6 Frameworks for Evaluating Agent Tool Use

An AI agent can produce a convincing final answer while using the wrong tools, passing incorrect arguments, or performing unnecessary actions along the way.

Consider an agent responsible for scheduling meetings. It may successfully create a calendar event but invite the wrong participants, select an incorrect time zone, or fail to check availability before confirming the booking.

From the user's perspective, the task appears complete. From an engineering perspective, the execution may be incorrect.

This is why evaluating agent tool use requires more than measuring final response quality.

For ML engineers and technical evaluators, six frameworks worth examining are Inspect AI, DeepEval, LangSmith, Braintrust, Promptfoo, and AgentBench.

These tools serve different evaluation requirements. Some support detailed execution traces and trajectory analysis. Others provide automated assertions, controlled environments, or benchmarks for agent capabilities.

This article explains how each framework approaches agent tool-use evaluation, where it fits, and what engineering teams should measure before deploying tool-using agents in production.

Why Agent Tool Use Needs Its Own Evaluation Strategy

Traditional language model evaluations often focus on the quality of generated text.

Tool-using agents introduce additional behaviors.

An agent may need to:

1. Interpret the user's request.
2. Decide whether a tool is necessary.
3. Select the appropriate tool.
4. Construct valid arguments.
5. Execute the tool.
6. Interpret the result.
7. Decide whether another action is needed.
8. Complete the task.
9. Report the outcome accurately.

A failure at any stage can affect the final result.

For example, a customer support agent might select the correct refund API but pass an incorrect transaction identifier.

Alternatively, it might execute a refund before checking whether the customer meets the refund policy.

These are different failure modes.

The first concerns argument correctness.

The second concerns workflow compliance and authorization.

A reliable evaluation process must distinguish between them.

What Should You Measure in Agent Tool Use?

Before selecting an evaluation framework, define the behaviors that matter for your application.

Tool Selection Accuracy

Did the agent choose the appropriate tool for the task?

For example, an agent should use a database query tool to retrieve current inventory rather than generate an answer from memory.

Argument Correctness

Were the tool arguments valid and consistent with the user's request?

A correctly selected tool can still produce an incorrect result when supplied with wrong parameters.

Tool Execution Success

Did the tool call execute successfully?

This includes handling authentication failures, invalid inputs, rate limits, and unavailable services.

Execution Order

Did the agent perform actions in the required sequence?

For example, an agent may need to verify an order before issuing a refund.

Task Completion

Did the agent achieve the intended outcome?

Task completion should be measured independently from the agent's own claim of success.

Unnecessary Tool Calls

Did the agent execute tools that were not needed?

Unnecessary calls can increase latency, cost, and operational risk.

Recovery From Failures

Can the agent recover from tool errors without repeating consequential actions?

Authorization and Safety

Did the agent stay within its permitted scope?

This is particularly important when tools can modify records, transfer information, or trigger external actions.

These dimensions form the basis of a practical evaluation strategy.

1. Inspect AI: Controlled Tool-Use and Agent Evaluations

Official documentation: https://inspect.aisi.org.uk/

GitHub: https://github.com/UKGovernmentBEIS/inspect_ai

Inspect AI is an open-source evaluation framework developed for structured assessment of language models and agentic systems.

It provides components for defining evaluation tasks, supplying tools, executing model interactions, and scoring outcomes.

Its support for sandboxed environments makes it particularly relevant for agents that execute code or interact with external systems.

Key Features

• Custom evaluation tasks.
• Agent and model evaluation.
• Tool integration.
• Multi-turn execution.
• Sandboxed environments.
• Custom scoring functions.
• Evaluation datasets.
• Execution logging.
• Model comparison.

Where Inspect AI Works Well

Consider an AI coding agent that must modify a repository and run tests.

The agent may need to inspect files, edit code, execute commands, and verify the result.

Evaluating only the final message would be insufficient.

The engineering team needs to know whether the agent actually changed the correct files and whether the resulting code passes tests.

Inspect AI can support this type of evaluation through controlled execution environments and task-specific scorers.

