AI Agent Observability with Streaming SQL
AI agent observability tracks agent behavior, performance, and decision quality in real time. Streaming SQL monitors agent actions, latency, error rates, and tool usage as events flow through the system — enabling real-time debugging and optimization.
| Metric | SQL Pattern | Alert Threshold |
| Agent latency | AVG(response_time) per agent | > 5 seconds |
| Error rate | COUNT(errors) / COUNT(total) | > 5% |
| Tool call frequency | COUNT per tool per minute | Sudden spikes |
| Token usage | SUM(tokens) per hour | > budget threshold |
| Hallucination rate | COUNT(flagged) / COUNT(total) | > 2% |
Monitoring Agent Behavior
CREATE MATERIALIZED VIEW agent_metrics AS
SELECT agent_id, model_name,
COUNT(*) FILTER (WHERE ts > NOW()-INTERVAL '5 minutes') as requests_5min,
AVG(response_time_ms) FILTER (WHERE ts > NOW()-INTERVAL '5 minutes') as avg_latency_5min,
COUNT(*) FILTER (WHERE status='error') / NULLIF(COUNT(*), 0)::DECIMAL as error_rate,
SUM(total_tokens) FILTER (WHERE ts > NOW()-INTERVAL '1 hour') as tokens_1h
FROM agent_events GROUP BY agent_id, model_name;
Agent Debugging
When an agent produces a bad response, streaming SQL helps trace back: what context did it receive? Which tools did it call? What was the latency at each step? Materialized views over agent event streams answer these questions in real time.
Frequently Asked Questions
Why monitor AI agents with streaming SQL?
AI agents make autonomous decisions. Without real-time observability, you can't detect when an agent is hallucinating, using excessive tokens, or experiencing latency spikes. Streaming SQL provides continuous monitoring without separate observability infrastructure.
What metrics should I track for AI agents?
Latency (response time), error rate, token usage, tool call patterns, hallucination rate, and user satisfaction. All can be computed as streaming SQL materialized views.
