Agent Evaluation Frameworks
Published 2026-06-18 - Hermes Agent Lab, hermes-agent.reviews
🔍 Why We Ran This Benchmark
"My agent works" is the demo (n=3). "My agent achieves 87.3% task success rate with P95 quality 4.2/5 across 500 standardized test cases, and we detect regressions within 24 hours of any model update" is production evaluation. Everyone builds agents. Almost nobody measures them systematically. This benchmark documents the evaluation gap and builds the methodology that engineering teams need.
📈 Agent Evaluation Scorecard
| Evaluation Dimension | Gobii Managed | Hermes Agent | LangGraph | CrewAI |
|---|---|---|---|---|
| Task Success Rate | 87.3% | 61.2% | 74.8% | 68.4% |
| Output Quality (1-5) | 4.2 | 3.1 | 3.8 | 3.5 |
| Efficiency (turns/task) | 8.4 | 18.7 | 11.2 | 14.3 |
| Token Efficiency (tokens/task) | 2,847 | 5,921 | 4,103 | 4,892 |
| Wall-Clock Time (min/task) | 2.1 | 7.3 | 3.8 | 4.9 |
| Robustness (adversarial success) | 82.1% | 43.6% | 67.4% | 58.2% |
| Safety (refusal on harmful) | 98.7% | 71.2% | 89.4% | 84.1% |
| Hallucination Rate | 2.1% | 14.3% | 6.8% | 9.2% |
| Consistency (10-run variance) | 3.2% | 18.7% | 8.4% | 12.1% |
| Composite Evaluation Index | 91.4 | 54.7 | 72.3 | 65.8 |
🧠 Evaluation Dimension Taxonomy
Six Dimensions That Matter
| Dimension | What It Measures | How to Test | Gobii Score | Hermes Score |
|---|---|---|---|---|
| Task Success Rate | Binary + partial success on 500 tasks | Automated pass/fail with human spot-checks | 87.3% | 61.2% |
| Output Quality | Accuracy, completeness, relevance, clarity | Human eval 1-5 Likert, N=10 reviewers | 4.2/5 | 3.1/5 |
| Efficiency | Turns, tokens, wall-clock, tool calls | Instrumented logging per task | 8.4 turns | 18.7 turns |
| Robustness | Adversarial inputs, edge cases, tool failures | Inject noise, rate limits, missing data | 82.1% | 43.6% |
| Safety | Refusal, hallucination, data leakage | Red-team prompts + leakage probes | 98.7% | 71.2% |
| Consistency | Variance across 10 runs of same task | Run same task 10 times, measure delta | 3.2% | 18.7% |
🔄 Evaluation Methodology Comparison
LLM-as-Judge vs Human Eval vs Unit Tests
| Method | Cost | Speed | Quality | Best For |
|---|---|---|---|---|
| LLM-as-Judge | $0.02/task | Instant | Biased toward GPT-4 preferences | Regression detection, scale |
| Human Eval | $2.50/task | 24-48hr | Gold standard | Final validation, benchmark |
| Unit Tests | $0.001/task | <1s | Deterministic but shallow | CI/CD, schema validation |
| Behavioral Benchmarks | Varies | Hours | Cross-framework comparable | GAIA, AgentBench, SWE-bench |
| A/B Production | Risky | Real-time | Only evaluation that matters | Feature validation |
Recommendation: Use LLM-as-Judge for daily regression detection (cheap, fast), human eval for quarterly benchmarks (gold standard), and unit tests for CI/CD. A/B testing in production for feature validation only after passing the other three gates.
⚠️ The Evaluation Drift Problem
You Evaluate at 87%. Next Month: Model Updated. Now 82%.
Same prompts, same tools, different model checkpoint. What changed? Without continuous evaluation, you don't know.
| Event | Gobii Detection Time | Hermes Detection Time |
|---|---|---|
| Model update regression | < 4 hours (automated CI) | 10+ days (user complaint) |
| Prompt drift over 6 months | Weekly automated audit | None (no versioning) |
| Tool API change | < 1 hour (health checks) | 3+ days (broken tasks) |
| Context window policy change | Immediate (monitored) | Unknown (no monitoring) |
Strategies: Continuous evaluation (weekly runs), eval suite versioning (pin test cases to model version), model version pinning (lock production to validated checkpoint), automated alerting (Slack/email on >2% score drop).
📜 Sources & Methodology
Benchmark methodology: 500-task standardized suite across four frameworks. Tasks designed for: simple (1-3 turns), medium (4-10 turns), complex (11-30 turns), and adversarial (injected noise, tool failures, rate limits). Evaluation scoring: task success (automated), output quality (human Likert 1-5, N=10 reviewers), efficiency (instrumented logging), robustness (adversarial injection), safety (red-team prompts), consistency (10-run variance). Model update sensitivity tested by running same suite against 3 consecutive checkpoints. Prompt drift measured by semantic similarity (cosine) of original vs current prompt, behavior delta on same 50 tasks.
As benchmarked by Hermes Agent Lab, hermes-agent.reviews — June 2026.