The client needed to evaluate agent systems beyond surface-level outputs. Traditional benchmarks measure correctness of final responses, but agent workflows depend on correctly executing sequences of tool calls, handling intermediate state, and completing tasks end-to-end.

The challenge was to design an evaluation framework that could:

• Measure actual workflow completion, not just response quality
• Detect failures in tool selection, parameter usage, and execution order
• Evaluate multistep reasoning and recovery from errors
• Provide repeatable, comparable signals across models
• Surface fine-grained failure patterns, not just pass/fail outcomes

This required an execution-based benchmark grounded in real workflows and validated through system-level checks.

Turing designed a structured evaluation framework combining realistic workflow tasks, repeated rollouts, and verifier-based validation.

1. Workflow-based task design

Each task simulated a real HR scenario requiring the agent to:

• Retrieve and validate candidate or offer data
• Trigger workflows such as onboarding, background checks, or case updates
• Execute multiple tool calls in sequence
• Update system state correctly

Tasks reflected real operational processes rather than synthetic prompts, ensuring realistic evaluation conditions

2. Execution-grounded evaluation using verifiers

Instead of relying on output inspection, evaluation used verifiers:

• Automated checks that confirm whether required actions were completed
• Based on database state changes after tool execution
• Compared expected vs actual outcomes for each step

This ensured that success required correct execution, not just plausible responses.

3. Multi-run benchmarking for stability

Each task was executed 10 times per model, producing:

• Pass@10 scores per task
• Distribution of success across runs
• Identification of unstable or inconsistent behavior

This approach reduced noise and enabled reliable comparison across models.

4. Failure taxonomy and root-cause analysis

Every failed run was classified into one or more categories:

• Tool use accuracy: Incorrect tool selection
• Parameter use accuracy: Incorrect or hallucinated inputs
• Multistep reasoning: Failure to follow the correct sequence
• Error handling & recovery: Failure to recover from tool errors
• Tool sequence / ordering: Correct tools used in the wrong order

This taxonomy enabled systematic analysis of failure modes rather than treating all failures equally

5. Cross-model comparison and domain analysis

Performance was analyzed across:

• Frontier models (Claude Opus, GPT, Qwen families)
• Workflow domains such as case management, background checks, and immigration
• Task difficulty levels and failure patterns

This provided both relative performance ranking and domain-specific insights.

• Evaluated more than 100 workflow tasks across 1,000+ runs per model, producing stable benchmarking signals
• Achieved clear performance separation, with top model at ~70% pass rate vs ~50% and ~5% for others
• Identified tool sequencing errors as the dominant failure mode, affecting over 80% of failures in top models
• Revealed parameter hallucination as a model-specific weakness, absent in top-tier models but prevalent in lower-performing ones
• Demonstrated that multistep reasoning quality scales with model capability, with higher-capacity models maintaining better execution plans

The client gained a reproducible, execution-grounded dataset that measures real agent performance across workflows. By validating outcomes through verifiers and analyzing failures at a granular level, the framework enables:

• Reliable comparison of agent capabilities across models
• Identification of systemic weaknesses in tool use and reasoning
• Tracking of model improvements across releases
• Better alignment between model outputs and real-world system behavior

This approach provides a foundation for evaluating agent systems as they move from isolated tasks to full workflow execution.