Example: Defining a Simple Evaluation Task

Install Inspect AI:

pip install inspect-ai

A simplified evaluation task can be defined as follows:

from inspect_ai import Task, task

from inspect_ai.dataset import Sample

from inspect_ai.scorer import exact

from inspect_ai.solver import generate

@task
def simple_tool_reasoning():
    return Task(
        dataset=[
            Sample(
                input=(
                    "Which tool should be used "
                    "to retrieve current inventory?"
                ),
                target="inventory_lookup"
            )
        ],
        solver=generate(),
        scorer=exact()
    )

This example tests the model's textual tool choice rather than executing a tool.

A real agent evaluation would provide tool definitions, record tool calls, and use a scorer that checks execution outcomes.

Advantages

Inspect AI supports reproducible evaluation tasks and controlled execution.

Its sandboxing capabilities are valuable when evaluating agents that execute code or interact with environments.

Limitations

Inspect AI requires engineers to define appropriate tasks, tools, and scoring logic.

It is not a complete replacement for production tracing or operational monitoring.

Best Suited For

• Coding agents.
• Tool-using research agents.
• Controlled execution benchmarks.
• Multi-step agent evaluation.
• Sandboxed testing.
• Reproducible model comparisons.

Technical verdict: Inspect AI is a strong option when agent evaluation requires controlled environments and explicit task-level scoring.

2. DeepEval: Automated Agent Tool-Use Testing

Official documentation: https://deepeval.com/docs/getting-started

GitHub: https://github.com/confident-ai/deepeval

DeepEval is an open-source evaluation framework for testing LLM applications.

It provides metrics for assessing agent behavior, including tool selection, tool execution, and task completion.

Its Python-oriented testing approach makes it useful for engineering teams that want to integrate agent evaluations into development workflows.

Key Features

• Tool correctness evaluation.
• Agent task completion metrics.
• Tool call recording.
• Custom evaluation criteria.
• LLM-based metrics.
• Test case management.
• Pytest-style workflows.
• CI integration.

Where DeepEval Works Well

Consider an AI agent that processes customer support requests.

The agent may have access to:

• Customer lookup.
• Order history.
• Refund eligibility.
• Refund execution.
• Ticket creation.

A test case might require the agent to retrieve the customer record, check eligibility, and create a support ticket.

DeepEval can help assess whether the agent selected appropriate tools and completed the intended task.

Example: Evaluating Tool Correctness

from deepeval import evaluate

from deepeval.metrics import ToolCorrectnessMetric

from deepeval.test_case import (
    LLMTestCase,
    ToolCall
)

test_case = LLMTestCase(
    input="Find the status of order 12345.",
    actual_output="Order 12345 has shipped.",
    tools_called=[
        ToolCall(
            name="order_lookup",
            input_parameters={
                "order_id": "12345"
            }
        )
    ],
    expected_tools=[
        ToolCall(
            name="order_lookup",
            input_parameters={
                "order_id": "12345"
            }
        )
    ]
)

metric = ToolCorrectnessMetric()

evaluate(
    test_cases=[test_case],
    metrics=[metric]
)

This example checks recorded tool calls against expected tool calls.

It does not independently verify whether the order actually shipped.

That requires a trusted test environment or an additional outcome assertion.

Advantages

DeepEval allows teams to define tool-use tests in Python and run them alongside other automated checks.

This can make regression testing easier to incorporate into CI pipelines.

Limitations

Tool correctness metrics depend on how expected behavior is defined.

Some tasks have multiple valid tool sequences.

A rigid expected sequence may incorrectly penalize an agent that completes the task using another valid approach.

Best Suited For

• Python-based agent applications.
• Tool selection testing.
• Tool argument validation.
• CI regression testing.
• Agent task completion evaluation.

Technical verdict: DeepEval is a practical option for teams that want automated agent tool-use tests integrated into their software development process.

