Agentfield isn't a framework you extend with infrastructure. It is the infrastructure.

Every feature you'd spend months building—durable workflows, async execution, webhooks, observability, identity—ships working. Not as configuration options. Not as plugins to install. Built-in and running from day one.

This is what separates prototypes from production systems.

The Complete Infrastructure Stack

Production Challenges for AI Agents

When you deploy AI agents at scale, you face challenges that traditional microservices don't have. Agentfield treats these as infrastructure problems, not application concerns.

Challenge 1: Non-Deterministic Execution Times

The Problem:

AI calls are unpredictable. A sentiment analysis might take 200ms. A document summarization might take 30 seconds. A deep research task might take 10 minutes.

You can't block HTTP requests for 10 minutes. You can't tie up worker threads waiting for AI. Traditional request-response patterns break.

How Agentfield Solves It:

Agentfield provides async execution with push (webhooks) and pull (polling) patterns—choose based on your environment.

Async Execution Pattern:

# Queue a long-running task
curl -X POST http://localhost:8080/api/v1/execute/async/data-processor.analyze_dataset \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "dataset_id": "large-dataset-123"
    },
    "webhook": {
      "url": "https://your-app.com/agentfield/callback",
      "secret": "your-webhook-secret"
    }
  }'

Immediate Response (202 Accepted):

{
  "execution_id": "exec-async-456",
  "workflow_id": "wf-789",
  "status": "queued",
  "target": "data-processor.analyze_dataset",
  "created_at": "2024-07-08T18:20:00Z",
  "webhook_registered": true
}

Queue Features:

• Lease-based processing: PostgreSQL FOR UPDATE SKIP LOCKED prevents duplicate execution
• Backpressure controls: Queue depth limits prevent resource exhaustion
• Fair scheduling: Prevents one workflow from monopolizing workers
• Automatic retries: Failed executions retry with exponential backoff

What This Enables:

• Frontend apps don't timeout waiting for AI
• Event-driven architectures with reliable callbacks
• Background processing for heavy workloads
• Choose push (webhooks) or pull (polling) based on your infrastructure

Challenge 2: Multi-Agent Coordination

The Problem:

Production AI systems aren't single agents. They're distributed systems where agents call each other:

• Customer support agent → sentiment analyzer → escalation handler → notification service
• Research agent → web scraper → summarizer → fact checker → report generator

Each agent is a separate microservice, potentially owned by different teams, deployed independently. How do they discover each other? How do you track execution across agents? How do you debug when something fails in a 5-agent chain?

How Agentfield Solves It:

Agentfield provides service discovery, execution context propagation, and automatic workflow tracking—treating agents like microservices.

Multi-Agent Workflow:

@app.reasoner()
async def handle_support_ticket(ticket: dict) -> dict:
    # Call sentiment agent (different team, different deployment)
    sentiment = await app.call(
        "sentiment-agent.analyze_message",
        text=ticket['message']
    )

    # Call knowledge base (different service)
    solutions = await app.call(
        "knowledge-base.search_solutions",
        category=ticket['category'],
        sentiment=sentiment['score']
    )

    # If high urgency, escalate (another agent)
    if sentiment['urgency'] == 'high':
        await app.call(
            "escalation-agent.create_case",
            ticket_id=ticket['id'],
            priority="urgent"
        )

    return {"solutions": solutions, "sentiment": sentiment}

What Agentfield Does Automatically:

• Service Discovery: Finds sentiment-agent without hardcoded URLs
• Context Propagation: All child calls share the same workflow ID
• Execution Tracking: Builds complete DAG showing agent-to-agent calls
• Error Handling: Failed calls bubble up with full context

Query the complete workflow:

curl http://localhost:8080/api/v1/workflows/wf-abc123

What This Enables:

• Teams deploy agents independently without coordination
• Debug multi-agent flows with visual DAGs
• Trace execution across distributed services
• Understand "which agent called which agent, and why"

Challenge 3: Operational Visibility

The Problem:

When an AI agent makes a decision, you need to know:

• What happened: Which agents executed, in what order
• Why it happened: What reasoning led to each decision
• How long it took: Performance bottlenecks in multi-agent chains
• What went wrong: Root cause when workflows fail

Traditional logging doesn't cut it. You need structured observability for autonomous systems.

