What one agent knows, another shouldn't.
In multi-agent pipelines, sensitive context accumulates across steps — customer records, internal configs, strategic data — and flows between agents as shared memory or inter-task payloads. No gateway sees this traffic. Privent reads the full session state at every agent boundary and controls what crosses.
Context accumulates across agent steps
A researcher agent retrieves customer records. An analyst agent queries financial projections. An executor agent receives both as context. By step 3, the prompt contains data from multiple sensitive sources — assembled automatically.
Privent reads state at every agent boundary
At each inter-agent handoff, Privent reads the full payload — shared memory, task outputs, tool call results, and accumulated context. This is the only point where the combined sensitive content is visible before it crosses to the next agent.
Sensitive fields masked at the boundary
APE identifies which fields in the inter-agent payload are sensitive and masks or fragments them before the next agent receives the context. The downstream agent continues with a safe, coherent version of the accumulated state.
Teams building agent workflows where multiple specialized agents collaborate and exchange state — especially in CrewAI, LangGraph, or custom multi-agent architectures.
Security professionals designing trust boundaries in agentic systems who need enforcement at the agent handoff layer, not just at the LLM gateway.
Product teams deploying internal AI assistants that chain multiple agents across different data domains, where data isolation between roles matters.
We integrate in under 30 minutes. No orchestration changes required. Your pipelines keep running — Privent keeps watching.
