AgentField is an open-source infrastructure layer designed for building production-grade AI agent systems. Instead of treating agents as application-level components or chatbot frameworks, it positions them as backend services that operate within a governed, observable control plane.

The core idea is to make AI agents behave like standard infrastructure primitives—similar to databases, message queues, or APIs. Developers define agents as services that can be invoked, routed, monitored, and scaled across distributed systems. This allows agents to integrate directly into backend architectures rather than existing as isolated tools or scripts.

AgentField provides a control plane that handles runtime discovery, memory management, streaming execution, and orchestration of multi-agent workflows. It supports DAG-based execution, task queues, auto-retry mechanisms, and long-running stateful operations, enabling agents to run reliably in production environments.

A key part of the system is its identity and governance layer. Each agent has a cryptographic identity, enabling scoped permissions, policy enforcement, and traceable actions. This includes support for decentralized identifiers (DIDs), verifiable credentials, and runtime policy checks that govern what agents can access and execute.

The platform also emphasizes observability and auditability. Every agent action can be logged, traced, and verified, allowing teams to understand how outputs were produced and how different agents contributed to a workflow. This is particularly important for regulated environments where transparency and compliance are required.

AgentField is designed for multi-agent systems where different specialized agents collaborate on complex tasks such as research, code generation, fraud detection, or operational automation. These agents can coordinate through shared infrastructure while maintaining isolated execution contexts and governed access.

Key features include:

• Infrastructure-level AI agent backend (not a framework)
• Stateless control plane for orchestration and routing
• Multi-agent DAG execution with queues and retries
• Runtime discovery, streaming, and memory management
• Cryptographic identity system for agents (DIDs, VCs)
• Policy engine for access control and permissions
• Full observability and audit trails for all agent actions
• Scalable execution for long-running workflows
• SDK support for Python, TypeScript, and Go
• Open-source Apache 2.0 license

Common use cases include:

• Building production-grade multi-agent systems
• Automating backend workflows with AI agents
• Fraud detection and risk analysis pipelines
• Autonomous code review and development pipelines
• Research and data synthesis systems
• Customer support and operational automation
• Agent orchestration in enterprise infrastructure

AgentField is developed as an “AI backend” layer for the agent era, focusing on reliability, governance, and coordination of intelligent systems at scale rather than isolated conversational experiences.