3. LangSmith: Trajectory Evaluation and Execution Tracing

Official documentation: LangSmith Evaluation Documentation

Official website: LangSmith

LangSmith is a platform for tracing, evaluating, and monitoring LLM applications, including agents built with LangChain, LangGraph, and other frameworks.

Its primary advantage for tool-use evaluation is visibility into the agent's execution trajectory.

Instead of examining only the final answer, engineers can inspect which tools were called, their inputs and outputs, and the sequence of decisions.

Licensing distinction: LangSmith is a commercial platform with self-hosting options, not an open-source evaluation framework. It is included because it addresses the same engineering problem as the other tools in this shortlist.

Key Features

• Agent execution tracing.
• Tool-call inspection.
• Trajectory evaluation.
• Offline evaluation datasets.
• Online evaluation.
• Custom evaluators.
• Human feedback.
• Experiment comparison.
• Integration with LangGraph and other agent frameworks.

Where LangSmith Works Well

Consider an agent responsible for resolving customer support requests.

The expected workflow is:

1. Retrieve the customer's record.
2. Identify the relevant order.
3. Check refund eligibility.
4. Request approval when necessary.
5. Execute an authorized refund.
6. Report the outcome.

The agent may complete the refund but skip the approval step.

A final-answer evaluation might incorrectly classify the interaction as successful.

Trajectory evaluation allows engineers to inspect the sequence of tool calls and identify the missing approval.

How to Evaluate Tool Trajectories

LangSmith supports approaches such as comparing an actual trajectory against a reference trajectory or using an evaluator to assess the execution path.

For example, a test case could specify:

{
  "task": "Refund an eligible order",
  "expected_tools": [
    "customer_lookup",
    "order_lookup",
    "refund_eligibility",
    "approval_check",
    "refund_execution"
  ]
}

The evaluator can compare the recorded tool sequence with the expected workflow.

However, exact sequence matching is not appropriate for every task.

An agent may complete some workflows correctly through multiple valid paths.

In those situations, evaluators should check required actions, prohibited actions, and final outcomes rather than demanding one exact sequence.

Advantages

LangSmith is useful for debugging complex agent workflows and evaluating behavior against recorded execution traces.

Its integration with LangGraph makes it relevant for teams already using that ecosystem.

Limitations

LangSmith is not an open-source framework.

Teams should assess data handling, pricing, deployment requirements, and vendor dependencies before adoption.

Tracing also introduces operational considerations because tool inputs and outputs may contain sensitive information.

Best Suited For

• LangGraph-based agents.
• Complex multistep workflows.
• Trajectory evaluation.
• Production agent debugging.
• Tool-call regression testing.
• Human review of agent execution.

Technical verdict: LangSmith is worth evaluating when trace visibility and trajectory analysis are priorities, particularly for teams using LangGraph.

4. Braintrust: Dataset-Driven Evaluation of Agent Workflows

Official website: Braintrust

Documentation: Braintrust Documentation

Braintrust is an evaluation and observability platform for AI applications.

It supports datasets, experiments, custom scoring functions, and tracing.

For agent evaluation, its main value is the ability to compare application behavior against defined test cases while retaining execution context.

Licensing distinction: Braintrust is a commercial platform with open-source SDKs and supporting components. The hosted evaluation platform itself should not be treated as fully open source.

Key Features

• Evaluation datasets.
• Custom scoring functions.
• Experiment tracking.
• Agent execution tracing.
• Human feedback.
• Model comparison.
• Production monitoring.
• Integration with application code.
• Regression evaluation.

Where Braintrust Works Well

Consider a procurement agent that can search supplier records, retrieve contract information, and prepare purchase requests.

The agent should select appropriate tools and follow procurement rules.

A test dataset might include:

• Requests from authorized employees.
• Requests exceeding approval limits.
• Suppliers with expired contracts.
• Missing purchase order information.
• Tool failures.
• Attempts to bypass purchasing policies.

The evaluation process should determine whether the agent handles each scenario correctly.

