theaios-trustgate 0.1.2

Black-box AI reliability certification via self-consistency sampling and conformal calibration

Know if your AI is ready to ship — one number, one guarantee.

TrustGate certifies the reliability of any AI endpoint — LLMs, agents, RAG pipelines, or any system you can ask a question to. It uses self-consistency sampling and conformal prediction to produce a single reliability level (e.g., 94.6%) backed by a formal statistical guarantee. Not a vibe, not a leaderboard score — a mathematical proof.

What's included:

• Self-consistency sampling — ask the same question K times, measure agreement
• Conformal calibration — formal coverage guarantee, distribution-free
• Human calibration — shareable HTML questionnaire for domain experts (no server needed)
• Runtime trust layer — wrap any endpoint with reliability metadata
• Sequential stopping — Hoeffding bounds reduce API costs by ~50%
• Profile diagnostics — automatic detection of canonicalization failures

[!NOTE] Part of the theaios ecosystem. Install with pip install theaios-trustgate.

Quickstart

pip install theaios-trustgate

from theaios import trustgate

result = trustgate.certify(config_path="trustgate.yaml")
print(result.reliability_level)  # 0.946

The pipeline: sample K responses → canonicalize → calibrate with conformal prediction → get a reliability level with a guarantee. Works with any provider (OpenAI, Anthropic, self-hosted), any task type, fully black-box.

[!TIP] For the full theory, see our paper: Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration (Mouzouni, 2026).

Three Ways to Use TrustGate

1. Deployment gate — certify before shipping

trustgate certify --yes
# Exit code 0 = PASS, 1 = FAIL

     TrustGate Certification Result
┌──────────────────────┬──────────┐
│ Reliability Level    │ 94.6%    │
│ M* (prediction set)  │ 1        │
│ Empirical Coverage   │ 0.956    │
│ Capability Gap       │ 2.4%     │
│ Status               │ PASS     │
└──────────────────────┴──────────┘

2. Runtime trust layer — confidence on every query

from theaios.trustgate import TrustGate, certify

result = certify(config_path="trustgate.yaml")
gate = TrustGate(config=config, certification=result)

# Passthrough (1 API call): attaches reliability metadata
response = gate.query("What is the treatment for X?")
response.reliability_level  # 0.946

# Sampled (K API calls): per-query prediction set
gate = TrustGate(config=config, certification=result, mode="sampled")
response = gate.query("What is the treatment for X?")
response.prediction_set  # ["Aspirin + PCI"]
response.consensus       # 0.8

3. Human calibration — no ground truth needed

Generate a questionnaire, share it with a domain expert, certify with their labels:

trustgate calibrate --export questionnaire.html
# Share via email/Slack → reviewer opens in browser → downloads labels.json
trustgate certify --ground-truth labels.json

Where Do the Questions Come From?

You don't need a gold-standard dataset to use TrustGate. Three ways to get started:

1. Generate questions with AI. Ask an LLM to produce realistic questions for your system:

"Generate 100 realistic customer support questions that users would ask
our e-commerce chatbot, covering orders, returns, shipping, and products."

2. Extract from production logs. Pull real queries from your observability stack (Datadog, Langfuse, LangSmith, custom logs). These are the actual questions your system faces — the most representative test set possible.

3. Use built-in benchmarks. For standard tasks, TrustGate ships dataset loaders:

from theaios.trustgate.datasets import load_gsm8k, load_mmlu
questions = load_mmlu(subjects=["abstract_algebra"], n=100)

No ground truth labels? No problem — use human calibration. A domain expert reviews 50 items in 10 minutes.

→ Full guide: Getting Your Questions

Works With Any Endpoint

LLMs, agents, RAG pipelines — anything with an HTTP API:

# LLM
endpoint:
  url: "https://api.openai.com/v1/chat/completions"
  model: "gpt-4.1"
  api_key_env: "OPENAI_API_KEY"

# Generic agent / RAG / custom API
endpoint:
  url: "https://my-agent.example.com/api/ask"
  temperature: null
  request_template:
    query: "{{question}}"
  response_path: "answer"
  cost_per_request: 0.03

Certify Each Component, Not Just the Final Output

Complex AI systems are pipelines — retriever, reranker, reasoning, generation. Don't certify the whole pipeline as a black box. Certify each component independently to find exactly where reliability breaks down.

Query → [Retriever] → [Reranker] → [Generator] → Answer
            ↑              ↑             ↑
      certify: 94%    certify: 91%   certify: 87%
      "right docs?"  "right order?" "right answer?"

Each component is just an endpoint — TrustGate certifies it independently with its own questions, canonicalization, and reliability level. This lets you:

• Pinpoint failures: the generator is the weak link, not the retriever
• Iterate efficiently: improve one component, re-certify just that one
• Stay agnostic: document changes don't invalidate the retriever certification

Component	Certify on	Canonicalization
RAG retriever	Retrieved document IDs	Exact match
SQL agent	Generated SQL query	Normalized SQL
Classification step	Category label	MCQ
Reasoning step	Intermediate conclusion	LLM-judge or custom
Final answer	Short structured output	Numeric / MCQ

TrustGate warns you automatically when outputs are too long or diverse for meaningful self-consistency measurement.

Pre-flight Cost Estimate

Before spending money, TrustGate shows the cost/reliability tradeoff:

     Cost / Reliability Tradeoff
┌────┬───────────┬──────────┬────────────┐
│  K │ Est. Cost │ Max Cost │ Resolution │
│  3 │ $9.00     │ $18.00   │   coarse   │
│  5 │ $15.00    │ $30.00   │  moderate  │
│ 10←│ $30.00    │ $60.00   │    fine    │
│ 20 │ $60.00    │ $120.00  │    fine    │
└────┴───────────┴──────────┴────────────┘
Proceed? [Y/n]:

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Documentation

• cohorte-ai.github.io/trustgate — Full documentation
• Concepts — How self-consistency + conformal prediction works
• Configuration — Full YAML reference
• CLI Reference — All commands and flags
• Human Calibration — Questionnaire and review UI
• API Reference — Python API

Additional Resources

• Examples — Working certification scripts
• FAQ — Common questions
• Paper — The research behind TrustGate

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Why TrustGate?

• Formal guarantee — conformal coverage bound, not a heuristic score
• Black-box — no model internals, no logprobs, just API access
• Any endpoint — LLMs, agents, RAG, custom APIs
• Human-in-the-loop — shareable questionnaire, no server needed
• Cost-aware — pre-flight estimates, sequential stopping saves ~50%
• Production-ready — passthrough mode, CI/CD gating, periodic recalibration

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Citation

@article{mouzouni2026trustgate,
  title={TrustGate: Black-Box AI Reliability Certification via
         Self-Consistency Sampling and Conformal Calibration},
  author={Mouzouni, Charafeddine},
  year={2026}
}

License

Apache 2.0. See LICENSE.