Mark has built an AI agent that generates client OKR drafts by analyzing all available client data — SEO performance, ad accounts, website health, and historical work. The agent is designed to replace the manual, time-intensive process of writing quarterly OKRs from scratch, which the team consistently lacks bandwidth to complete.
The tool is not a one-shot generator. It's designed for iterative refinement through a human feedback loop, with account managers providing directional guidance (e.g., "more of this, less of that") that the agent integrates in a second pass.
The agent ingests all available client signals:
Based on this, it surfaces recommended focus areas for the quarter.
Feedback can be sent as a Slack message or email — it does not need to be structured or technical.
For high-volume use cases, the agent can be run as a swarm of parallel instances. In the meeting where this was discussed, 25 agents were running simultaneously to generate strategy documents for PaperTube's 200+ accounts — processing roughly one account every four minutes.
"It's a crazy amount of research that you get in a short time." — Mark Hope
This swarm approach makes it feasible to produce strategy documentation at a scale that would be completely impractical manually. At the time of the Q2 OKR meeting, only ~50 of PaperTube's accounts had existing strategy docs; the swarm was filling in the remaining 150+.
The team had not completed manual OKRs for Q2 due to time constraints. The agent addresses this directly — it handles the first draft entirely, leaving account managers to apply their client relationship knowledge as a refinement layer rather than doing the full authoring work.
This also creates a reusable, scalable process: the same agent and feedback loop can be applied each quarter without rebuilding from scratch.
The agent works from data signals, not relationship context. Account managers are expected to correct for:
The agent's output is a starting point, not a final deliverable.