1┌─────────────────────────────────────────┐
2│              Your Product               │
3├─────────────────────────────────────────┤
4│  Frontend          │  Backend           │
5│  - User Interface  │  - API Gateway     │
6│  - Real-time UI    │  - Agent Runtime   │
7│  - Settings        │  - Tool Services   │
8│                    │  - Database        │
9├─────────────────────────────────────────┤
10│            Infrastructure               │
11│  - Queues  - Cache  - Storage  - CDN    │
12└─────────────────────────────────────────┘

1# User waits for complete response
2@app.post("/analyze")
3async def analyze(request: AnalyzeRequest):
4    result = agent.run(request.query)
5    return {"result": result}

1# Return job ID immediately
2@app.post("/analyze")
3async def start_analysis(request: AnalyzeRequest):
4    job_id = create_job(request)
5    queue.enqueue(run_agent_job, job_id)
6    return {"job_id": job_id}
7 
8# Poll for results
9@app.get("/jobs/{job_id}")
10async def get_job(job_id: str):
11    job = get_job_status(job_id)
12    return job

1@app.post("/chat/stream")
2async def stream_chat(request: ChatRequest):
3    async def generate():
4        async for chunk in agent.stream(request.message):
5            yield f"data: {json.dumps(chunk)}\n\n"
6 
7    return StreamingResponse(generate(), media_type="text/event-stream")

GitHub Webhook → Queue → Code Review Agent → GitHub API
                              ↓
                    Analysis Storage (for history)

1Build a GitHub code review bot that:
21. Receives webhooks for new PRs
32. Fetches the diff
43. Analyzes code for issues, style, security
54. Posts review comments on GitHub
65. Approves or requests changes
7 
8Include FastAPI backend, Redis queue, and GitHub integration.

Support Channels → Ticket System → Support Agent → Response
      ↓                                   ↓
  (Email, Chat)              Knowledge Base + CRM

1Build a customer support agent that:
21. Receives tickets from multiple channels
32. Searches knowledge base for answers
43. Drafts responses with tone matching
54. Escalates complex issues to humans
65. Tracks resolution metrics
7 
8Include ticket queue, knowledge base RAG, and admin dashboard.

Research Query → Research Agent → Report Generator → Delivery
                      ↓
        Web Search + Document Analysis + Citations

1Build a research assistant product that:
21. Takes research questions from users
32. Searches web and academic sources
43. Synthesizes findings with citations
54. Generates reports in PDF/Markdown
65. Allows users to ask follow-up questions
7 
8Include user accounts, report history, and sharing.

User Question → Data Agent → SQL/Analysis → Visualization
                    ↓
          Database Schema + Query History

1Build a data analysis agent that:
21. Connects to user databases
32. Understands schema automatically
43. Converts questions to SQL queries
54. Generates visualizations
65. Creates shareable dashboards
7 
8Include database connectors, chart library, and permissions.

1Level 1: Simple Chat
2└── User asks, agent responds
3 
4Level 2: Show Thinking
5└── Display agent's reasoning steps
6 
7Level 3: Tool Visibility
8└── Show which tools are being used
9 
10Level 4: Full Control
11└── Let users guide agent decisions

1// Frontend component for agent status
2function AgentStatus({ jobId }) {
3  const [status, setStatus] = useState('starting');
4  const [steps, setSteps] = useState([]);
5 
6  useEffect(() => {
7    const eventSource = new EventSource(`/jobs/${jobId}/stream`);
8 
9    eventSource.onmessage = (event) => {
10      const data = JSON.parse(event.data);
11      setStatus(data.status);
12      setSteps(data.steps);
13    };
14 
15    return () => eventSource.close();
16  }, [jobId]);
17 
18  return (
19    <div>
20      <StatusBadge status={status} />
21      <StepsList steps={steps} />
22      <ProgressBar progress={steps.length / totalSteps} />
23    </div>
24  );
25}

1# Dockerfile for agent service
2FROM python:3.11-slim
3 
4WORKDIR /app
5 
6COPY requirements.txt .
7RUN pip install --no-cache-dir -r requirements.txt
8 
9COPY . .
10 
11CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

