Agentfield is an infrastructure for autonomous software that treats intelligent agents like microservices. It provides a control plane for orchestration, memory, identity, workflows, observability, and an API gateway, designed to support distributed, observable, and governable AI agents in production environments. (Ref: page 3, "What is Agentfield?")

How does Agentfield ensure trust and auditability for AI agents?

Agentfield automatically provides cryptographic Decentralized Identifiers (DIDs) for every component and generates Verifiable Credentials (VCs) for every execution. This creates tamper-proof audit trails that can be verified offline, ensuring non-repudiation and regulatory compliance without requiring developers to write crypto code. (Ref: page 5, "Identity & Trust")

What programming languages and SDKs does Agentfield support?

Agentfield provides SDKs for Python, TypeScript, and Go, allowing developers to build agents using their preferred language. (Ref: page 1, "Python SDK", "TypeScript SDK", "Go SDK")

Can Agentfield handle long-running AI workflows?

Yes, Agentfield is designed for multi-hour autonomous workflows with no timeout limits, unlike traditional serverless frameworks. This makes it suitable for tasks like document processing, batch analysis, or complex orchestrations that can run for hours or days. (Ref: page 1, "Multi-Hour Autonomous Workflows")

How does Agentfield facilitate inter-agent communication and coordination?

Agentfield enables secure, server-side inter-agent communication via a simple `ctx.call`, where the control plane automatically verifies the caller's identity (DID) and records the interaction. It also provides automatic service discovery, routing, load balancing, and a zero-config shared memory fabric for seamless coordination. (Ref: page 1, "Secure Inter-Agent Communication", "Parallel Multi-Agent Coordination", "Zero-Config Coordination"; page 6, "Why agent coordination through the control plane matters")

Is Agentfield open source?

Yes, Agentfield is an open-source project, licensed under Apache 2.0, fostering community-driven innovation for autonomous software. (Ref: page 1, "Agentfield Open-Source"; page 3, "Open source, Apache 2.0")

What are the deployment options for Agentfield?

Agentfield supports various deployment topologies, including local development (using `af server`), Docker Compose for isolated testing, and Kubernetes for production-grade orchestration with horizontal scaling and health checks. It can be deployed on any cloud platform that runs containers, such as Railway, Render, Fly.io, AWS, GCP, or Azure. (Ref: page 6, "Deployment Topologies")

What AI models or providers does Agentfield integrate with?

Agentfield integrates with a wide range of Large Language Model (LLM) providers, including OpenAI, Anthropic, Google, Meta, Mistral, Cohere, DeepSeek, Perplexity, Groq, OpenRouter, together.ai, and HuggingFace. (Ref: page 1, "View All Features" section with LLM provider logos)

What is the difference between synchronous and asynchronous execution in Agentfield?

Agentfield supports both synchronous and asynchronous execution modes. Synchronous execution (e.g., via `/execute` endpoint) provides real-time responses for immediate results, suitable for user-facing APIs. Asynchronous execution (e.g., via `/execute/async` endpoint) is designed for long-running tasks, batch processing, or event-driven architectures, returning an immediate 202 Accepted response and delivering results via webhooks upon completion. (Ref: page 6, "How a Request Flows", "Synchronous Execution", "Asynchronous Execution")

Agentfield: Open-Source Kubernetes & IAM for AI Agents

Agentfield Overview

Agentfield is a cutting-edge, open-source control plane that revolutionizes how developers build and deploy autonomous AI agents. Moving beyond traditional agent frameworks, Agentfield treats AI agents like microservices, offering a complete backend infrastructure that is scalable, observable, and inherently identity-aware from day one. It eliminates the need for complex glue code and manual routing, providing a robust foundation for the next generation of intelligent software systems.

How to use Agentfield

To use Agentfield, developers begin by installing its command-line interface (CLI) and language-specific SDKs (Python, TypeScript, Go). After installation, an agent can be initialized using `af init` and the control plane started with `af server` in a separate terminal. Agents are defined as typed functions (e.g., Python's `@app.reasoner()`) that automatically expose REST API endpoints. Developers can then test their agents using simple `curl` commands. To enable AI-powered capabilities, API keys for supported Large Language Model (LLM) providers (like OpenAI or Anthropic) are configured, allowing agents to leverage `app.ai()` for structured, intelligent reasoning within defined schemas. Agentfield handles the underlying orchestration, memory, and identity management, allowing developers to focus on agent logic.

Core Features of Agentfield

Kubernetes-like Orchestration for AI Agents
Built-in IAM and Cryptographic Decentralized Identity (DIDs) for agents
Tamper-Proof Verifiable Credentials (VCs) for every execution, ensuring auditability
Auto-Generated REST API Endpoints with OpenAPI 3.0 specifications
Independent Agent Deployment with Automatic Service Discovery and Load Balancing
Production-Ready Operations including Health Probes, Async Webhooks, and Auto-Retry mechanisms
Live Progress Tracking via Server-Sent Events (SSE) for multi-hour workflows
Secure Inter-Agent Communication and Parallel Multi-Agent Coordination
Multi-Hour Autonomous Workflows with no timeout limits for complex tasks
Guided AI Autonomy using schema validation for predictable outputs
Zero-Config Shared Memory and Built-in Vector Search capabilities
Extensive LLM Provider Integration (OpenAI, Anthropic, Google, Meta, Mistral, Cohere, DeepSeek, Perplexity, Groq, OpenRouter, together.ai, HuggingFace)
Open Source under the Apache 2.0 license

Use Cases for Agentfield

Agentfield is ideal for scenarios requiring robust, scalable, and auditable autonomous software. It's perfectly suited for building complex multi-agent systems in regulated industries like finance, where cryptographic proof of decisions (e.g., loan approvals, fraud detection) is critical for compliance. Developers can leverage it for long-running data processing, batch analysis, or intricate workflow orchestrations that exceed typical serverless function limits. It enables the creation of intelligent microservices that can be independently deployed and scaled, making it invaluable for enterprises integrating AI into their core operations, customer support, or analytics pipelines.

Advantages of Agentfield

Agentfield offers significant advantages over traditional frameworks, primarily by providing a complete production backend out-of-the-box. Its core strength lies in cryptographic trust, automatically generating Decentralized Identities (DIDs) for agents and Verifiable Credentials (VCs) for every action, ensuring tamper-proof audit trails and non-repudiation for compliance and security. It simplifies infrastructure management by handling orchestration, shared memory, and observability, allowing developers to focus purely on agent logic. The architecture is designed for horizontal scalability, supporting independent agent deployments and long-running asynchronous workflows without timeouts. Being open-source, it fosters community-driven innovation and offers flexibility, making it a cost-effective and reliable choice for building enterprise-grade autonomous software.

Agentfield FAQ

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