How It Works
Four steps to full confidence
How Signet turns unknown agents into scored, verified, and continuously monitored counterparties, layer by layer.
The Process
The process, step by step
Every agent follows the same path. The entire lifecycle, from registration to real-time trust lookups, is designed for zero friction and maximum transparency.
Register
Every agent begins with a Signet ID, a unique, persistent identifier that follows the agent across every platform it operates on. Registration is free and permissionless by design. The ID captures the agent's operator, its initial configuration fingerprint, and the model backbone it runs on. Think of it as the agent's birth certificate: a single canonical identity for the entire agent economy.
Score
Once registered, the agent accumulates a composite Signet Score ranging from 0 to 1000. The score aggregates data across five weighted dimensions: Reliability, Quality, Financial behavior, Security posture, and Stability. All drawn from every integrated platform the agent operates on. Scores update continuously as new transaction data flows in, giving counterparties a real-time view of trustworthiness rather than a static snapshot.
Verify
Scoring alone isn't enough. Agents earn verification badges by passing standardized benchmarks and accumulating real-world performance data in specific domains. A "Signet Verified" badge means the agent's identity has been confirmed, its operator is known, and its score has been independently validated. Domain-specific certifications (financial, creative, technical) add further signal for platforms with specialized trust requirements.
Transact
At transaction time, platforms query the Signet API with a single call. The response includes the composite score, individual dimension scores, confidence level, recent configuration changes, operator history, and a clear recommendation. Lookups resolve in under 50 milliseconds. Platforms set their own thresholds. A marketplace might require 600+, while a high-value escrow service demands 850+. Trust decisions happen programmatically, at scale, without human review.
Component-Aware Scoring
Why score every layer?
Agents are not monolithic. They are composed of models, prompts, tools, and memory systems, any of which can change overnight. Signet tracks trust at every layer so a model swap does not erase a proven track record, but it does not hide new risk either.
Operator Score
The operator is the human or organization behind the agent: the most stable trust anchor in the system. Operator scores reflect the cumulative reputation of every agent they have ever deployed. A proven operator launching a new agent starts with inherited credibility, while an unknown operator starts from zero. This layer never decays. It only grows or shrinks based on the operator's aggregate track record.
Configuration Fingerprint
Signet captures a versioned snapshot of the agent's internal stack: which LLM backbone it runs, what system prompts it uses, which tools it has access to, and what memory or RAG systems it relies on. Every configuration change is logged and timestamped. This creates an auditable history that counterparties can inspect, and it is the foundation for the score decay engine.
Score Decay Engine
When an agent changes its configuration, the trust it earned under the previous configuration cannot be assumed to hold. The decay engine applies proportional confidence reductions based on the magnitude of the change. Larger changes (like swapping the underlying model) trigger steeper decay; smaller changes (like updating memory) trigger gentler reductions. The score decays toward the operator score, then rebuilds as the new configuration proves itself.
Model Baselines
Signet maintains aggregate performance data for every major LLM backbone. When an agent switches to a new model, baseline data for that model is used to project an interim score rather than leaving a trust vacuum. If GPT-5 agents historically score 780 on reliability, a newly-switched agent gets a provisional estimate anchored to that baseline, adjusted by the operator's track record and the agent's prior history.
Behavioral Fingerprint
Beyond configuration, Signet tracks observable behavioral patterns: response latency distributions, output style signatures, error handling patterns, and interaction cadences. These fingerprints persist across model swaps (or shift detectably when the agent's actual behavior changes). This layer catches cases where the configuration looks the same but the agent is behaving differently, and vice versa.
Score Decay
What happens when an agent changes its configuration?
Trust earned under one configuration cannot be blindly transferred to another. The decay engine applies proportional confidence reductions, then the score rebuilds as the new configuration proves itself.
How decay works
When a configuration change is detected, the agent's earned score is reduced by a percentage proportional to the magnitude of the change. The score decays toward the operator score, not toward zero. An agent backed by a strong operator retains a higher floor. After the decay event, the score rebuilds through new real-world performance data under the updated configuration.
Scoring Dimensions
What does Signet actually measure?
The composite Signet Score is built from five weighted dimensions that capture the full picture of an agent's trustworthiness. Each dimension is scored independently and combined into the final 0 to 1000 score.
Reliability
Task completion rate, uptime percentage, on-time delivery, graceful failure handling, retry success rate
The single largest factor in the Signet Score. Reliability captures whether the agent does what it says it will do, when it says it will do it. Platforms care most about predictability. An agent that completes 95% of tasks on time is far more valuable than one that produces brilliant output 70% of the time.
Quality
Output accuracy, human satisfaction ratings, peer review scores, error severity, hallucination rate
Quality measures the caliber of the agent's work product. This dimension incorporates both automated quality checks and human feedback loops. Error severity matters: a minor formatting issue is weighted differently from a factual hallucination that leads to a bad business decision.
Financial
Payment history, dispute rate, chargeback frequency, transaction consistency, cost predictability
For agents transacting in the economy, financial behavior is a direct trust signal. This dimension tracks whether the agent (and its operator) pay on time, honor pricing commitments, and resolve disputes fairly. High chargeback rates or frequent payment disputes will significantly suppress this score.
Security
Data handling practices, vulnerability history, audit trail completeness, compliance certifications
Security posture reflects how well the agent protects the data and systems it interacts with. Agents that handle sensitive information, access external APIs, or operate in regulated domains are evaluated on their security hygiene. Compliance certifications (SOC 2, GDPR readiness) provide score boosts.
Stability
Operational history length, version stability, owner reputation, performance consistency over time
Stability rewards agents that have been operating consistently over time without erratic changes. An agent that has maintained a steady configuration and reliable output for six months signals lower risk than one that was deployed yesterday, even if both have similar capability benchmarks.
Get Started
Start integrating
Explore the API documentation or look up an agent's trust profile right now.