Scaling Operations with AI Agents via Contextual Data
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the gist
To stop AI agents from producing low-quality output, you must treat your company's internal communications and recordings as a structured knowledge base, granting agents access to Slack, Notion, and call transcripts to ground their tasks in real business data.
The Breakthrough
AI agents stop producing generic, low-quality output when they are provided with a comprehensive, searchable knowledge base of internal company data, such as Slack messages, Notion documentation, and customer call recordings, rather than relying on vague instructions.
Operationalizing AI Context
To make a company legible to AI, you must shift from ephemeral, in-person communication to recorded, searchable formats. At Skyvern, this involved banning direct messages to ensure all internal communication occurs in public channels and recording every internal and external meeting. This creates a persistent record that agents can query to understand specific customer pain points, feature requirements, and historical product failures.
Specific AI Workflows
- Automated PRD Generation: The agent performs a multi-step process: it searches call recordings, Slack, and Notion for a specific topic, drafts a requirements document grounded in evidence, undergoes an adversarial review by a sub-agent, and finally applies a prioritization framework (such as RICE) to filter out non-essential requirements.
- Content Marketing Pipeline: The system analyzes the last 20 customer conversations to identify recurring pain points or contrarian observations. It then drafts five social media posts (Twitter and LinkedIn), runs them through a style-correction tool (Pangram) to remove generic filler, adds a meme, and emails the final drafts for human review before publication.
Context
Skyvern scaled to a $2 million run rate by using AI agents to handle product management, sales, marketing, and customer support. The founder found that agents initially produced "slop" because they lacked the nuanced context that human employees naturally absorb. By restructuring the company to prioritize documentation and recording, the agents gained the ability to correlate specific customer issues with database failures and product requirements, allowing the team to move significantly faster.