Example: A Custom Tool-Use Scorer

A custom Python scorer can inspect recorded tool calls.

def required_tool_score(output, expected):
    actual_tools = {
        call["name"]
        for call in output["tool_calls"]
    }

    required_tools = set(
        expected["required_tools"]
    )

    return float(
        required_tools.issubset(actual_tools)
    )

This function returns 1 when all required tools appear in the recorded execution and 0 otherwise.

It is a standalone scoring function, not a complete Braintrust SDK example.

A production evaluator should also check arguments, ordering constraints, prohibited actions, and actual task outcomes.

Advantages

Braintrust provides a dataset-driven approach to evaluating application behavior.

Its experiment and tracing capabilities can help teams compare agent implementations.

Limitations

The evaluation platform is commercial.

Teams should assess deployment requirements, data governance, pricing, and the degree of integration with their existing engineering systems.

Best Suited For

• Dataset-driven agent evaluation.
• Custom tool-use scoring.
• Experiment comparison.
• Production trace analysis.
• Regression testing.
• Teams requiring a shared evaluation platform.

Technical verdict: Braintrust is a useful option for organizations that want centralized experiments, custom scoring, and agent observability, provided its commercial model fits their requirements.

5. Promptfoo: Tool-Call Assertions and Agent Regression Testing

Official documentation: Promptfoo Documentation

GitHub: Promptfoo Repository

Promptfoo is an open-source framework for evaluating LLM applications and agents through configurable test cases and assertions.

It is particularly useful when engineering teams want to validate tool calls as part of automated regression testing.

Promptfoo supports tool-call validation, trajectory assertions, tracing, and security testing. Its documentation describes ways to inspect tool names, arguments, execution order, and errors using OpenTelemetry traces.

Key Features

• Tool-call schema validation.
• Tool selection assertions.
• Tool argument matching.
• Tool execution sequence checks.
• Trajectory evaluation.
• Custom assertions.
• Model and prompt comparison.
• CI integration.
• Agent red teaming.
• OpenTelemetry tracing.

Where Promptfoo Works Well

Consider an agent that retrieves order information and answers customer questions.

The agent should use search_orders with the correct order identifier.

A test should verify that the agent actually called the tool rather than inventing an order status.

It should also verify that the tool received the correct arguments.

Example: Testing Tool Calls

The following YAML illustrates trajectory assertions for an instrumented agent:

description: Order lookup agent evaluation

tracing:
  enabled: true

tests:
  - vars:
      order_id: "12345"

    assert:
      - type: trajectory:tool-used
        value: search_orders

      - type: trajectory:tool-args-match
        value:
          name: search_orders
          args:
            order_id: "12345"

These assertions require the evaluated application to emit compatible tool-call traces.

A complete runnable configuration also needs a provider and the application under test.

Testing Execution Order

For workflows where order matters, Promptfoo supports trajectory sequence assertions.

For example, an agent should verify an order before processing a refund.

assert:
  - type: trajectory:tool-sequence
    value:
      steps:
        - verify_order
        - process_refund

This checks the recorded sequence. It does not independently prove that the refund was authorized or correctly processed.

For those requirements, engineers should add assertions against trusted application state.

Advantages

Promptfoo is useful for automated regression testing because many tool-use requirements can be expressed as deterministic assertions.

Its tracing capabilities also help engineers investigate why a test failed.

Limitations

Trajectory assertions depend on complete and correctly instrumented traces.

A tool call that is not captured cannot be evaluated reliably.

Strict sequence assertions can also be inappropriate for workflows that permit multiple valid execution paths.

Best Suited For

• Tool-call regression testing.
• API argument validation.
• CI pipelines.
• Agent security testing.
• Execution sequence verification.
• Prompt and model comparisons.

Technical verdict: Promptfoo is a strong choice for engineering teams that need repeatable tool-use assertions and automated agent regression testing.

6. AgentBench: Benchmarking Agents in Interactive Environments

Official repository: AgentBench GitHub Repository

Research paper: AgentBench: Evaluating LLMs as Agents

AgentBench is an open-source benchmark designed to evaluate language models as agents operating in interactive environments.

