Observability + governance for AI agents — instrumenting tool usage

Enterprises that embed multi-step LLM agents—customer support bots, automated workflows, and decisioning systems—are rapidly losing visibility into what those agents do when they call external tools, creating operational, security, and compliance blind spots for SREs, security teams, and ML engineers. Roughly 200,000 mid-to-large enterprises are prime candidates for this problem as they adopt agentized features, and many already face requirements for audit trails, explainability, and incident forensics. You could build an observability and governance platform that instruments agent tool usage end-to-end: lightweight SDKs and runtime hooks to capture structured action logs and causal traces, a policy-as-code layer for runtime enforcement and redaction, enrichment pipelines that produce human-readable summaries and alerts, and exports to SIEM and existing monitoring stacks. Positioning it as an org-wide product with a realistic ACV of ~$90K points to an $18.0B TAM (200,000 enterprises × $90K ACV), but also forces focus on scalability, data residency, retention policies, and low-latency telemetry processing. Market conditions favor this move because agentization of software, the convergence of MLOps and DevOps, and rising regulatory/audit pressure are creating new, specific needs that general-purpose APM and logging tools do not yet satisfy. Competition is medium; incumbents can add features, so the core challenge is winning developer and security trust quickly by delivering a superior developer experience and enterprise-grade reliability. You can stand out by shipping an open, minimal schema for agent actions, tight integrations and SDKs for the dominant agent frameworks, and prebuilt compliance and security policy templates—advantages that drive rapid adoption among ML and SRE teams—but be honest that winning enterprise customers will require substantial work on integrations, certifications, and proven operational scale.