Building Enterprise-Grade AI Agent Monitoring

​One agent is easy to watch. Hundreds of agent workflows across an org is a different system.

​As teams put agents into engineering, ops, support, research, and internal tools, they generate a flood of operational data: prompts, tool calls, model responses, latency, tokens, cost, errors, retries, and outcomes. Most teams still treat that as logs. It isn’t. It’s time-series + event data.

​In this session, AlphaSignal and Tiger Data will build an enterprise-grade monitoring architecture for AI agents on Postgres and time-series data. We’ll start with a simple agent monitoring system, then scale the same design to an org running autonomous agents in parallel.

​What we'll cover

​→ What to capture on every agent run
→ How to model agent activity as events and time-series
→ Tracking tool calls, latency, errors, token usage, cost, and outcomes
→ Real-time dashboards for live agent activity
→ Historical queries that surface trends and failure modes
→ What actually changes when you go from one agent to thousands
→ Why Postgres + TimescaleDB works as the data layer for agent observability

​Who should attend

​AI engineers, platform engineers, data engineers, engineering leaders, and technical founders putting agents into production.

​If your org is moving from “we built an agent” to “agents are part of the infrastructure,” this session is for you.

​Live technical walkthrough + Q&A + Webinar Recording Replay

​Presented by AlphaSignal × Tiger Data