AgentField provides an open‑source AI backend that lets developers build and deploy AI agents as standalone services, similar to APIs or databases. By running as infrastructure rather than a library, agents can be called from product backends, frontends, or external systems using standard service calls, enabling consistent governance and integration across a stack.
Funding
Funding not disclosed
Founders
Product
Problem
Integrating autonomous AI agents into existing systems often requires embedding specialized libraries in each service, creating operational overhead and limiting scalability. Teams must also build custom infrastructure for routing, state management, identity, and observability, which diverts resources from core product development.
Solution
AgentField offers an open‑source AI backend that functions like a database or message queue, exposing agents through standard service interfaces (REST, webhooks, SSE). By decoupling agent execution from application code, product backends, front‑ends, and external APIs can invoke AI reasoning without adding per‑service libraries. The platform provides a control plane for routing, execution, memory, identity, and audit, while a lightweight runtime runs agents in independently deployed nodes. Support for Python, TypeScript, and Go enables seamless integration across heterogeneous tech stacks, allowing organizations to add autonomous agents to existing infrastructure with minimal code changes.
Target Audience
Primary customers are engineering teams building backend services, fintech platforms, dev‑tools, voice applications, and supply‑chain systems that need to incorporate autonomous AI reasoning without redesigning their existing architecture.
Features
- Service‑oriented API (REST, webhooks, SSE) for invoking agents, similar to traditional data stores
- Centralized control plane handling routing, execution queues, retries, and back‑pressure management
- Built‑in state management, discovery, and streaming capabilities for long‑running or interactive agents
- Identity and governance layer using DIDs, verifiable credentials, and policy enforcement for secure agent actions
- Language SDKs for Python, TypeScript, and Go to register, discover, and call agents from any stack
- Scalable agent fleet architecture where independently deployed nodes register capabilities and run under defined policies
- Observability hooks providing metrics, audit logs, and execution traces for compliance and debugging