Give your AI agents REST endpoints and cryptographic passports

What it does

AgentField wraps agent logic written in Python, Go, or TypeScript and turns it into deployable backend infrastructure. You decorate functions with @app.reasoner() or @app.skill(), and the control plane auto-exposes them as REST endpoints, handles routing between agents, manages memory across four scopes, and suspends execution for human approval when needed. It acts as the operating layer between your LLM calls and the rest of your stack.

The interesting bit

Instead of treating AI like a chatbot wrapper, AgentField treats non-deterministic logic as a software service. Every agent gets a W3C decentralized identifier and a cryptographic signature, so agents authenticate to each other like microservices with mTLS, and every execution produces an offline-verifiable audit trail. That is a lot of governance ceremony for a field that usually ships with zero accountability.

Key highlights

• Multi-language SDKs (Python, Go, TypeScript) with typed structured output via Pydantic or Zod schemas
• Cross-agent discovery and calls routed through the control plane with full tracing
• Built-in memory with KV and vector search across four scopes, claimed to need no Redis
• Async execution with no timeout limits, plus crash-safe human-in-the-loop suspension
• Canary deployments, A/B testing, and blue-green rollouts for agent versions

Caveats

• The README says memory works with “no Redis needed” but never clarifies what actually backs the vector search and KV store
• Harness orchestration dispatches tasks to external coding agents like Claude Code and Codex, but the README is unclear on whether those tools must be installed locally or authenticated separately
• Many governance and deployment features are documented only via outbound links to agentfield.ai, so evaluating them requires leaving the repository

Verdict

If you need to run autonomous agents as long-lived, auditable backend services rather than one-off scripts, AgentField is worth evaluating. If you are still iterating on prompts in a notebook, it is almost certainly overkill.