Most production agent systems hit the same deployment wall: your team needs agents running in different environments—some on-premises for compliance, others in the cloud for scale, some in private VPCs for security. Traditional frameworks force you into a monolith or require extensive infrastructure coordination.

Agentfield's deployment architecture is built for distributed production systems.

The control plane runs stateless—scale it horizontally without coordination. Each agent scales independently—marketing's sentiment analyzer doesn't affect support's ticket router. Agents run anywhere—your cloud, customer's on-prem, isolated VPCs—and the control plane routes everything automatically.

The Deployment Coordination Problem

Scenario: Your support team needs to deploy a critical bug fix Friday at 5pm. Marketing is testing a new campaign analyzer in staging. Analytics is running a data pipeline that can't be interrupted.

Traditional monolith: Everyone waits. Deployment windows. Change approval boards. Cross-team coordination.

Traditional microservices: Better, but now you need service mesh configuration, load balancer updates, service discovery coordination, and infrastructure overhead for each agent.

Agentfield: Support deploys their agent. Marketing deploys theirs. Analytics keeps running. Zero coordination. The control plane discovers new agents via heartbeat and routes automatically.

Agentfield's Deployment Model

From Testing to Production

Testing: Zero Infrastructure

# One command, zero dependencies
af server

Local development uses an embedded database. No PostgreSQL installation needed. No Docker. No configuration. Start coding immediately.

What you get: Hot reload, web UI, full observability—everything production has, just faster iteration.

Production: Add PostgreSQL

# Same binary, point to PostgreSQL
export AGENTFIELD_POSTGRES_URL=postgres://user:pass@db.company.com/agentfield
af server

For production, add PostgreSQL. That's the only infrastructure requirement. Same code, same agents, same API—just swap the database connection.

What changes: The database. What doesn't change: Your agent code, deployment patterns, API contracts.

Production patterns:

• Stateless control plane → Deploy 1 to N instances behind a load balancer
• Independent agent scaling → Each team deploys and scales their agents separately
• Multi-cloud deployment → Control plane in one cloud, agents distributed across others
• Hybrid deployment → Some agents in your cloud, others on-prem for compliance

Deployment guides →

What This Enables

Team Autonomy

Support ships Friday afternoon. Marketing tests new features in staging. Analytics runs overnight jobs. No coordination needed.

The control plane discovers agents automatically via heartbeat registration. Each team deploys on their own schedule. No shared deployment windows. No cross-team approval processes.

Real scenario: Enterprise with 8 agent types across 5 teams. Each team deploys 2-3 times per week independently. Before Agentfield: weekly deployment windows with cross-team coordination. After: deploy whenever ready.

Multi-Cloud and Hybrid Deployment

Healthcare compliance scenario: Patient data must stay on-prem. Analytics workloads run in the cloud for scale.

With Agentfield:

• Control plane runs in AWS (stateless, no patient data)
• Patient-facing agents run on-prem in hospital data center
• Analytics agents run in cloud with anonymized data
• All inter-agent calls route through control plane

The control plane routes requests between environments. Agents don't need direct network access to each other. Data stays where compliance requires it.

Scale to Zero, Scale to Thousands

Cost optimization scenario: Reporting agent processes end-of-day reports. Runs for 30 minutes, idle for 23.5 hours.

Traditional deployment: Keep instance running 24/7. Pay for idle time.

Agentfield serverless pattern: Scale to zero overnight. Control plane queues requests. Auto-scale on demand when reports trigger.

Same pattern works for:

• Scheduled batch jobs (data pipelines, exports)
• Event-driven agents (webhook handlers, notifications)
• Seasonal workloads (tax season, holiday campaigns)

Same Code, Any Environment

# Your agent code - runs identically everywhere
app = Agent("support-agent")

@app.reasoner()
async def triage_ticket(ticket: dict) -> Decision:
    # This code runs on:
    # - Your laptop (testing)
    # - Docker Compose (team staging)
    # - Kubernetes (production)
    # - Customer's on-prem server (compliance)

    return await app.ai(system="...", user=ticket["message"])

What changes between environments: Database connection URL and where agents run. That's it.

Production Patterns Built-In

Features that typically require extensive infrastructure setup are built into the deployment architecture:

Architecture details →

Deployment Guides

Stateless control plane. Independent agent scaling. Distributed deployment.

That's production-ready multi-agent systems.