How Agentfield Solves It:

Agentfield provides agent notes, structured logging, and automatic metrics—making AI decisions transparent.

Agent Notes for Reasoning Transparency:

@app.reasoner()
async def evaluate_loan_application(application: dict) -> dict:
    # AI analysis
    risk = await app.ai(
        "Evaluate loan risk",
        f"Application: {application}",
        schema=RiskAssessment
    )

    # Emit structured note for audit trail
    app.note(f"""
    ## Risk Assessment

    **Application ID:** {application['id']}
    **Risk Score:** {risk.score}/10
    **Decision:** {'APPROVED' if risk.score < 7 else 'REJECTED'}

    **AI Reasoning:** {risk.reasoning}

    **Factors Considered:**
    - Credit history: {risk.credit_factor}
    - Income stability: {risk.income_factor}
    - Debt ratio: {risk.debt_factor}
    """, tags=["loan-decision", "risk-assessment"])

    return risk.dict()

Prometheus Metrics (Automatic):

# Track execution patterns
agentfield_executions_total{agent="loan-processor", status="completed"} 1247
agentfield_execution_duration_seconds{agent="loan-processor"} 2.3

# Monitor queue health
agentfield_queue_depth{priority="normal"} 45
agentfield_queue_processing_time_seconds 1.8

# Agent health
agentfield_agent_status{agent="loan-processor", status="healthy"} 1

What This Enables:

• Understand AI reasoning without reading code
• Debug production incidents with complete context
• Monitor performance across distributed agents
• Alert on anomalies (queue depth, execution time, error rates)

Challenge 4: Compliance & Auditability

The Problem:

When AI agents make business decisions, regulators ask:

• "Prove this agent made this decision with these inputs"
• "Show me the complete audit trail for this loan approval"
• "How do I know this execution record wasn't tampered with?"

Logs can be edited. Screenshots can be faked. You need cryptographic proof.

How Agentfield Solves It:

Every execution can be tied to a cryptographic identity (DID). When VC generation is enabled you also get verifiable credentials (VCs) for tamper-proof audits.

Identity & Verification (Opt-In):

if app.vc_generator:
    app.vc_generator.set_enabled(True)

@app.reasoner()
async def approve_loan(application: dict) -> LoanDecision:
    # Your business logic
    decision = await app.ai(
        "Make loan decision",
        f"Application: {application}",
        schema=LoanDecision
    )

    # The SDK now submits POST /api/v1/execution/vc after each run, producing a VC
    # that references the reasoner's DID and hashed inputs/outputs.
    return decision

Export for Auditors:

# Download the VC chain for a workflow
curl http://localhost:8080/api/v1/did/workflow/wf-abc123/vc-chain > audit.json

Auditor Verifies Offline:

agentfield verify audit.json

Output:

✓ Workflow: wf-abc123
✓ 5 executions verified
✓ All signatures valid
✓ No tampering detected
✓ Complete provenance chain

Execution Chain:
  1. loan-processor.approve_loan (DID: did:key:z6Mk...)
     ├─ Input hash: sha256:a1b2c3...
     ├─ Output hash: sha256:d4e5f6...
     └─ Signature: valid ✓

What This Enables:

• Pass regulatory audits with cryptographic proof
• Non-repudiation (agents can't deny their actions)
• Offline verification (auditors don't need your systems)
• Complete provenance for every AI decision

Challenge 5: Resource Management at Scale

The Problem:

In multi-tenant systems, one agent shouldn't monopolize resources:

• Marketing's batch job shouldn't starve Support's real-time tickets
• One customer's heavy workflow shouldn't impact others
• Failed executions shouldn't retry infinitely

You need fair scheduling, backpressure controls, and resource isolation.

How Agentfield Solves It:

Production-grade queue management with fairness guarantees and automatic retries.

Fair Scheduling:

• Lease-based processing prevents duplicate execution
• Queue depth limits prevent resource exhaustion
• Per-workflow rate limiting prevents monopolization
• Automatic retries with exponential backoff

**Webhook Architecture

Agentfield delivers completion callbacks reliably with cryptographic verification.

