Read the full article: AI Agent Observability and Control: Building the New Monitoring Stack

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Excerpt:

Introduction

As enterprises deploy more autonomous AI agents – from conversational assistants to task-automating “bots” – a new challenge emerges: observability. These agents make multiple decisions, call APIs, update context, and even act on behalf of users. Yet traditional monitoring tools provide only a narrow view. In practice, teams often rely on scattered logs or dashboards that were not designed to capture an agent’s multi-step reasoning. A recent survey by Dynatrace found that half of AI-driven projects stall at the pilot stage because organizations “can’t govern, validate, or safely scale” their agents (www.itpro.com). Similarly, Microsoft security leads warn that we “cannot protect what we cannot see” – stressing that AI agents require an “observability control plane” as adoption grows (www.itpro.com) (www.itpro.com). In this article, we examine the monitoring gaps for autonomous and semi-autonomous agents (especially around tool usage, memory, and decision paths). We then propose a specialized observability-and-control platform that captures end-to-end traces, enforces policies, simulates workflows, and can roll back unsafe actions. We compare this approach to traditional APM (application performance monitoring) tools, explain why agent-specific telemetry is critical, and outline a pricing/integration model (e.g. per-agent-minute billing with PagerDuty/Jira integrations).

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