Overview
AgentField enables building, running, and scaling AI agents like APIs and microservices with observability, auditability, and identity awareness from day one. It abstracts agent lifecycle, identity, and communication as cloud-native objects so multi-agent applications can run on clusters with scalability and built-in operational controls.
Key Features
- Kubernetes-native scheduling and runtime integration with built-in horizontal scaling support.
- Identity-aware authentication for secure inter-agent communication and fine-grained access control.
- Built-in observability with logs, metrics, and tracing for behavior analysis and troubleshooting.
Use Cases
- Deploying multi-agent workflows as scalable backend services for task distribution and complex orchestration.
- Ensuring secure agent-to-agent communication and auditing in multi-tenant or enterprise environments.
- Combining with RAG and external model services to provide long-running, domain-specific agent services.
Technical Details
- Implements Kubernetes extensions and controller patterns to reduce operational friction.
- Language- and model-agnostic runtime design enabling calls to external LLMs and inference services via APIs.
- Provides observability and authentication integration points compatible with existing cloud-native monitoring and security toolchains.