ai agent governance
Your AI agent has access to 47 tools.
Who approved that?
Neurelay controls which agents use which tools, with what permissions, under whose authority. Every tool call policy-driven. Every action auditable.
the problem
Your infrastructure is AI-ready.
Your governance isn't.
You spent a decade building APIs, auth layers, and access control. Now AI agents connect to those same systems — and every tool is visible, every tool is callable, with no policy layer in between.
No access control
Every agent sees every tool. A support bot and a billing agent have the same access. There's no concept of scoped permissions.
No audit trail
Which agent called which tool, with what arguments, when? If you can't answer that, you can't pass a compliance review.
No kill switch
Revoking access means reconfiguring every agent. No central control to cut off a compromised credential in seconds.
how it works
Three layers. Default deny.
Define policies
Create blueprints that declare which tools exist and how they're accessed. Attach policies that scope permissions per credential — read, write, execute, or deny.
Filter discovery
When an agent asks "what tools can I use?", it only sees what its policy allows. Unauthorized tools don't appear. The agent doesn't know they exist.
Gate execution
Every tool call passes through the gateway. Validated against policy. Logged with full context — who, what, when, result. Denied if unauthorized.
capabilities
What you get
Blueprint registry
Register your tool servers as blueprints. Define tool schemas, connection details, and metadata in one place. Version and manage your tool catalog.
Policy-based access control
Fine-grained RBAC for AI tools. Policies define which tools a credential can discover and execute. Default deny — nothing is accessible until explicitly granted.
Aggregation gateway
One endpoint for your agents. The gateway connects to multiple tool servers behind the scenes, aggregates tools, and enforces policies at the boundary.
Audit & kill switch
Every tool call logged — credential, tool, arguments, timestamp, result. Revoke a credential or disable a tool instantly from the dashboard. No agent reconfiguration needed.
Smarter tool discovery
More tools in discovery means more tokens burned and more chances the agent picks the wrong one — the hallucination tax. Policy filtering cuts the list. Tuned descriptions help agents choose right. No code changes.
Stage & production environments
Connect tool servers per environment. Test policies and agent behaviour in staging before promoting to production — at the governance layer, not in your dev framework.
Product demo — coming soon
from the blog
Technical depth
Mar 10, 2026
When an SLM Routes Every Request, PII Recall Drops to Zero — Why Layered Architecture Wins for Enterprise AI
A 1.5B model classified credit card numbers as 'not sensitive.' The same model, used only for ambiguous cases behind a deterministic layer, improved routing accuracy to 95%. Right-sizing isn't just about model size — it's about knowing where each layer belongs.
Mar 3, 2026
How We Pushed PII Recall from 76% to 98% — Right-Sized Models, No Fine-Tuning, No LLMs
Statistical NER + five pattern recognizers + one threshold change. No fine-tuning. No GPU. No data leaving your perimeter unmasked. A practical guide to enterprise PII detection under GDPR.
Sep 10, 2025
The Philosophy of Secure AI: Let LLMs Think, Let Tools Execute
For a secure AI native, the real security challenge isn't keeping bad actors out — it's keeping powerful AI from making dangerous moves by accident.
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