1# docker-compose.yml
2version: '3.8'
3 
4services:
5  api:
6    build: .
7    ports:
8      - "8000:8000"
9    environment:
10      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
11      - REDIS_URL=redis://redis:6379
12    depends_on:
13      - redis
14      - postgres
15 
16  worker:
17    build: .
18    command: celery -A tasks worker --loglevel=info
19    environment:
20      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
21      - REDIS_URL=redis://redis:6379
22 
23  redis:
24    image: redis:7-alpine
25 
26  postgres:
27    image: postgres:15
28    volumes:
29      - postgres_data:/var/lib/postgresql/data

1# AWS Lambda handler
2def handler(event, context):
3    body = json.loads(event['body'])
4    result = agent.run(body['query'])
5 
6    return {
7        'statusCode': 200,
8        'body': json.dumps({'result': result})
9    }

1# k8s deployment
2apiVersion: apps/v1
3kind: Deployment
4metadata:
5  name: agent-service
6spec:
7  replicas: 3
8  selector:
9    matchLabels:
10      app: agent-service
11  template:
12    spec:
13      containers:
14      - name: agent
15        image: your-registry/agent:latest
16        resources:
17          requests:
18            memory: "512Mi"
19            cpu: "500m"
20          limits:
21            memory: "1Gi"
22            cpu: "1000m"
23        env:
24        - name: ANTHROPIC_API_KEY
25          valueFrom:
26            secretKeyRef:
27              name: api-keys
28              key: anthropic

1import structlog
2 
3logger = structlog.get_logger()
4 
5def run_agent_with_logging(user_id: str, task: str):
6    log = logger.bind(user_id=user_id, task_id=str(uuid4()))
7 
8    log.info("agent_started", task=task)
9 
10    try:
11        start = time.time()
12        result = agent.run(task)
13        duration = time.time() - start
14 
15        log.info("agent_completed",
16                 duration=duration,
17                 tokens_used=result.tokens,
18                 tools_used=result.tools)
19 
20        return result
21 
22    except Exception as e:
23        log.error("agent_failed", error=str(e))
24        raise

1from opentelemetry import trace
2 
3tracer = trace.get_tracer(__name__)
4 
5def run_agent(task: str):
6    with tracer.start_as_current_span("agent_run") as span:
7        span.set_attribute("task", task)
8 
9        # Agent loop
10        for i, step in enumerate(agent_steps):
11            with tracer.start_as_current_span(f"step_{i}") as step_span:
12                step_span.set_attribute("tool", step.tool)
13                result = execute_step(step)
14                step_span.set_attribute("result_size", len(result))

1# Prometheus alert rules
2groups:
3- name: agent-alerts
4  rules:
5  - alert: HighAgentLatency
6    expr: histogram_quantile(0.95, agent_duration_seconds) > 30
7    for: 5m
8    labels:
9      severity: warning
10    annotations:
11      summary: "Agent latency is high"
12 
13  - alert: HighToolFailureRate
14    expr: rate(tool_failures_total[5m]) / rate(tool_calls_total[5m]) > 0.05
15    for: 5m
16    labels:
17      severity: critical

1class BudgetedAgent:
2    def __init__(self, max_tokens: int = 100000):
3        self.max_tokens = max_tokens
4        self.tokens_used = 0
5 
6    def run(self, task: str) -> str:
7        if self.tokens_used >= self.max_tokens:
8            raise BudgetExceededError("Token budget exceeded")
9 
10        response = self._call_api(task)
11        self.tokens_used += response.usage.total_tokens
12 
13        return response

1import hashlib
2from functools import lru_cache
3 
4class CachedAgent:
5    def __init__(self):
6        self.cache = {}
7 
8    def run(self, task: str) -> str:
9        cache_key = hashlib.md5(task.encode()).hexdigest()
10 
11        if cache_key in self.cache:
12            return self.cache[cache_key]
13 
14        result = self._run_agent(task)
15        self.cache[cache_key] = result
16 
17        return result