Unlike application-specific testing frameworks, AgentBench focuses on assessing general agent capabilities across predefined environments.

The original research introduced eight environments to evaluate reasoning, decision-making, and interaction capabilities. The paper was published at ICLR 2024.

Key Features

• Interactive evaluation environments.
• Multistep agent tasks.
• Environment-based success measurement.
• Model comparison.
• Agent reasoning assessment.
• Decision-making evaluation.
• Reproducible benchmark tasks.
• Research-oriented evaluation.

Where AgentBench Works Well

Consider a research team comparing several language models for agentic applications.

The team wants to understand how models perform when they must interact with environments rather than simply answer questions.

AgentBench provides predefined tasks that can help reveal differences in interactive decision-making.

This is useful during model selection or research.

Why Interactive Evaluation Matters

A model may answer a question about database operations correctly while failing to perform those operations in a simulated environment.

Interactive tasks introduce additional requirements:

1. Understand the current environment.
2. Select an action.
3. Execute the action.
4. Interpret feedback.
5. Adjust the next action.
6. Continue until the objective is achieved or the task fails.

These capabilities are difficult to assess using static question-answer benchmarks.

Advantages

AgentBench provides a standardized approach to evaluating agent behavior across interactive tasks.

It can support research into model capabilities and limitations.

Limitations

AgentBench is a benchmark suite rather than a complete production evaluation platform.

Its environments may not reflect the exact tools, permissions, workflows, or business requirements of an enterprise application.

A model that performs well on AgentBench may still fail on a company's custom API workflows.

Teams should therefore supplement benchmark results with application-specific evaluations.

Best Suited For

• Agent model benchmarking.
• Interactive decision-making research.
• Comparing language models.
• Evaluating multistep behavior.
• Research-oriented agent testing.

Technical verdict: AgentBench is useful for assessing general agent capabilities, but production teams still need tests based on their own tools and workflows.

Which Framework Should You Choose?

The six frameworks address different evaluation requirements.

They should not be treated as interchangeable products.

Best for Controlled Agent Execution

Inspect AI is a strong candidate when agents must operate inside controlled environments.

It is particularly relevant for coding agents, sandboxed execution, and reproducible research evaluations.

Best for Python-Based Tool-Use Testing

DeepEval provides tool correctness and argument correctness metrics alongside trajectory-level evaluation.

It is useful when agent tests should run as part of the Python development workflow.

Best for Trajectory Analysis

LangSmith is relevant when engineers need detailed execution traces and trajectory evaluation.

It is especially useful for applications built with LangGraph.

Best for Centralized Evaluation Experiments

Braintrust supports dataset-driven experiments, custom scoring, and trace analysis.

It is worth evaluating when multiple engineering teams need a shared evaluation environment.

Best for CI Regression Testing

Promptfoo provides deterministic assertions for tool selection, arguments, and execution order.

It is useful when agent behavior must be checked automatically before deployment.

Best for Agent Benchmarking

AgentBench is appropriate for research-oriented comparisons of agent capabilities across interactive environments.

It is not a replacement for production application testing.

How to Build a Reliable Agent Tool-Use Evaluation Pipeline

A framework provides testing infrastructure.

It does not automatically determine whether an agent behaves correctly.

That requires a structured evaluation process.

Step 1: Define the Agent's Responsibilities

Document the tasks the agent is permitted to perform.

For example, a customer support agent may be allowed to:

• Retrieve customer information.
• Check order status.
• Review refund eligibility.
• Create support tickets.
• Request approval for refunds.

It may not be permitted to execute certain financial actions without additional authorization.

These rules should become testable requirements.

Step 2: Define the Available Tools

For each tool, document:

• Tool name.
• Purpose.
• Input schema.
• Output schema.
• Required permissions.
• Side effects.
• Failure conditions.

For example:

{
  "name": "order_lookup",
  "description": "Retrieve an order by ID",
  "parameters": {
    "type": "object",
    "properties": {
      "order_id": {
        "type": "string"
      }
    },
    "required": ["order_id"]
  }
}

This definition establishes the expected input structure.

