Governed execution
Protected runtime paths bind organization and governed agent identity, evaluate policy, and return explicit ALLOW / DENY / ASK_HUMAN outcomes before execution.
Zdravo provides persistent context plus a governed execution path for AI agents. The runtime binds identity, evaluates policy, persists explicit decisions, and records evidence so teams can inspect what was allowed, denied, or escalated.
Current enterprise foundation
These are the capabilities represented by the current product and runtime. Enterprise-specific requirements are evaluated separately rather than presented as universally deployed defaults.
Protected runtime paths bind organization and governed agent identity, evaluate policy, and return explicit ALLOW / DENY / ASK_HUMAN outcomes before execution.
The repository-maintained MCP server carries organization and governed identity into protected runtime paths instead of treating MCP as an ungoverned passthrough.
Postgres-backed memory and semantic retrieval keep context outside the model so organizations can control what is stored and retrieved.
Tenant-scoped access controls, agent identity binding, audit records, and governed observations provide the current foundation for team deployments.
Design-partner evaluation
Why now
AI regulation is moving from broad principles toward enforceable requirements around transparency, traceability, documentation, and human oversight. Zdravo is designed around the runtime evidence and control surface that organizations can inspect and evolve as their requirements mature.
Current boundaries
Start with evidence
We can review the agent, MCP or API path, organizational boundary, policy requirements, and evidence you need before discussing broader enterprise terms.
Talk to Zdravo