Custom Resources¶

Sympozium models every agentic concept as a Kubernetes Custom Resource:

CRD	Kubernetes Analogy	Purpose
`Agent`	Namespace / Tenant	Per-user gateway — channels, provider config, memory settings, skill bindings
`AgentRun`	Job / Sandbox CR	Single agent execution — task, model, result capture, memory extraction. Optionally uses Agent Sandbox CRDs for kernel-level isolation
`SympoziumPolicy`	NetworkPolicy	Feature and tool gating — what an agent can and cannot do
`SkillPack`	ConfigMap	Portable skill bundles — kubectl, Helm, or custom tools — mounted into agent pods as files, with optional sidecar containers for cluster ops
`SympoziumSchedule`	CronJob	Recurring tasks — heartbeats, sweeps, scheduled runs with cron expressions
`Ensemble`	Helm Chart / Operator Bundle	Pre-configured agent bundles — activating a pack stamps out Agents, Schedules, and memory for each persona
`Model`	Deployment + Service	Cluster-local inference — declares a model (GGUF or HuggingFace), controller deploys an inference server (llama.cpp, vLLM, or TGI) and exposes an OpenAI-compatible endpoint
`MCPServer`	Deployment + Service	Managed Model Context Protocol server — external tool providers with auto-discovery and allow/deny filtering
`SympoziumConfig`	Cluster configuration	Platform-wide singleton — gateway, canary, and pricing settings

Agent¶

The core resource representing an agent identity. Each instance has:

An LLM provider configuration (model, API key reference, base URL)
Skill bindings (which SkillPacks are active)
Channel connections (Telegram, Slack, etc.)
Memory settings (enabled/disabled, max size)
A policy reference
Optional node selector for pinning agent pods to specific nodes (e.g. GPU nodes running Ollama)

apiVersion: sympozium.ai/v1alpha1
kind: Agent
metadata:
  name: my-agent
spec:
  agents:
    default:
      model: gpt-4o
  skills:
    - skillPackRef: k8s-ops
    - skillPackRef: code-review
  policyRef: default-policy

AgentRun¶

Represents a single agent execution. The controller reconciles each AgentRun into an ephemeral Kubernetes Job containing the agent container, IPC bridge, and any skill sidecars.

apiVersion: sympozium.ai/v1alpha1
kind: AgentRun
metadata:
  name: quick-check
spec:
  agentRef: my-agent
  agentId: default
  sessionKey: "quick-check-001"
  task: "How many nodes are in the cluster?"
  model:
    provider: openai
    model: gpt-4o
    authSecretRef: my-openai-key
  skills:
    - skillPackRef: k8s-ops
  timeout: "5m"

Phase transitions: Pending → Running → Succeeded (or Failed). When lifecycle hooks with postRun are defined: Pending → Running → PostRunning → Succeeded (or Failed).

Setting spec.backend: celln routes the run to a hardware-isolated microVM instead of a Job — no ensembles, delegation, or shared memory, and the run's own model: field is ignored in favor of whatever AI provider is configured on the KVM host. See Celln Backend.

SympoziumPolicy¶

Gates features and tools at admission time. The webhook evaluates policies before a pod is created.

Policy	Who it is for	Key rules
Permissive	Dev clusters, demos	All tools allowed, no approval needed
Default	General use	`execute_command` requires approval, everything else allowed
Restrictive	Production, security	All tools denied by default, must be explicitly allowed

SkillPack¶

Portable skill bundles mounted into agent pods as files. Can optionally declare sidecar containers with runtime tools and RBAC rules. See Skills & Sidecars for details.

SympoziumSchedule¶

Cron-based recurring agent runs. See Scheduled Tasks for details.

Ensemble¶

Pre-configured agent bundles. See Ensembles for details.