Webhook Configuration:

curl -X POST http://localhost:8080/api/v1/execute/async/report-generator.create_report \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "report_type": "monthly_summary"
    },
    "webhook": {
      "url": "https://your-app.com/agentfield/callback",
      "secret": "your-webhook-secret",
      "headers": {
        "X-Custom-Header": "value"
      }
    }
  }'

Heads up: The current control plane stores webhook metadata but does not dispatch HTTP callbacks yet. Poll the execution status APIs until delivery support ships.

Planned Webhook Payload (for future releases):

{
  "event": "execution.completed",
  "execution_id": "exec-123",
  "workflow_id": "wf-456",
  "status": "completed",
  "target": "report-generator.create_report",
  "result": {
    "report_url": "https://storage.example.com/report.pdf"
  },
  "duration_ms": 45000,
  "timestamp": "2024-07-08T18:21:00Z"
}

Headers:

• X-Agentfield-Signature (not emitted yet) will carry an HMAC-SHA256 signature once webhook delivery is enabled
• Any custom headers you specify in the request

Retry Behavior: Not implemented yet—retry attempts and delivery status tracking will ship with webhook dispatch.

Observability & Monitoring

Agentfield makes autonomous software observable by default.

Health Checks & Resilience

Agentfield agents are Kubernetes-ready with built-in health endpoints.

Health Check Endpoints:

# Liveness probe
curl http://localhost:8001/health/live

# Readiness probe
curl http://localhost:8001/health/ready

Kubernetes Configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: customer-support-agent
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: agent
        image: your-registry/customer-support:latest
        ports:
        - containerPort: 8001
        livenessProbe:
          httpGet:
            path: /health/live
            port: 8001
          initialDelaySeconds: 10
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /health/ready
            port: 8001
          initialDelaySeconds: 5
          periodSeconds: 10

Graceful Shutdown:

When an agent receives SIGTERM:

1. Stops accepting new executions
2. Completes in-flight work
3. Deregisters from control plane
4. Exits cleanly

Docker & Kubernetes Deployment

Agentfield agents deploy like any other containerized service.

version: '3.8'
services:
  af-server:
    image: agentfield/server:latest
    ports:
      - "8080:8080"
    environment:
      - DATABASE_URL=postgresql://user:pass@db:5432/agentfield
    depends_on:
      - db

  customer-support-agent:
    build: ./agents/customer-support
    environment:
      - AGENTFIELD_SERVER=http://af-server:8080
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    depends_on:
      - af-server

  db:
    image: postgres:15
    environment:
      - POSTGRES_DB=agentfield
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=pass
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  postgres_data:

# Agentfield Control Plane
apiVersion: apps/v1
kind: Deployment
metadata:
  name: agentfield-control-plane
spec:
  replicas: 3
  selector:
    matchLabels:
      app: af-server
  template:
    metadata:
      labels:
        app: af-server
    spec:
      containers:
      - name: af-server
        image: agentfield/server:latest
        ports:
        - containerPort: 8080
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: agentfield-secrets
              key: database-url

---
# Agent Deployment (scales independently)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: customer-support-agent
spec:
  replicas: 5
  selector:
    matchLabels:
      app: customer-support
  template:
    metadata:
      labels:
        app: customer-support
    spec:
      containers:
      - name: agent
        image: your-registry/customer-support:latest
        env:
        - name: AGENTFIELD_SERVER
          value: "http://af-server:8080"

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

# Cloud Run expects PORT environment variable
CMD python main.py --port $PORT

gcloud run deploy customer-support-agent \
  --source . \
  --region us-central1 \
  --set-env-vars AGENTFIELD_SERVER=https://agentfield.your-domain.com

Why This Matters

The Bottom Line

Traditional frameworks make you build infrastructure. Agentfield is infrastructure.

You write agents. Agentfield handles:

• Durable workflows that survive crashes
• Async execution with reliable webhooks
• Automatic observability and monitoring
• Production-grade health checks
• Kubernetes-ready deployment
• Cryptographic identity and verification

This is what production-ready means.

Next Steps:

• Core Architecture → - Understand how it all works
• Quick Start → - Build your first production-ready agent
• Deployment Guide → - Deploy to production