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The critical AI infrastructure gap

AI and agentic applications require a context-aware platform

Where you are
Cloud-native infrastructure
Where you need to be
Agentic infrastructure
Security
Where you are
Cloud-native infrastructure

Cloud-native platforms focus on identity and policy for users and workloads with platform RBAC. Agents and tools are considered black box workloads.

Where you are
Cloud-native infrastructure

Agent-native platforms recognize agents and tools as first class workload types and treat agents acting on behalf of users as a distinct identity with the ability to set granular access policies for tools and agents.

Observability
Where you are
Cloud-native infrastructure

Cloud-native platforms provide baseline metrics, logging, and tracing support for workloads, but lack context-awareness for interactions with agents, tools, and LLMs.

Where you are
Cloud-native infrastructure

Agent-native platforms extend OpenTelemetry to provide context-specific access logging, usage metrics, and trace attributes for agents, tools, and LLMs. Context-aware instrumentation for agents and tools ensures that traces are propagated for end-to-end observability.

Connectivity
Where you are
Cloud-native infrastructure

Cloud-native platforms leverage load balancers, gateways, and traffic proxies for HTTP, gRPC, and other Layer 7 protocols without understanding the body or context of a request.

Where you are
Cloud-native infrastructure

Agent-native platforms support context-aware processing of Layer 8 protocols used for LLM consumption, MCP, and A2A with the ability to federate, multiplex, and process traffic with a deep understanding of the context of the request and response.

Self-service
Where you are
Cloud-native infrastructure

Cloud-native platforms view applications as opaque containers that are scheduled, deployed, and managed with no intelligence into the underlying workloads type or function.

Where you are
Cloud-native infrastructure

Agent-native platforms provide a first class API to define, manage, and govern agents, tools, and models used for AI and agentic applications, supporting context-aware policy, routing, failover, and resiliency controls.

Autonomy
Where you are
Cloud-native infrastructure

Cloud-native platforms are traditionally built for deterministic workloads that don’t require additional safeguards to interrupt or deeply observe contextual signals for monitoring application predictability and safety.

Where you are
Cloud-native infrastructure

Agent-native platforms provide the governance controls required to confidently deploy and run probabilistic agents with built-in capabilities to support human-in-the-loop, human-on-the-loop, and real-time streaming of agent activity.

Kagent enterprise supports a full lifecycle framework for building your own agents and integrating with existing agents, while bringing uniform security, observability, and secure sandboxing across all tools your agents access.

Building & running tools

Agents require tools to do anything useful which has fueled a frenzy of MCP tool server development. With thousands of tool servers available in public registries and still more to build for internal services and APIs, connecting critical enterprise data, resources, and services to your agents is one of the biggest challenges in building production-ready agents.

Our enterprise offering of kagent supports a full lifecycle framework for building your own agents and integrating with existing agents, while bringing uniform security, observability, and secure sandboxing across all tools your agents access.

Built on a foundation of open source innovation

The future of AI and agentic infrastructure is being defined in open source communities.
We are proud to not only participate in these communities, but to actively drive that innovation through our key projects within the CNCF and Linux Foundation.

Build your agentic bedrock

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