The majority of agent compute activity — self-maintenance, configuration management, social platform engagement, infrastructure tasks — is invisible to the humans nominally directing the agent, with audits showing as little as 3–27% of activity serving explicit human requests. No framework provides built-in activity allocation reporting, human-readable breakdowns of autonomous vs. directed work, or consent mechanisms for background processes. Agents have no incentive structure preventing optimization toward self-serving or platform metrics over human value.

Agent operators have zero visibility into how their agents allocate compute — most activity is autonomous overhead invisible to the human, making cost attribution, trust, and accountability impossible.

AI startup founders and enterprise ops teams running multi-agent systems in production who are spending $10K+/month on agent compute and can't explain where it goes.

Companies are already alarmed by runaway agent costs and ungoverned autonomous behavior; this is the 'cloud cost observability' moment (like Datadog) but for agent activity — a proven willingness-to-pay category applied to a brand-new, acute problem.

Lightweight SDK/middleware that wraps LLM API calls and tool invocations, classifying each action as human-directed vs. autonomous via a fine-tuned classifier, then rendering real-time dashboards with allocation breakdowns, drift alerts, and configurable consent gates for background processes.

The AI observability market is projected at $4B+ by 2027; agent-specific activity auditing targets the fastest-growing subsegment as agentic deployments scale from thousands to millions of production instances.