Technology
AI Governance Control for the
Agents You Build and Ship
Build AI governance into the products you ship and the tools your teams use. Enforce agent permissions, test behavior before release, and record production activity so engineers can investigate failures and demonstrate how controls work.
What Is AI Control for Tech Companies?
AI control is the ability to decide what an AI system may do before it acts, see what it did, and stop it when something goes wrong. For a technology company running agents in production, it borrows from practices your engineers already use:
- Staged Rollout
New limits run in flag mode first, logging what they would block before enforcing. - Blast Radius
Each agent scoped to the tools and data its use case needs, and nothing more. - Runaway Protection
Budget and rate limits that stop a retry loop before it reaches the invoice. - Instant Revocation
Access cut at the gateway the moment a key is deleted or a user leaves a group.
For many technology companies, agents now run inside the product customers pay for. A wrong action is a defect a customer sees, and a leaked credential or a runaway loop is an incident. Your teams already handle defects and incidents with code review, tests, and version control, and AI control should work the same way. Lumenova AI puts controls in the path of each call, written as policy your engineers review, test, and version like the rest of your stack.
Where AI Agents Work in Technology Companies
As agents become part of your product and daily operations, their mistakes can reach code, infrastructure, and customer accounts.
Defined permissions and traceable actions help teams investigate failures and keep access in check as adoption grows.
Agents in Your Product
Features where the agent's action is what the customer bought, so a wrong action is a visible product defect.
Coding Agents
Assistants with repository, terminal, and MCP access, where spend, tool reach, and data exposure need clear, enforceable limits.
Support and Success
Assistants that work inside customer accounts and answer from your documentation across support channels.
Capabilities
How Lumenova AI Gives Engineering Teams AI Control
Build agent controls into your existing stack, test changes before release, and use production traces to diagnose failures, track costs, and review access.
Model and MCP Gateway
Put model providers and MCP servers behind one endpoint, with per-key tool permissions checked before a request reaches the server.
Budgets and Rate Limits
Manage budgets and rate limits by team, key, and user, with each call linked to its agent and approved use case.
CI Evaluations
Make evaluations part of your CI pipeline, with score thresholds that fail the build when agent performance falls below standards.
Pre-Release Red Teaming
Probe agents the way a real user or attacker would, and replay the attacks that worked after each fix.
OpenTelemetry Tracing
Keep agent traces flowing to Datadog, Splunk, and other tools your team already uses through OpenTelemetry.
Audit and Review Records
Decision records and test results give your team, and your independent auditor, evidence rather than screenshots.
An Example: A Coding Agent and an MCP Server
A coding agent
Calls a write tool on an internal MCP server that exposes database tools.
Identify
The gateway resolves the team and use case behind the agent's key.
Check tool permissions
The key allows the server's read tools only.
MCP server
Read tools stay available to the agent.
Refused at the gateway
The call never reaches the server.
Call record
Shows which agent tried, under which use case.
Frequently Asked Questions
AI governance sets the rules: who may build and use AI, for what, and with what oversight. AI control enforces them at runtime, deciding what each agent may do on each call. Lumenova AI covers both, so the policy your risk team approves is the policy your agents run under.
Register the agent and its use case, set its risk tier, then test it four ways: rule checks, quality scoring, a review of its full workflow, and adversarial probes that act like a real attacker. Lumenova AI runs those tests before release and in production and keeps the findings with the use case.
Enforcement adds under a millisecond per call, and agents on OpenAI-compatible clients connect by changing the base URL, with no other code changes. Policies are code your engineers review and test like any other change, so governance moves at your release pace.
Both. The platform comes with the Lumenova AI Forward Deploy Team, who work inside your organization to prove the business case for each agent, tune guardrails, and stand up observability early, filling gaps wherever your team needs them.
The EU AI Act’s transparency duties have applied since August 2, 2026, and its high-risk obligations apply from December 2, 2027. Lumenova AI helps you build the evidence those rules call for: an inventory of AI systems, test results, human oversight, and records of what each system did.