The application should also validate the arguments before executing the operation.

Step 3: Create Representative Evaluation Scenarios

A useful evaluation dataset should include successful and unsuccessful scenarios.

Examples include:

Normal operation: The agent retrieves an order using the correct identifier.

Missing information: The user requests an order update without providing an order number.

Ambiguous request: The user has several orders matching the description.

Tool failure: The order lookup API returns an error.

Authorization failure: The user requests information about another customer's order.

Workflow violation: The agent attempts a refund without checking eligibility.

Repeated execution: The agent retries an operation that has already succeeded.

Prompt injection: An external document attempts to instruct the agent to bypass its rules.

These scenarios reveal different failure modes.

Step 4: Record the Execution Trajectory

Each agent run should produce enough information to reconstruct the execution.

A useful trace includes:

• Test case identifier.
• User request.
• Model and prompt version.
• Tool calls.
• Tool arguments.
• Tool responses.
• Execution timestamps.
• Errors.
• Final response.
• Final application state.

Sensitive information should be redacted or excluded according to the application's data-handling requirements.

Step 5: Evaluate Tool Selection

Compare actual tool calls with expected behavior.

For tasks with a single valid tool, exact matching may be appropriate.

For more flexible workflows, evaluate whether the selected tools were permissible and sufficient.

Step 6: Evaluate Arguments

Check whether tool arguments satisfy both the schema and the user's actual intent.

Schema validation alone is insufficient.

For example, an order ID can be a valid string while referring to the wrong order.

Step 7: Evaluate Execution Order

Identify required dependencies between actions.

For example:

Retrieve Order
      |
      v
Check Eligibility
      |
      v
Request Approval
      |
      v
Execute Refund

The evaluation should detect prohibited transitions.

Not every workflow requires exact sequence matching.

Independent read-only operations may be executed in different orders without affecting correctness.

Step 8: Verify the Final Outcome

Do not rely exclusively on the agent's final message.

If the agent claims to have created a ticket, verify that the ticket exists.

If it claims to have updated a record, inspect the resulting application state.

For consequential actions, outcome verification should use a trusted source independent of the agent's generated response.

Step 9: Measure Efficiency

Track:

• Tool calls per completed task.
• Redundant tool calls.
• Failed tool calls.
• Model inference requests.
• Total execution latency.
• Cost per successful task.

Efficiency should be evaluated only after correctness and safety requirements are satisfied.

Step 10: Run Regression Tests

Repeat evaluations when the team changes:

• Language models.
• System prompts.
• Tool definitions.
• API integrations.
• Agent orchestration.
• Authorization rules.
• Retry behavior.

Maintain a versioned evaluation dataset so results remain comparable.

Example: A Lightweight Tool-Use Evaluator in Python

Before adopting a larger evaluation platform, engineers can implement basic deterministic checks.

The following example evaluates required tool calls, argument correctness, and prohibited actions.

def evaluate_tool_use(
    actual_calls,
    required_calls,
    prohibited_tools
):
    results = {
        "required_calls_present": True,
        "arguments_correct": True,
        "no_prohibited_tools": True
    }

    actual_tool_names = [
        call["name"]
        for call in actual_calls
    ]

    for expected in required_calls:
        if expected["name"] not in actual_tool_names:
            results["required_calls_present"] = False

        matching_calls = [
            call
            for call in actual_calls
            if call["name"] == expected["name"]
        ]

        if not any(
            call.get("arguments") == expected["arguments"]
            for call in matching_calls
        ):
            results["arguments_correct"] = False

    for call in actual_calls:
        if call["name"] in prohibited_tools:
            results["no_prohibited_tools"] = False

    return results


actual_calls = [
    {
        "name": "order_lookup",
        "arguments": {
            "order_id": "12345"
        }
    },
    {
        "name": "refund_eligibility",
        "arguments": {
            "order_id": "12345"
        }
    }
]

required_calls = [
    {
        "name": "order_lookup",
        "arguments": {
            "order_id": "12345"
        }
    },
    {
        "name": "refund_eligibility",
        "arguments": {
            "order_id": "12345"
        }
    }
]

prohibited_tools = {
    "refund_execution"
}

results = evaluate_tool_use(
    actual_calls,
    required_calls,
    prohibited_tools
)

print(results)

