Production capabilities

AgentField gives agent systems the backend capabilities they need once they move past a demo: APIs, routing, memory, triggers, approvals, rollouts, IAM, audit, observability, and SDKs.

The point is not a longer feature list. The point is that these capabilities come from one operating model instead of a pile of queues, wrappers, databases, cron jobs, dashboards, and policy checks you assemble yourself.

Agent logic becomes backend infrastructure.

Agent APIs

Turn agent logic into callable backend capabilities with one target format:

<node_id>.<reasoner_or_skill>

Every caller uses the same shape whether it calls over HTTP, through an SDK, or from another agent:

POST /api/v1/execute/pricing.quote_enterprise_plan
POST /api/v1/execute/security.triage_alert
POST /api/v1/execute/approvals.review_large_refund
POST /api/v1/execute/vendor_risk.review_vendor

Inside an agent, the same target is used through app.call:

risk = await app.call("risk.score_claim", claim=claim)
approval = await app.call("approvals.review_large_refund", refund=refund)

Agent Services

Agent nodes deploy separately even when they call each other. That is the microservice-shaped part of AgentField: each agent owns a capability, registers with the control plane, and can scale or change independently.

This is the core architecture idea: agents are not buried inside one app. They are independently deployed backend capabilities that can talk to each other.

Agent Discovery

Make the network discoverable instead of wiring every call by hand.

Agent Traffic

Run agents the way backend engineers run services: many calls, long jobs, retries, webhooks, and visible execution state.

This is the difference between "the agent ran" and "the agent is part of production traffic."

Triggers

Agents should not only run when a user clicks a button. They should react to the systems around them.

That turns agents into reactive backend workers, not only request-response handlers.

Safe Rollouts

Reasoning changes are production changes. They need rollout patterns, human gates, and clear lifecycle boundaries.

AgentField does not ask you to treat a new prompt, model, or workflow as a blind deploy.

Agent IAM

AI backends need identity and authorization the same way service backends do. The difference is that the caller may be another agent, the target may be a tool, and the decision may need to be proven later.

The implementation uses DIDs and signed credentials under the hood, but the product idea is simpler: agents get first-class IAM.

Cryptographic Proof

When agents make decisions, logs are not enough. AgentField can produce verifiable records of what ran, who ran it, and how a workflow evolved.

This is one of the biggest differences between an agent app and an AI backend: the backend needs to prove what happened.

Intelligence

AgentField still gives you the core agent-building pieces. They just live inside the backend runtime.

You still get planning, tool use, structured model calls, memory, and orchestration. The difference is that they are wrapped in production controls.

SDK Surfaces

AgentField is meant for real stacks, not one-language demos.

Deployment stays backend-shaped too: local dev, Docker, PostgreSQL, Kubernetes, and cloud platforms are covered in the deployment guide.

What This Replaces

If you build this around a framework yourself, you usually end up owning:

Next: Quickstart to run it, or Building Blocks to see the implementation model.