Every agent that makes your teams faster also adds new risks: data leaks, rising costs, and errors that spread.
How many agents are running across your organization? What data are they sending out? What are they costing you? And what happens when one acts on another agent’s flawed output?
When each team governs its own agents, no one sees the full picture.
Join Nagarro’s experts to see how TRICA, our enterprise agent control plane, gives you one layer to secure, monitor and manage agent actions across teams.
It works with your existing AI platforms, security tools and observability stack, so teams can keep building without losing oversight.
What you’ll learn
-
Find shadow AI: how automatic discovery and a central registry record each agent’s owner, purpose and risk tier.
-
Protect sensitive data: how blocking, masking, custom detection patterns and DLP integration reduce the risk of data leaks.
-
Control AI spend: how budgets, usage limits and routing rules manage costs, with spend tracked by team and agent.
-
Check actions before they run: how agent requests and tool calls are evaluated against your policies first.
-
Keep people in control: how approvals through email or Teams, and a kill switch, let people step in when needed.
-
Trace agent activity: how actions are logged, sent to your SIEM and flagged through alerts in Teams, Slack or email.
-
Fit your existing stack: how a vendor-neutral approach works across clouds, models, tools and legacy systems
See TRICA in action
A live demo of how an agent moves through policy checks before execution, and how guardrails, human approvals and kill switches work at runtime. Using coding and customer-service scenarios, we’ll show how to trace usage, tool calls, costs and decisions, and how TRICA connects with your existing identity, security and observability tools. We’ll also cover how a readiness sprint can identify your first high-value use case.
Who should attend
CISOs, Chief AI Officers, AI security and governance leaders, Enterprise architects, Engineering or platform teams building or deploying enterprise AI agents.