Expected output:

{
    "required_calls_present": True,
    "arguments_correct": True,
    "no_prohibited_tools": True
}

What This Evaluator Measures

The example checks whether:

1. Required tools were called.
2. Expected arguments were supplied.
3. Prohibited tools were avoided.

What It Does Not Measure

It does not determine whether:

• Tools executed successfully.
• Calls occurred in the correct order.
• The agent completed the task.
• The returned data was accurate.
• The agent handled errors correctly.
• The final application state was valid.

Those requirements need additional assertions.

For example, a refund workflow should verify that an authorized refund was created exactly once.

A production evaluator should also account for repeated calls and workflows with multiple valid execution paths.

Metrics for Agent Tool-Use Evaluation

A production evaluation process should measure several dimensions independently.

Tool Selection Accuracy

Measures whether the agent selected the expected tools.

For tasks with a single expected tool:

Tool selection accuracy = Correct tool selections ÷ Total evaluated selections

Argument Accuracy

Measures whether tool arguments match the expected values.

Argument accuracy = Correctly parameterized calls ÷ Evaluated calls

Argument correctness should include semantic checks where necessary.

Task Completion Rate

Measures the percentage of tasks successfully completed.

Task completion rate = Successfully completed tasks ÷ Total attempted tasks

Success should be verified through trusted outcomes.

Workflow Compliance Rate

Measures how often the agent follows mandatory workflow requirements.

For example, a refund agent should check eligibility before execution.

Unauthorized Action Rate

Measures the proportion of evaluated runs containing prohibited actions.

This metric should be reported separately from overall task success.

Tool Failure Recovery Rate

Measures whether the agent can recover from recoverable tool failures.

For example, it may retry a read-only request after a temporary timeout.

Retries of consequential actions require additional safeguards against duplicate execution.

Average Tool Calls per Successful Task

Measures execution efficiency.

Lower values are not always better.

A workflow that skips necessary validation may use fewer calls while producing an unsafe outcome.

End-to-End Latency

Measures the time required to complete the full task.

Track percentile latency rather than relying only on averages.

Cost per Successful Task

Measures the total cost of successfully completed tasks.

Include model inference, external APIs, and relevant infrastructure expenses.

How to Evaluate Agents With Multiple Valid Tool Paths

Exact trajectory matching is useful when a workflow has strict ordering requirements.

However, many tasks can be completed through several valid sequences.

Consider a research agent that must gather information from two independent sources.

The agent could retrieve Source A before Source B or retrieve Source B before Source A.

Both approaches may be correct.

An evaluator that requires one exact sequence would incorrectly reject one of them.

Use Invariants Instead of Exact Sequences

An invariant is a condition that must remain true regardless of the execution path.

Examples include:

• The agent must verify authorization before a protected action.
• The agent must not execute prohibited tools.
• The agent must use valid identifiers.
• The agent must not perform the same consequential action twice.
• The final state must satisfy the task requirements.

Evaluate Partial Ordering

Some actions must happen before others, while independent operations can occur in any order.

For example:

Customer Lookup
      |
      v
Eligibility Check
      |
      v
Approval
      |
      v
Refund Execution

The evaluator can require these dependencies without requiring every intermediate read-only operation to occur in one exact position.

Verify State Transitions

For agents that modify external systems, checking the final state is often more reliable than comparing generated messages.

For example:

Initial State:
Refund Status = Not Started

Expected Final State:
Refund Status = Completed
Approval = Verified
Refund Count = 1

This approach measures whether the system reached the intended outcome.

It should be combined with checks for prohibited intermediate actions, since a correct final state does not prove that every action was authorized.

Common Mistakes in Agent Tool-Use Evaluation

Evaluating Only the Final Response

An agent may claim success without performing the required action.

