Agenvia sits between your tools and every LLM. It enforces identity-aware access, detects threats in multiple languages, governs agent tool calls, and improves privacy intelligence through federated learning — in one deployable layer.
Reserved for a layered architecture visual with gateway, policy engine, sanitization, LLM connectors, audit traces, and federated pattern learning.
Each layer handles a distinct security concern — identity, detection, policy, transformation, agent governance, and learning. Add only the layers your deployment needs.
User apps, copilots, and AI agents
Identity + Universal Role Engine (role × domain × action tier)
Detection pipeline, policy engine, sanitization, and output guard
Agent runtime, tool governance, and memory protection
LLM connectors, audit traces, and FL with differential privacy
From transformer-powered detection to differential privacy federated learning — each capability is production-deployed and benchmark-validated.
Every prompt is evaluated for intent before reaching your model. Malicious requests are stopped at the gate.
Decisions are made against your defined policies, with a clear reason attached to every outcome.
High-impact actions are assessed before they run. Nothing escalates silently.
Threat patterns that develop across multiple turns are caught — not just single-prompt attacks.
Every enforcement decision is logged, tamper-evident, and exportable for compliance review.
Your deployment gets stronger from signals across the network. No raw data ever leaves your environment.
From identity check to output delivery — five deterministic stages, each logged and auditable, with no sensitive data escaping the trust boundary.
JWT decoded. Role, domain access, and action-tier ceiling checked against the Universal Role Engine. Unauthorized requests are rejected before any processing begins.
SetFit intent classifier scans for sensitive entities. Multilingual injection and jailbreak patterns (EN/FR/ES/DE) are checked. FL-promoted patterns add tenant-learned signals.
Action intent is classified on a 6-tier scale (summarize → bulk). The policy engine applies per-org rules and selects: allow, sanitize, minimize, local-only, or block.
Named entities are replaced, context is minimized, and only the safe outbound prompt reaches the selected LLM. Blocked requests stop here.
Model response is scanned for leakage before delivery. Audit events are written. Qualified patterns enter the FL candidate pool for HMAC-signed aggregation.