Production Agents

From Prototype to Production

Prototype:
- Works on examples
- Barely tested
- May fail in production

Production:
- Tested thoroughly
- Handles edge cases
- Monitored continuously
- Fails gracefully

Reliability Requirements

1. Error Handling

Potential errors:
- Tool timeout (search takes too long)
- Tool failure (API down)
- Invalid input from user
- Agent hallucination

Handling:
- Retries with backoff
- Fallback tools
- Input validation
- Output verification

2. Consistency

Agent should:
- Give same answer for same input
- Not contradict itself
- Maintain memory consistency

3. Safety

Dangerous actions need approval:
- Money transfers (require confirmation)
- Data deletion (require approval)
- System access (restricted)

Use: Human-in-the-loop for sensitive decisions

Scalability

Distributed Agents

Single agent handling 1M requests/day?
Solution: Run multiple agent instances

Load balancer → [Agent 1, Agent 2, Agent 3] → Shared database

Agents share memory, scale horizontally

Caching

Expensive operations (search, compute) get cached:
- First request: Execute search → Cache result
- Same query again: Return from cache instantly

Cache invalidation: Update when data changes

Monitoring & Observability

Track metrics:
- Success rate per hour
- Average response time
- Error rate by type
- Tool usage patterns

Alerts:
- Success rate drops below 80%
- Response time exceeds 10s
- Error rate spikes

Use: Dashboards (Grafana, DataDog)

Agent Logs

Log every decision:
{
  "timestamp": "2024-05-03T10:30:00Z",
  "user_id": "user_123",
  "goal": "Book flight",
  "steps": [
    {"action": "search_flights", "result": "5 flights found"},
    {"action": "select_cheapest", "result": "Selected UA123"},
    {"action": "book", "result": "Success"}
  ],
  "duration_ms": 3240,
  "success": true
}

Benefits: Debugging, auditing, improvement!

Cost Optimization

Ways to reduce agent costs:
1. Smaller LLM for simple tasks (GPT-3.5 vs GPT-4)
2. Caching frequent queries
3. Local tools instead of API calls
4. Efficient prompting (fewer tokens)
5. Batch requests

Example: 
- Using GPT-4 for all: $10/user/day
- Smart selection: $2/user/day (5x savings!)