Salesforce

Traditional GTM processes suffer from the 'context gap' (switching between different customer calls) and the 'expert gap' (inconsistent execution among reps), leading to significant inefficiency. Back-office teams often rely on manual work in Excel or limited dashboards, which fail to meet all operational needs.

To empower non-SQL users, the team built role-specific 'skill files.' These allow business users to ask questions of data directly and quickly, eliminating bottlenecks that previously required a dedicated data engineer. This capability is used both for back-office analysis and building GTM skills.

Instead of forcing users to search dashboards, insights are proactively delivered through synthetic weekly summaries. This is achieved by transforming data dimensions (time, manager, metric) and using a multi-agent workflow: a first draft agent, a second reviewer agent for veracity checks, and a third 'tone agent' that crafts the message, ensuring risks and opportunities are weighted equally.

The internal tool, Cloudflare OS, is an agentic workspace running on Workers and Durable Objects. It allows GTM teams to perform complex tasks—such as building QBR decks, account plans, or forecast briefs—by interacting with curated expert skills without needing manual intervention.

In regulated environments (e.g., HIPAA, SOC 2), an audit trail must be a complete record of every action taken by the agent, every place it accessed data, and the authorization behind each step—not merely a developer log like those found in DataDog [5:19].

Engineers should take regulatory constraints seriously first (e.g., auditability) and design the architecture around them, rather than bolting compliance requirements onto a high-performing POC [12:07].

Effective AI agent systems require three core primitives: an immutable append-only event log (for state tracking), schema-driven object storage (for data separation and Zero Trust), and human/model agent equivalency (for seamless escalation) [8:30].

By implementing these three primitives, robust evaluation (evals) can emerge naturally—allowing for action replay, testing on production data without exposure, and comparing human vs. model performance—rather than being an afterthought [10:37].