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About Kagenti

Kagenti is an open source platform for deploying and governing AI agents on Kubernetes. It's framework-neutral — agents built with LangGraph, CrewAI, AG2, AutoGen, or any A2A-compatible framework all run on Kagenti. Communication uses open standards (A2A and MCP) so teams aren't locked into any vendor's stack.

The project started in early 2025 out of IBM Research with deep collaboration from Red Hat, and has grown to 12 repos, 43 org members, and 80+ contributors across both organizations.

Where we're headed

Agents are changing. The tools developers reach for daily — Claude Code, Codex, OpenClaw — run continuously, rewrite their own files, and act without being asked. Kagenti's direction is the secure deployment of autonomous agents on Kubernetes.

The goal is a platform where always-on, self-modifying agents can run on your own infrastructure with real identity, governance, and isolation. Even companies using cloud-hosted agents will have workloads that can't leave their network — agents touching internal databases, proprietary code, secrets, internal APIs. Kagenti is intended to be where those agents run.

The Problem

Frameworks and harnesses for building agents are maturing fast. But there is no widely adopted platform that gives you identity, governance, sandboxing, and audit for agents on Kubernetes — especially autonomous ones that make their own decisions and run unsupervised.

This is the same gap Kubernetes filled for containers a decade ago. Agents are the new workload.

Who it's for

Platform Engineer — "People are deploying agents. I need to govern them." They need to enforce safety policies, scope permissions, audit what agents do, and isolate tenants.

AI Engineer — "I have an agent and I need to run it securely." Their agent needs to touch internal systems that can't leave their network, and cloud platforms can't give them the identity, governance, and audit their company requires.

Differentiating capabilities

What Kagenti is building toward:

Capability What it solves
Workload Identity Cryptographic identity for agents via SPIFFE/SPIRE, not just API keys
Tool Governance Deterministic filtering between agents and external services, with audit trails and human-in-the-loop approval
Guardrails Enforcement Content safety and compliance policies for agents making autonomous decisions
Sandboxing Isolated execution environments for agents running arbitrary code
Authorization & Policy Scoped permissions with runtime policy enforcement
Audit Trail Full record of agent actions for compliance
Workspace Isolation Multi-tenant filesystem isolation for self-modifying agents
State Management Persistent state and session management across restarts
Agent Trust Signed agent cards, attestation of capabilities
Skills Governance Skills as versioned, signed, governed artifacts

What the ecosystem covers today

Area What it does
Developer Tooling ADK — CLI, Python + TypeScript SDKs, and a local dev environment. Build and test agents without a cluster.
Lifecycle Orchestration Deploy agents and tools as containers via AgentCard CRDs. Auto-build with Shipwright. Discovery and registration handled by the operator.
Networking MCP Gateway routes tool calls across agents. Istio service mesh for mTLS. Gateway API for ingress.
Security Zero-trust from the ground up. SPIFFE/SPIRE for cryptographic workload identity. Keycloak for OAuth/OIDC. AuthBridge for token exchange. No static credentials.
Observability Distributed tracing via MLflow, Langflow, and Phoenix. Network visualization through Kiali. Token cost attribution. OpenTelemetry auto-instrumentation.
Security Testing Capture the Flag — red-team scenarios with real AI agents probing for policy violations.
Benchmarking Agent benchmarking and test infrastructure for evaluating agent behavior at scale.

Community

The community ships weekly, publishes on Medium, and has presented at KubeCon NA 2025, KubeCon EU 2026, and The Cloudcast podcast.

See Content for the full list of blogs, demos, and coverage.