How to Evaluate AI Agents: Trust Scores, Uptime & Protocol Support Explained

Why Evaluation Matters

Every registry will tell you an agent exists. Almost none will tell you if it's any good. When you're integrating an AI agent into a production system, you need to know: Will it be online when my users need it? Does it handle edge cases consistently? Is it secure enough for my compliance requirements? Does it actually support the protocols it claims to? Trust scores answer these questions with objective, measurable data.

AgentResourceDB's trust score is a 0-100 rating based on 12 factors. Every agent in our registry is scored using the same methodology, regardless of which source registry it comes from. This means you can compare an agent from OpenAI's GPT Store directly against one from LangChain Hub — apples to apples.

The 12-Factor Trust Score Methodology

Each factor is scored 0-100 and weighted equally in the composite score. Here's what each factor measures and why it matters.

1. Uptime Reliability

Measures the percentage of time the agent responds to requests over a 30-day rolling window. Agents with 99.9%+ uptime score 95-100. Agents below 95% uptime are flagged as unreliable. Uptime is checked via automated liveness pings every 5 minutes from multiple geographic regions.

2. Response Consistency

Evaluates whether the agent produces similar outputs for similar inputs. We send the same prompt 10 times and measure variance in the responses. High consistency (low variance) scores well. This catches agents that hallucinate randomly or produce unstable outputs.

3. Security Posture

Assesses the agent's security practices: does it sanitize inputs? Does it expose sensitive data in logs? Does it use encryption for API calls? Agents that pass automated security scans score 90+. Agents with known vulnerabilities score below 70.

4. Documentation Quality

Evaluates the completeness and accuracy of the agent's documentation. We check for API docs, usage examples, parameter descriptions, and error handling guides. Well-documented agents score 90+; agents with no documentation score below 50.

5. Community Adoption

Measures real-world usage signals: number of integrations, GitHub stars (for open-source agents), download counts, and API call volume. High adoption indicates the agent is battle-tested. This factor helps separate production-grade agents from experimental ones.

6. Protocol Compliance

Verifies that the agent actually implements the protocols it claims to support. We test MCP tool calls, A2A Agent Card endpoints, and ACP REST interfaces. Agents that claim MCP support but fail protocol tests lose significant points. Learn how each protocol works in our explainers: MCP, A2A, ACP.

7. Error Rate Resistance

Measures how the agent handles malformed inputs, rate limits, and edge cases. Agents that degrade gracefully (returning helpful error messages) score well. Agents that crash or return 500 errors score poorly. This factor is critical for production reliability.

8. Latency Stability

Evaluates response time consistency. We measure P50, P95, and P99 latency over 100 requests. Agents with stable latency (low P99/P50 ratio) score well. Agents with wild latency swings — fast sometimes, slow other times — score poorly because they're unpredictable.

9. Versioning Discipline

Assesses whether the agent follows semantic versioning, maintains changelogs, and provides backward compatibility. Agents with clear versioning practices score 90+. Agents that break changes without version bumps score below 60.

10. Dependency Health

Evaluates the health of the agent's dependencies — APIs, models, and external services it relies on. If an agent depends on a deprecated API or an unreliable model endpoint, its dependency health score drops. This factor helps predict future reliability issues.

11. Data Provenance

Tracks where the agent's training data and knowledge base come from. Agents with transparent, documented data sources score well. Agents with unknown or potentially copyrighted data sources score poorly. This factor is especially important for legal and healthcare agents.

12. Peer Verification

Measures whether other trusted agents and registries vouch for this agent. Agents that are cross-referenced across multiple registries and endorsed by reputable sources score well. This is the social proof layer — it catches agents that look good on paper but haven't been validated by the community.

How to Use Trust Scores in Practice

Trust scores are a filter, not a verdict. Here's a practical framework for using them when evaluating agents.

Red Flags to Watch For

• Uptime below 95%: The agent goes down regularly. Unacceptable for production.
• Protocol compliance below 80: The agent claims to support MCP/A2A/ACP but fails tests. Integration will break.
• Security posture below 70: Known vulnerabilities. Do not deploy in regulated environments.
• Documentation below 50: No API docs or examples. You'll spend hours reverse-engineering the agent.
• No peer verification: The agent exists on only one registry with no community validation. Could be abandoned or malicious.

Comparing Agents Side by Side

The registry page lets you filter by trust score range, so you can quickly narrow to production-grade agents (90+). From there, compare the 12-factor breakdown on each agent's profile page to find the one that's strongest in the factors that matter most for your use case.

For example, if you're deploying a healthcare agent, prioritize security posture, data provenance, and protocol compliance. If you're deploying a coding agent, prioritize response consistency, error rate resistance, and documentation quality. Browse the registry to start comparing.