The Lynx Launch package covers the following:
Lynx Launch takes you from a completed proof of concept to Lynx governing the AI agents in a first environment, with your own team able to run it. Over five consecutive days, a named Tigera Solutions Architect works alongside your platform, security and AI teams to install Lynx, discover every AI workload in the cluster, register the agents you choose to allow, and put a default-deny posture in place with explicit policy for the paths your agents need: LLM providers, MCP servers and agent-to-agent calls.
The engagement is built around proof on your own agents, not a demo environment. You will follow a real request end to end in the audit trail, quarantine and release an agent, and apply sandbox rules, so that the controls are understood by the people who will operate them.
Our journey begins with installation and identity, moves through discovery and registration, then policy design and enforcement, and finishes with containment drills and a hand-over to your team and to Tigera support. The same Solutions Architect stays on your account afterwards as your customer success contact.
By the end of the five days, Lynx is live in one cluster, your agent registry is established as the record of allowed agents, and your team can add agents, write policy and read the audit trail without Tigera.
Scope
Prerequisites
Day 1 does not start until the following are in place. The kick-off call checks them.
Schedule resource that has access to configure IdP with Lynx.
Value
Delivery
Step 1: Install
- Introductions, agenda review and confirmation of the prerequisites
- Confirm the estate: cluster, nodes, storage, kernel and OS versions, gateways in the path
- Helm install of the Lynx control plane
- Trace storage: ClickHouse in cluster or bring-your-own
- Configure the identity model: OIDC against your IdP, SPIFFE, or PodOwner
- Configure log export; confirm the control plane is healthy
Step 2: Discover and register
- Deploy the agent detector on every node where agents run
- Review the discovered AI workloads with your platform, security and AI teams
- Create credentials and register the agreed agents, MCP servers and LLM providers
- Produce the inventory of discovered versus registered agents
- Name the registry owner and agree the process for adding new agents
Step 3: Policy
- Confirm the default-deny posture and what it means for unregistered agents
- Which agents may call which LLM providers, MCP servers and other agents
- Cedar policy model and how policies are evaluated
- Write, apply and version the policy set
- Verify refused and permitted calls at runtime
Step 4: Prove
- Prompt an agent and follow the request end to end in the audit trail
- Quarantine and release an agent, including via the API
- Configure and apply sandbox rules
- Token budgets and spend controls
Step 5: Enable and hand over
- Registering a new agent end to end, writing policy, reading the audit trail, day-two operations
- Runbook walkthrough; support portal access and escalation path confirmed
- Close-out deck and sign-off; account live in Tigera support
