Measure AI consensus programmatically
When four independently trained models agree on an answer, it's likely correct. When they disagree, it needs human attention. Synero's API gives you this consensus signal as a structured, programmable output.
/api/queryReturns individual model responses plus a synthesis that identifies areas of agreement and disagreement — providing a structured consensus signal.
Capabilities
Consensus Measurement
Four models independently evaluate the same question. The synthesis identifies specific points of agreement and disagreement — not just a binary yes/no, but a nuanced map of consensus.
Decision Automation
Build automated decision trees based on consensus level. High consensus → auto-approve. Partial consensus → route to specialist. No consensus → escalate to human.
Structured Output
The synthesis provides structured agreement/disagreement sections that you can parse programmatically. Build logic around specific consensus signals, not just raw text.
Domain Flexibility
Measure consensus on any question type — factual claims, strategic assessments, technical evaluations, risk analyses, or creative outputs.
Historical Consensus Tracking
Track how consensus shifts over time as models are updated. Identify areas where AI agreement is strengthening (more reliable) or weakening (emerging uncertainty).
Agent Integration
Use the OpenClaw terminal agent skill to integrate consensus checks into agentic workflows. Agents can query the council before making high-stakes decisions.
Code Example
// Use consensus to automate decision quality gates
const result = await fetch('https://synero.ai/api/query', {
method: 'POST',
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
'Content-Type': 'application/json',
},
body: JSON.stringify({
prompt: `Is this claim accurate? "${claim}"
Rate your confidence and identify any caveats or conditions.`,
}),
});
// Parse the synthesis for consensus signals
// Build routing logic:
// - Full consensus → auto-approve with high confidence
// - Partial consensus → approve with caveats noted
// - Disagreement → flag for human review with specific dispute pointsFrequently asked questions
What does 'consensus' mean in this context?
Consensus means that four independently trained AI models, using different architectures and training data, arrive at the same conclusion when given the same question. This is a much stronger signal than a single model's confidence score.
Can I tune the consensus threshold?
Synero returns the full responses from all four models plus the synthesis. You define what 'sufficient consensus' means for your application — whether that's 4/4 agreement, 3/4, or specific conditions in the synthesis.
Is this useful for agentic workflows?
Very. AI agents often need to make decisions autonomously. Using consensus as a quality gate means the agent can proceed confidently when models agree and escalate to a human when they don't — reducing both false confidence and unnecessary escalation.
How does this compare to ensemble methods?
Traditional ensembles average or vote across models. Synero goes further — the synthesis layer actively resolves contradictions, weighs evidence, and produces a coherent answer that explains where and why models agree or disagree.
Add consensus to your AI stack
Four models. Structured agreement signals. Programmable quality gates.
Read the Docs