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Agent-Field/agentfield
Build, run and scale AI agents like API and microservices - observable,auditable and identity-aware from day one.
AgentField is a Go control plane that turns Python/Go/TypeScript functions into production-grade REST endpoints, handling fan-out, queuing, retries, and tracing across fleets of AI agents. It sits between your agent logic and your infrastructure — you write plain functions, it handles the distributed execution plumbing. The target is teams running multi-agent workflows at scale who would otherwise build their own queue/retry/observability layer.
The PostgreSQL-backed durable queue with lease-based processing is the right call — it avoids introducing a separate broker dependency and survives crashes cleanly. The cryptographic identity model (W3C DID + Ed25519 per agent, signed cross-agent calls, offline-verifiable execution receipts) is genuinely more principled than the shared-API-key approach most similar tools take. The 100–200ms per-hop overhead is disclosed honestly in the README, which is refreshing. Test coverage looks solid — the internal handlers directory alone has 50+ test files, including invariant and isolation tests that suggest real investment in correctness.
The 100–200ms routing overhead per hop is a real problem for deeply recursive workflows — the deep-research example claims 10,000+ agent invocations, and at 200ms each that's 2000 seconds of pure routing latency in the critical path if they're sequential. The control plane is stateless Go but the single PostgreSQL queue is the obvious chokepoint at serious scale, and there's no mention of how that shards or degrades. The 'build a production multi-agent backend with one prompt' pitch is aspirational marketing — the actual scaffolding generates a Docker Compose stack, not a running production system. The multi-language SDK story (Python, Go, TypeScript) is breadth-first and the cross-language feature parity is untested in any publicly documented way.