AgentField is a backend infrastructure layer for autonomous AI that treats AI agents as first-class backend services, making them scalable, observable, and identity-aware from day one.
One-Minute Overview#
AgentField is a control plane for autonomous AI that lets AI agents run as standard microservices. It solves the challenges of scaling, coordinating, and trusting AI as it evolves from chatbots to true backend systems. Ideal for development teams and enterprises building production-grade AI backends, multi-agent coordination systems, and solutions requiring audit trails.
Core Value: Transform AI agents from prototype tools into standardized, production-grade microservice architectures.
Quick Start#
Installation Difficulty: Medium - Requires understanding of the control plane + agent node dual architecture, with SDKs available in three languages (Python, Go, TypeScript)
# Install AgentField
curl -fsSL https://agentfield.ai/install.sh | bash
# Create your first agent
af init my-agent --defaults
cd my-agent && pip install -r requirements.txt
Is this for me?
- ✅ Multi-agent Systems: Scenarios where multiple AI agents need to coordinate and communicate
- ✅ Production AI Backends: Long-running AI decision systems requiring reliability and async execution
- ✅ Enterprise Environments: Regulated industries requiring audit trails and authentication
- ❌ Simple Chatbots: Single-turn conversations without persistence needs
- ❌ Rapid Prototyping: Concept testing phases not considering production readiness
Core Capabilities#
1. Scalable Infrastructure - Solving AI Scaling Challenges#
- Control plane as a stateless Go service for routing, tracking, and orchestration
- Async execution with "fire-and-forget" tasks, webhook results
- Support for tasks running hours or days with durable checkpointing
- Built-in backpressure handling with queuing and circuit breakers Actual Value: Enterprise AI systems no longer limited by synchronous request timeouts, can handle complex long-duration tasks
2. Multi-Agent Native - Solving AI Collaboration Challenges#
- Agent discovery mechanism for registering capabilities and finding each other via API
- Cross-agent calls routed through control plane with full traceability
- Workflow DAGs automatically visualized showing every execution path
- Hierarchical shared memory (global, agent, session, or run level) with vector search Actual Value: Building complex AI ecosystems where specialized agents can collaborate automatically, forming optimal workflows
3. Enterprise-Grade Security & Trust - Solving AI Accountability Challenges#
- Every agent gets a W3C Decentralized Identifier (DID) as cryptographic identity
- Every execution produces Verifiable Credentials - tamper-proof receipts for actions
- Built-in Prometheus metrics monitoring with
/metricsendpoint - Policy-based execution control like "Only agents signed by 'Finance' can access this tool" Actual Value: In regulated industries like finance and healthcare, providing mathematically-provable audit trails ensuring compliance and accountability for AI decisions
Technical Stack & Integration#
Development Languages: Go, Python, TypeScript Main Dependencies: Control plane is a Go service, with language-specific SDKs for agents Integration Method: Control plane provides REST API, agents integrate via SDKs or direct API calls
Maintenance Status#
- Development Activity: Actively developed with regular feature additions and improvements
- Recent Updates: Recent releases include multi-language SDK support and enterprise-grade features
- Community Response: Comprehensive documentation, example code, and GitHub issue tracking
Commercial & Licensing#
License: Not explicitly specified (check official repo for latest license info)
- ✅ Commercial: Generally allowed based on open-source nature
- ✅ Modification: Generally allowed based on open-source nature
- ⚠️ Restrictions: Check official license for specific limitations
Documentation & Learning Resources#
- Documentation Quality: Comprehensive with detailed technical references, architecture explanations, and best practices
- Official Documentation: https://agentfield.ai/docs
- Example Code: Complete examples provided for Python, Go, and TypeScript
- Installation Script: One-click installation script simplifies deployment