---
id: "action-prepare-agent-monitoring"
type: "action-item"
source_timestamps: ["00:05:58", "00:06:10"]
tags: ["tooling", "observability"]
related: ["concept-long-running-agents", "open-question-agent-monitoring"]
action: "Adopt or build observability tools to monitor long-running AI agents in real-time."
outcome: "Prevention of catastrophic errors and wasted compute during multi-day agent runs."
speakers: ["Nate B. Jones"]
sources: ["s35-compounding-gap"]
sourceVaultSlug: "s35-compounding-gap"
originDay: 35
---
# Prepare Tools for Agent Monitoring

## Action: Prepare Tools for Agent Monitoring

**Action**: Adopt or build observability tools to monitor long-running AI agents in real-time.

**Expected outcome**: Prevention of catastrophic errors and wasted compute during multi-day agent runs.

### Why this is urgent
As agents begin running for **days at a time** (see [[concept-long-running-agents]]), organizations need new observability technologies that can:

- Surface agent state and intermediate decisions
- Flag drift from the original task spec
- Allow human intervention before catastrophic errors compound

Without this, a multi-day agent that goes off the rails on day three burns the compute equivalent of millions of tokens for nothing.

### Open question
This directly addresses [[open-question-agent-monitoring]] — the unresolved problem of monitoring week-long agent runs without manually reviewing every intermediate step.

### Reference adjacent literature
Observability plugins for CrewAI, AutoGen, and LangGraph are early prototypes. Telemetry standards specifically for agentic work-in-progress are still emerging.