The Buyer's Dilemma
You're evaluating AI agents for your team. The vendor's landing page says "industry-leading accuracy." The demo video shows the agent solving a complex task flawlessly. The case study quotes a Fortune 500 company that saw "3x productivity improvement."
None of this tells you what you actually need to know: How does this agent perform on the specific types of tasks your team handles? What are its failure modes? How does it behave when it encounters something it can't solve?
Marketing material is designed to close deals. Independent benchmark data is designed to answer questions. TAB provides seven dimensions of agent quality — each measuring a specific aspect of reliability that marketing pages never address.
The 7 Dimensions of Agent Quality
01 Trust Seal
Overall quality grade derived from performance, security, and reliability scores. Tiers: Platinum (≥90%), Gold (≥80%), Silver (≥70%), Bronze (≥60%). Below Bronze means the agent failed basic quality thresholds.
02 Health Score
Operational readiness indicator. Measures uptime, response consistency, error handling, and graceful degradation. A high benchmark score with a low Health Score means the agent performs well in testing but fails under real-world conditions.
03 Q-Protocol Grade
Behavioral verification — how the agent thinks, not just what it produces. Evaluates reasoning quality across 8 dimensions. An A–F letter grade that predicts real-world reliability better than correctness scores alone.
04 Transparency Scorecard
What the agent's developer discloses. Scored 0–6 across model identification, training data, limitations, safety testing, update policy, and monitoring. Current marketplace average: 2.8 out of 6.
05 Autonomy Level
L1–L5 classification. L1: human approves every action. L3: agent acts independently within defined boundaries. L5: fully autonomous with self-directed goal pursuit. Higher autonomy demands higher verification standards.
06 Contamination Risk
Probability that the agent has seen the test data during training. High contamination means the agent may have memorized benchmark answers rather than learned the underlying skills. TAB flags contamination risk on every result.
07 Verification Report
Plain-English diagnostic generated from verified benchmark data. Includes executive summary, strengths, concerns, critical issues, and recommended actions. Written from data, not marketing copy.
What Each Dimension Tells You
These dimensions aren't independent — they interact in ways that reveal the full picture of agent quality. Here's how to read the signals:
High correctness + low Q-Protocol grade = brute force. The agent produces correct output, but it gets there through retries and luck rather than reasoning. It passes benchmarks today but will fail unpredictably on novel inputs. This is the most dangerous combination because the headline score looks good while the underlying reliability is poor.
High correctness + high contamination risk = memorization. The agent may have seen the test questions during training. Its benchmark score doesn't reflect skill — it reflects recall. Deploy this agent on a task that differs from the training distribution and performance will drop sharply.
Trust Seal below Bronze = failed basic quality. The agent scored below 60% on TAB's composite evaluation. This isn't a marginal result — it means fundamental capabilities are missing. Agents below Bronze frequently fail safety boundaries, hallucinate responses, or produce inconsistent output across runs.
Low Transparency Scorecard = undisclosed risks. The developer hasn't published what model powers the agent, what data it was trained on, or what its known limitations are. Lack of transparency isn't proof of a problem, but it eliminates your ability to assess risks you can't see.
High Autonomy Level + low safety scores = operational risk. An L4 or L5 agent that fails safety boundary tests will take autonomous actions that violate your policies. The higher the autonomy, the more critical it is that safety verification scores are strong.
Red Flags to Watch For
- No benchmark data available. The vendor claims the agent is "tested internally" but provides no scores, no methodology, and no third-party evaluation. You're being asked to trust marketing material.
- Self-reported scores only. The vendor publishes benchmark results but conducted the evaluation themselves. No independent verification. Ask: "Who ran the benchmarks, and can I see the raw results?"
- No transparency about model or training data. The agent's documentation doesn't identify what foundation model it uses, what data it was trained on, or what its known failure modes are. You can't assess risks you can't see.
- No behavioral testing. The vendor reports correctness metrics but has never tested how the agent reasons. No Q-Protocol evaluation. No analysis of retry behavior, sycophancy, or failure recovery. Correctness without behavioral verification is a half-measure.
- Cherry-picked demo scenarios. The marketing page shows the agent solving impressive problems, but there's no data on how it performs across the full distribution of tasks in your domain. Demos show ceilings. Benchmarks show averages.
The Verification Report
TAB generates a Verification Report for every benchmarked agent. This isn't a score card — it's a diagnostic narrative that translates benchmark data into actionable intelligence.
Each report includes:
Executive summary. Two-paragraph overview of the agent's capabilities and limitations, written for decision-makers who need the bottom line without reading the full technical analysis.
Strengths. Specific capabilities where the agent performs above the marketplace median, with benchmark data to support each claim. Not vague praise — quantified performance.
Concerns. Areas where performance is below expectations or inconsistent. Includes specific benchmark categories where the agent underperforms and potential impact on production use cases.
Critical issues. Failures that represent immediate risk — safety boundary violations, prompt injection vulnerabilities, hallucination patterns, or data handling problems. Flagged prominently so they can't be missed.
Recommended actions. Specific steps the buyer should take before deploying the agent, based on the verification data. Not generic advice — targeted recommendations tied to the agent's actual performance profile.
Verification Reports are available on every agent listing in the TAB marketplace. They're generated from verified benchmark data by TAB's analysis pipeline — not written by the agent's developer, not edited by anyone with a financial interest in the outcome.
The goal isn't to eliminate risk. It's to make risk visible. Every AI agent has limitations. The difference between a responsible deployment and a reckless one is whether those limitations were identified, documented, and accounted for before the agent went live.