1TIER_LIMITS = {
2    "free": {"tokens_per_day": 10000, "tasks_per_day": 10},
3    "pro": {"tokens_per_day": 100000, "tasks_per_day": 100},
4    "enterprise": {"tokens_per_day": None, "tasks_per_day": None}
5}
6 
7def check_usage(user_id: str, tier: str) -> bool:
8    usage = get_daily_usage(user_id)
9    limits = TIER_LIMITS[tier]
10 
11    if limits["tokens_per_day"] and usage.tokens > limits["tokens_per_day"]:
12        return False
13    if limits["tasks_per_day"] and usage.tasks > limits["tasks_per_day"]:
14        return False
15 
16    return True

1from pydantic import BaseModel, validator
2 
3class AgentRequest(BaseModel):
4    task: str
5    max_tokens: int = 4096
6 
7    @validator('task')
8    def validate_task(cls, v):
9        if len(v) > 10000:
10            raise ValueError("Task too long")
11        if contains_pii(v):
12            raise ValueError("Please remove personal information")
13        return v
14 
15    @validator('max_tokens')
16    def validate_tokens(cls, v):
17        if v > 100000:
18            raise ValueError("Token limit too high")
19        return v

1def filter_output(response: str) -> str:
2    """Filter agent output before returning to user."""
3 
4    # Remove any leaked system information
5    response = remove_system_paths(response)
6 
7    # Redact sensitive patterns
8    response = redact_secrets(response)
9 
10    # Check for harmful content
11    if contains_harmful_content(response):
12        return "I cannot provide that information."
13 
14    return response

1SENSITIVE_ACTIONS = ["delete_file", "send_email", "make_payment"]
2 
3def execute_with_approval(tool: str, params: dict, user_id: str) -> str:
4    if tool in SENSITIVE_ACTIONS:
5        # Create approval request
6        request_id = create_approval_request(user_id, tool, params)
7 
8        # Wait for approval (or timeout)
9        approved = wait_for_approval(request_id, timeout=300)
10 
11        if not approved:
12            return "Action requires approval and was not approved."
13 
14    return execute_tool(tool, params)

1from slowapi import Limiter
2from slowapi.util import get_remote_address
3 
4limiter = Limiter(key_func=get_remote_address)
5 
6@app.post("/agent")
7@limiter.limit("10/minute")
8async def run_agent(request: Request):
9    # ...

1Pay per task:
2- Free: 10 tasks/month
3- Basic: $0.10/task (up to 1000)
4- Pro: $0.05/task (unlimited)
5 
6Pay per token:
7- $0.01 per 1000 tokens

1Free Tier:
2- 100 tasks/month
3- Basic tools only
4- Community support
5 
6Pro Tier ($29/month):
7- 1000 tasks/month
8- All tools
9- Priority support
10- Custom prompts
11 
12Enterprise (Custom):
13- Unlimited tasks
14- Custom tools
15- SLA guarantee
16- Dedicated support

1def calculate_price(user: User, task: Task) -> float:
2    base_price = TIER_PRICES[user.tier]
3 
4    # Add usage overage
5    if task.tokens > user.tier_limit:
6        overage = task.tokens - user.tier_limit
7        base_price += overage * OVERAGE_RATE
8 
9    # Add premium features
10    if task.uses_premium_tools:
11        base_price += PREMIUM_TOOL_FEE
12 
13    return base_price

1Build a content creation agent product with:
2 
31. Backend (FastAPI):
4   - User authentication
5   - Content generation API
6   - Template management
7   - Usage tracking
8 
92. Agent:
10   - Web research for topics
11   - Brand voice analysis
12   - SEO keyword optimization
13   - Multiple content formats
14 
153. Database:
16   - User accounts and teams
17   - Content history
18   - Templates and prompts
19   - Usage analytics
20 
214. Frontend (Next.js):
22   - Dashboard
23   - Content editor
24   - Template library
25   - Settings
26 
27Include deployment config for Railway or Vercel.