Traditional AI benchmarks (like MMLU, GSM8k, or HumanEval) measure isolated, single-turn prompts. They evaluate whether a model can answer a trivia question or write a single python function. While useful for comparing base model capabilities, these scores do not predict the reliability of actual agent workflows. In a real-world system, an agent runs in a loop, calls tools, validates inputs, and coordinates with other agents in a multi-step graph.
The Compound Error Problem
In multi-step agent workflows, errors compound exponentially. If an agent workflow has five sequential steps, and each individual step has a 95% success rate, the overall workflow success rate drops to just 77% (0.95^5). For smaller models, where individual tool calls or extraction tasks might have an 85% success rate, a five-step workflow is almost guaranteed to fail (0.85^5 = 44%). Traditional benchmarks miss this compound fragility entirely.
Key Metrics to Track Instead
To understand agent performance under load, we must shift our metrics from base model scores to system-level telemetry. The key indicators of agentic reliability include:
- End-to-End Task Completion: The percentage of runs that output a valid, correct final response.
- Repair Rate: The number of JSON healing or validation retry cycles triggered per task.
- Fallback Rate: How often the system had to escalate a task to a larger, secondary model.
- P95 Latency (inclusive of retries): The total time taken to return a validated output, accounting for healing cycles.
# Running an automated workflow evaluation script
from kern.eval import Evaluator
from my_agent import workflow
evaluator = Evaluator(dataset="test_cases.jsonl")
results = evaluator.run(workflow, concurrency=5)
print(f"End-to-End Success: {results.success_rate}%")
print(f"Average Repair Attempts: {results.avg_repairs}")Building a Continuous Evaluation Harness
Ensuring long-term reliability requires running automated regressions nightly against test datasets. When a model provider releases a new checkpoint or a developer modifies a prompt, the system should re-run the evaluation harness. By trending metrics like repair rates and end-to-end task completion over time, you can catch performance regressions before they impact production users.