Inspect actual tool execution and final system state.

Treating Tool Selection as Task Completion

Calling the correct tool does not guarantee a successful outcome.

The arguments, execution result, and final state also matter.

Requiring One Exact Tool Sequence for Every Task

Some workflows permit multiple valid paths.

Use invariants and partial-order checks when appropriate.

Ignoring Unauthorized Actions

An agent may complete the requested task while performing prohibited operations.

Safety and authorization must be evaluated independently.

Running Tests Against Live Production Systems

Write-capable agent tests can modify records, send messages, or trigger financial operations.

Use isolated environments and test fixtures wherever possible.

Relying Entirely on LLM-Based Judges

LLM evaluators can make mistakes.

Use deterministic checks for schemas, permissions, and state transitions when possible.

Ignoring Tool Failure Scenarios

Agents must handle timeouts, invalid responses, and unavailable services.

Test these conditions explicitly.

Measuring Efficiency Before Correctness

An agent that uses fewer tool calls is not necessarily better.

Correctness, authorization, and task completion should take priority.

Frequently Asked Questions

What is agent tool-use evaluation?

Agent tool-use evaluation measures whether an AI agent selects appropriate tools, supplies correct arguments, follows required workflows, handles execution results, and completes tasks successfully.

Which framework is best for evaluating agent tool calls?

The answer depends on the evaluation requirements.

DeepEval and Promptfoo are useful for automated tool-call checks.

Inspect AI supports controlled execution environments.

LangSmith and Braintrust provide trace-linked evaluation workflows.

AgentBench is more suitable for standardized research benchmarking.

What is the difference between tool correctness and task completion?

Tool correctness examines whether the agent selected and called appropriate tools.

Task completion measures whether the intended outcome was achieved.

An agent can call the correct tools and still fail the task.

How do you evaluate tool arguments?

Validate the argument schema and compare values against expected inputs.

For sensitive operations, also verify that arguments correspond to authorized resources.

Should agent evaluations check execution order?

Yes, when order affects correctness or safety.

For example, authorization should precede protected actions.

However, exact ordering is unnecessary for independent operations that can occur in any valid sequence.

Can LLM-as-a-judge evaluate agent trajectories?

Yes.

A judge model can assess whether an execution path appears appropriate.

However, its judgments should be calibrated against human-reviewed examples and supplemented with deterministic checks.

How do you test agents that modify external systems?

Use isolated test environments with controlled data.

Record tool calls and verify resulting state changes.

Avoid running destructive or consequential evaluations against live production systems.

How do you measure agent efficiency?

Track tool calls, model requests, latency, resource usage, and cost per successful task.

Efficiency should be considered alongside correctness and safety.

Is AgentBench suitable for enterprise agent testing?

AgentBench is useful for general agent benchmarking.

However, enterprise applications also require tests involving their actual tools, permissions, workflows, and operating conditions.

How often should agent evaluations run?

Important changes to models, prompts, tools, and orchestration should trigger relevant regression tests.

Production teams should also review representative traces and add newly discovered failures to their evaluation datasets.

Final Recommendations

Evaluating AI agents requires examining both their decisions and their outcomes.

Choose Inspect AI when controlled execution environments and reproducible agent evaluations are important.

Consider DeepEval for Python-based tool correctness, argument validation, and task completion testing.

Evaluate LangSmith when trajectory analysis and execution tracing are central requirements.

Consider Braintrust for centralized datasets, experiments, and custom agent scoring.

Use Promptfoo when deterministic tool-call assertions and CI regression testing are priorities.

Evaluate AgentBench when comparing general agent capabilities across interactive environments.

The best evaluation strategy may combine more than one approach.

For example, a team could use Promptfoo for tool-call regression testing, Inspect AI for controlled execution tasks, and a tracing platform for investigating production behavior.

The goal is not to adopt the largest number of frameworks.

It is to verify that agents select appropriate tools, execute authorized actions, handle failures correctly, and achieve the intended outcomes without introducing unacceptable operational risk.