Source trust for AI agents
Your agent has no idea where it's reading from.
One suspicious URL. One typosquat domain. One weak source your user trusts because your agent trusted it first.
One suspicious URL. One typosquat domain. One weak source your user trusts because your agent trusted it first.
Entropy0 scores each external source across Trust, Threat, and Deviation signals — before it enters the pipeline. One call. Deterministic. No LLM in the loop.
150 free lookups/month. No API key needed to try the playground.
Pre-ingestion source trust for agents, RAG pipelines, and automated workflows.
150 free lookups/month. No API key needed to try the playground.
Blind retrieval is the attack surface
Every agent that fetches external content without checking the source first is running this loop. A manipulated source in context can alter what your agent cites, summarizes, and does next.
Agent receives task
Finds a candidate source
Fetches content — no gate
Content added to context
LLM reasons over it
Agent acts
Spoofed, suspicious, or low-quality sources reach your LLM unchecked.
Agent receives task
Finds a candidate source
POST /v1/decide — source evaluated
proceed / sandbox / escalate / deny
Only cleared sources enter context
LLM reasons over it
Agent acts
Untrusted sources are stopped before context admission.
Source trust, evidence usability, structural deviation — kept separate.
No collapsed verdict. Three independent signals with confidence and coverage per dimension, plus longitudinal state memory across scans.
DNS consistency, SSL validity, WHOIS age, registrar context, and network hygiene — consolidated into a single structural integrity score.
TLD risk, phishing keywords, domain age, brand impersonation, deceptive content patterns, and corroboration from external intel feeds.
How far this domain sits from typical infrastructure patterns, scored against heavy-tail distribution baselines.
One call between retrieval and trust
Drop Entropy0 into any agent loop or RAG pipeline. Evaluate before you fetch, before you synthesize, before you act.
// Check the source before it enters model context const res = await fetch( "https://entropy0.ai/api/v1/decide", { method: "POST", headers: { "X-API-Key": "sk_ent0_xxxx" }, body: JSON.stringify({ target: { type: "domain", value: sourceDomain }, interaction: { kind: "fetch", sensitivity: "medium" }, policy: { profile: "balanced" }, }), } ).then(r => r.json()); const { recommended_action } = res.decision; if (recommended_action === "proceed") { ragContext.push(await fetch(url)); }
{ "target": { "value": "source-docs-api.io" }, "decision": { "recommended_action": "proceed_with_caution", "confidence": 0.91, "reasons": [ "domain_registered_under_90d", "ssl_cert_recently_issued", "no_prior_observations" ] }, "scores": { "trust": 61, "threat": 28, "deviation": 44 }, "temporal_state": { "observation_count": 1, "cohort_match_percentile": null, "plain_english_summary": "First observation — no baseline yet." } }
The same principle — applied to context, not access.
Traditional Zero Trust changed cybersecurity by removing implicit trust from networks and access decisions.
Entropy0 applies the same principle to AI agents and RAG systems.
Agents constantly pull in external URLs, documents, code, API responses, and web content. That content should not be trusted just because it came from a known domain.
Removes implicit trust from networks and access decisions. Never trust, always verify — users, devices, identity.
Removes implicit trust from the content that enters AI agents. Never trust external content by default — verify before it reaches context.
Traditional Zero Trust protects access.
Entropy0 protects context.
Trust is not a snapshot. It is behavior over time.
Designed for platforms that require explainable decisions
Context layer for AI
Your agent fetches a URL. Entropy0 scores it before your model trusts it. One API call between retrieval and synthesis — structured action guidance, not a heuristic guess.
Longitudinal memory
Entropy0 tracks domain state across scans. A domain seen 50 times with a stable clean record is structurally different from one seen once — that difference is in the response.
Uncertainty is visible
Coverage and confidence are first-class outputs, not footnotes. Missing data is reported as a signal gap, not a clean pass.
Built for accountability
Every score is tied to observable evidence. Outputs include bounded validity windows, confidence levels, and human escalation signals — auditable and defensible.
Start with 150 free lookups.
No credit card. API key in 30 seconds. Works with any HTTP client, LangChain, Express, or MCP.