Agentic Evaluation Harness Services
Reviewed by Umar Abbas • Founder & Principal AI Architect
An agentic evaluation harness is an automated testing framework designed to evaluate multi-step agent trajectories, tool-calling precision, and final response accuracy across diverse prompt benchmarks. We engineer custom LLM-as-a-judge evaluators, synthetic dataset generators, and CI/CD regression gates using Ragas and DeepEval.
Automated Trajectory Evaluation Pipeline
Continuous Agent Evaluation Pipeline
Interactive Flow Diagram
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| Step | Stage Name | Function & Detail | Metrics / SLA |
|---|---|---|---|
| 1 | N/A | ||
| 2 | N/A | ||
| 3 | N/A | ||
| 4 | N/A | ||
| 5 | N/A |
PyTest Trajectory Evaluation Suite
import pytest
from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase
trajectory_correctness_metric = GEval(
name="Trajectory Correctness",
criteria="Evaluate whether the agent executed minimum required tools without redundant calls.",
evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.TOOLS_CALLED]
)
@pytest.mark.asyncio
async def test_agent_account_lookup_trajectory():
agent_output = await run_test_agent("Find ledger summary for ACC-99201")
test_case = LLMTestCase(
input="Find ledger summary for ACC-99201",
actual_output=agent_output.final_text,
tools_called=agent_output.tool_history
)
trajectory_correctness_metric.measure(test_case)
assert trajectory_correctness_metric.score >= 0.95Benchmark Trajectory Test Coverage Calculator
Automated Test Suite Coverage vs Defect Mitigation
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At baseline baseline volume of 50,000 Test Cases: Legacy execution cost: $2,000/mo ($0.04/unit). Optimized architecture cost: $400/mo ($0.008/unit). Net monthly cost savings: $1,600/mo (80% cost reduction).
1,200 CI Build Executions Telemetry
Frequently Asked Questions
What metrics does an agentic evaluation harness measure?↓
We measure trajectory step efficiency, tool-calling precision, schema compliance, hallucination rates, and end-to-end task completion accuracy.
How do you run agent evaluation tests in GitHub Actions?↓
We build PyTest test suites that invoke isolated mock tool servers and run trajectory assertions against static benchmark datasets during git pull requests.
Is LLM-as-a-judge deterministic enough for CI/CD gates?↓
We combine deterministic schema validators for tool outputs with calibrated judge models running at zero temperature across 5 evaluation runs.
How long does it take to implement an agent evaluation harness?↓
Harness setup and benchmark dataset curation take 3 to 6 weeks, including CI/CD integration and regression dashboard deployment.
Who owns the test datasets and evaluation harness code?↓
Your engineering team holds 100% ownership of all evaluation scripts, test cases, and CI pipeline configurations.
Build Automated QA Harnesses for AI Agents
Consult with Founder & Principal AI Architect Umar Abbas to set up continuous agent regression testing.
Request QA Architecture Review